WorldWideScience

Sample records for network detection capability

  1. Detection capability of the IMS seismic network based on ambient seismic noise measurements

    Science.gov (United States)

    Gaebler, Peter J.; Ceranna, Lars

    2016-04-01

    All nuclear explosions - on the Earth's surface, underground, underwater or in the atmosphere - are banned by the Comprehensive Nuclear-Test-Ban Treaty (CTBT). As part of this treaty, a verification regime was put into place to detect, locate and characterize nuclear explosion testings at any time, by anyone and everywhere on the Earth. The International Monitoring System (IMS) plays a key role in the verification regime of the CTBT. Out of the different monitoring techniques used in the IMS, the seismic waveform approach is the most effective technology for monitoring nuclear underground testing and to identify and characterize potential nuclear events. This study introduces a method of seismic threshold monitoring to assess an upper magnitude limit of a potential seismic event in a certain given geographical region. The method is based on ambient seismic background noise measurements at the individual IMS seismic stations as well as on global distance correction terms for body wave magnitudes, which are calculated using the seismic reflectivity method. From our investigations we conclude that a global detection threshold of around mb 4.0 can be achieved using only stations from the primary seismic network, a clear latitudinal dependence for the detection threshold can be observed between northern and southern hemisphere. Including the seismic stations being part of the auxiliary seismic IMS network results in a slight improvement of global detection capability. However, including wave arrivals from distances greater than 120 degrees, mainly PKP-wave arrivals, leads to a significant improvement in average global detection capability. In special this leads to an improvement of the detection threshold on the southern hemisphere. We further investigate the dependence of the detection capability on spatial (latitude and longitude) and temporal (time) parameters, as well as on parameters such as source type and percentage of operational IMS stations.

  2. Detection and localization capability of an urban seismic sinkhole monitoring network

    Science.gov (United States)

    Becker, Dirk; Dahm, Torsten; Schneider, Fabian

    2017-04-01

    Microseismic events linked to underground processes in sinkhole areas might serve as precursors to larger mass dislocation or rupture events which can cause felt ground shaking or even structural damage. To identify these weak and shallow events, a sensitive local seismic monitoring network is needed. In case of an urban environment the performance of local monitoring networks is severely compromised by the high anthropogenic noise level. We study the detection and localization capability of such a network, which is already partly installed in the urban area of the city of Hamburg, Germany, within the joint project SIMULTAN (http://www.gfz-potsdam.de/en/section/near-surface-geophysics/projects/simultan/). SIMULTAN aims to monitor a known sinkhole structure and gain a better understanding of the underlying processes. The current network consists of six surface stations installed in the basement of private houses and underground structures of a research facility (DESY - Deutsches Elektronen Synchrotron). During the started monitoring campaign since 2015, no microseismic events could be unambiguously attributed to the sinkholes. To estimate the detection and location capability of the network, we calculate synthetic waveforms based on the location and mechanism of former events in the area. These waveforms are combined with the recorded urban seismic noise at the station sites. As detection algorithms a simple STA/LTA trigger and a more sophisticated phase detector are used. While the STA/LTA detector delivers stable results and is able to detect events with a moment magnitude as low as 0.35 at a distance of 1.3km from the source even under the present high noise conditions the phase detector is more sensitive but also less stable. It should be stressed that due to the local near surface conditions of the wave propagation the detections are generally performed on S- or surface waves and not on P-waves, which have a significantly lower amplitude. Due to the often

  3. Detection capability of seismic network based on noise analysis and magnitude of completeness

    Czech Academy of Sciences Publication Activity Database

    Fischer, Tomáš; Bachura, M.

    2014-01-01

    Roč. 18, č. 1 (2014), s. 137-150 ISSN 1383-4649 R&D Projects: GA MŠk LM2010008 Institutional support: RVO:67985530 Keywords : seismic monitoring * magnitude of completeness * detection capability Subject RIV: DC - Siesmology, Volcanology, Earth Structure OBOR OECD: Volcanology Impact factor: 1.386, year: 2014

  4. The Global Detection Capability of the IMS Seismic Network in 2013 Inferred from Ambient Seismic Noise Measurements

    Science.gov (United States)

    Gaebler, P. J.; Ceranna, L.

    2016-12-01

    All nuclear explosions - on the Earth's surface, underground, underwater or in the atmosphere - are banned by the Comprehensive Nuclear-Test-Ban Treaty (CTBT). As part of this treaty, a verification regime was put into place to detect, locate and characterize nuclear explosion testings at any time, by anyone and everywhere on the Earth. The International Monitoring System (IMS) plays a key role in the verification regime of the CTBT. Out of the different monitoring techniques used in the IMS, the seismic waveform approach is the most effective technology for monitoring nuclear underground testing and to identify and characterize potential nuclear events. This study introduces a method of seismic threshold monitoring to assess an upper magnitude limit of a potential seismic event in a certain given geographical region. The method is based on ambient seismic background noise measurements at the individual IMS seismic stations as well as on global distance correction terms for body wave magnitudes, which are calculated using the seismic reflectivity method. From our investigations we conclude that a global detection threshold of around mb 4.0 can be achieved using only stations from the primary seismic network, a clear latitudinal dependence for the detection thresholdcan be observed between northern and southern hemisphere. Including the seismic stations being part of the auxiliary seismic IMS network results in a slight improvement of global detection capability. However, including wave arrivals from distances greater than 120 degrees, mainly PKP-wave arrivals, leads to a significant improvement in average global detection capability. In special this leads to an improvement of the detection threshold on the southern hemisphere. We further investigate the dependence of the detection capability on spatial (latitude and longitude) and temporal (time) parameters, as well as on parameters such as source type and percentage of operational IMS stations.

  5. Dynamic capabilities and network benefits

    Directory of Open Access Journals (Sweden)

    Helge Svare

    2017-01-01

    Full Text Available The number of publicly funded initiatives to establish or strengthen networks and clusters, in order to enhance innovation, has been increasing. Returns on such investments vary, and the aim of this study is to explore to what extent the variation in benefits for firms participating in networks or clusters can be explained by their dynamic capabilities (DC. Based on survey data from five Norwegian networks, the results suggest that firms with higher DC are more successful in harvesting the potential benefits of being member of a network.

  6. Detection capabilities. Some historical footnotes

    International Nuclear Information System (INIS)

    Currie, L.A.

    2017-01-01

    Part I Summary of relevant topics from 1923 to present-including: Currie (Anal Chem 40:586-593, 1968) detection concepts and capabilities; International detection and uncertainty standards; Failure of classical "1"4C dating and birth of new scientific disciplines; Exploratory nuclear data analysis of "8"5Kr monitors found coincident with the collapse of the Iron Curtain (1989); Faulty statistics proved responsible for mistaken assertions that Currie's LC yields excessive false positives; Low-level counting and AMS for atmospheric "3"7Ar and µmolar fossil/biomass carbon in the environment; Erroneous assumption that our low-level background is a Poisson Process, linked to ∼8 % spurious anticoincidence events. Part II. Exact treatment of bivariate Poisson data-solved in 1930s by Przyborowski and Wilenski, Krakow University, for detecting extreme trace amounts of a malicious contaminant (dodder) in high purity seed standards. We adapted their treatment to detection capabilities in ultra-low-level nuclear counting. The timing of their work had great historical significance, marking the start of World War II, with the invasion of Poland (1939). (author)

  7. Networking capability and new product development

    NARCIS (Netherlands)

    Mu, J.; Di Benedetto, A.C.

    2012-01-01

    Current research on network theory remains largely focused on structures and outcomes without exploring the capability that firms need to build efficient and effective networks to their advantage. In this paper, we take a networking capability view in studying inter-firm relationships. We assume

  8. BURAR: Detection and signal processing capabilities

    International Nuclear Information System (INIS)

    Ghica, Daniela; Radulian, Mircea; Popa, Mihaela

    2004-01-01

    Since July 2002, a new seismic monitoring station, the Bucovina Seismic Array (BURAR), has been installed in the northern part of Romania, in a joint effort of the Air Force Technical Applications Center, USA, and the National Institute for Earth Physics (NIEP), Romania. The array consists of 10 seismic sensors (9 short-period and one broad band) located in boreholes and distributed in a 5 x 5 km 2 area. At present, the seismic data are continuously recorded by BURAR and transmitted in real-time to the Romanian National Data Centre (ROM N DC), in Bucharest and to the National Data Center of USA, in Florida. The statistical analysis for the seismic information gathered at ROM N DC by the BURAR in the August 2002 - December 2003 time interval points out a much better efficiency of the BURAR system in detecting teleseismic events and local events occurred in the N-NE part of Romanian territory, in comparison with the actual Romanian Telemetered Network. Furthermore, the BURAR monitoring system has proven to be an important source of reliable data for NIEP efforts in issuing the seismic bulletins. Signal processing capability of the system provides useful information in order to improve the location of the local seismic events, using the array beamforming procedure. This method increases significantly the signal-to-noise ratio by summing up the coherent signals from the array components. In this way, possible source nucleation phases can be detected. At the same time, using the slowness and back azimuth estimations by f-k analysis, locations for the seismic events can be established based only on the information recorded by the BURAR array, acting like a single seismic station recording system. (authors)

  9. BURAR: Detection and signal processing capabilities

    International Nuclear Information System (INIS)

    Ghica, Daniela; Radulian, Mircea; Popa, Mihaela

    2004-01-01

    Since July 2002, a new seismic monitoring station, the Bucovina Seismic Array (BURAR), has been installed in the northern part of Romania, in a joint effort of the Air Force Technical Applications Center, USA, and the National Institute for Earth Physics (NIEP), Romania. The array consists of 10 seismic sensors (9 short-period and one broad band) located in boreholes and distributed in a 5 x 5 km area. At present, the seismic data are continuously recorded by BURAR and transmitted in real-time to the Romanian National Data Centre (ROM N DC), at Bucharest and to the National Data Center of USA, in Florida. The statistical analysis for the seismic information gathered at ROM N DC by the BURAR in the August 2002 - December 2003 time interval points out a much better efficiency of the BURAR system in detecting teleseismic events and local events occurred in the N-NE part of Romanian territory, in comparison with the actual Romanian Telemetered Network. Furthermore, the BURAR monitoring system has proven to be an important source of reliable data for NIEP efforts in elaborating of the seismic bulletins. Signal processing capability of the system provides useful information in order to improve the location of the local seismic events, using the array beamforming facility. This method increases significantly the signal-to-noise ratio of the seismic signal by summing up the coherent signals from the array components. In this way, eventual source nucleation phases can be detected. At the same time, using the slowness and backazimuth estimations by f-k analysis, locations for the seismic events can be performed based only on the information recorded by the BURAR array, acting like a single seismic station recording system. Additionally, f-k analysis techniques are useful in the local site effects estimation and interpretation of the local geological structure. (authors)

  10. Leak detection capability in CANDU reactors

    International Nuclear Information System (INIS)

    Azer, N.; Barber, D.H.; Boucher, P.J.

    1997-01-01

    This paper addresses the moisture leak detection capability of Ontario Hydro CANDU reactors which has been demonstrated by performing tests on the reactor. The tests confirmed the response of the annulus gas system (AGS) to the presence of moisture injected to simulate a pressure tube leak and also confirmed the dew point response assumed in leak before break assessments. The tests were performed on Bruce A Unit 4 by injecting known and controlled rates of heavy water vapor. To avoid condensation during test conditions, the amount of moisture which could be injected was small (2-3.5 g/hr). The test response demonstrated that the AGS is capable of detecting and annunciating small leaks. Thus confidence is provided that it would alarm for a growing pressure tube leak where the leak rate is expected to increase to kg/hr rapidly. The measured dew point response was close to that predicted by analysis

  11. Leak detection capability in CANDU reactors

    Energy Technology Data Exchange (ETDEWEB)

    Azer, N.; Barber, D.H.; Boucher, P.J. [and others

    1997-04-01

    This paper addresses the moisture leak detection capability of Ontario Hydro CANDU reactors which has been demonstrated by performing tests on the reactor. The tests confirmed the response of the annulus gas system (AGS) to the presence of moisture injected to simulate a pressure tube leak and also confirmed the dew point response assumed in leak before break assessments. The tests were performed on Bruce A Unit 4 by injecting known and controlled rates of heavy water vapor. To avoid condensation during test conditions, the amount of moisture which could be injected was small (2-3.5 g/hr). The test response demonstrated that the AGS is capable of detecting and annunciating small leaks. Thus confidence is provided that it would alarm for a growing pressure tube leak where the leak rate is expected to increase to kg/hr rapidly. The measured dew point response was close to that predicted by analysis.

  12. Wireless LAN security management with location detection capability in hospitals.

    Science.gov (United States)

    Tanaka, K; Atarashi, H; Yamaguchi, I; Watanabe, H; Yamamoto, R; Ohe, K

    2012-01-01

    In medical institutions, unauthorized access points and terminals obstruct the stable operation of a large-scale wireless local area network (LAN) system. By establishing a real-time monitoring method to detect such unauthorized wireless devices, we can improve the efficiency of security management. We detected unauthorized wireless devices by using a centralized wireless LAN system and a location detection system at 370 access points at the University of Tokyo Hospital. By storing the detected radio signal strength and location information in a database, we evaluated the risk level from the detection history. We also evaluated the location detection performance in our hospital ward using Wi-Fi tags. The presence of electric waves outside the hospital and those emitted from portable game machines with wireless communication capability was confirmed from the detection result. The location detection performance showed an error margin of approximately 4 m in detection accuracy and approximately 5% in false detection. Therefore, it was effective to consider the radio signal strength as both an index of likelihood at the detection location and an index for the level of risk. We determined the location of wireless devices with high accuracy by filtering the detection results on the basis of radio signal strength and detection history. Results of this study showed that it would be effective to use the developed location database containing radio signal strength and detection history for security management of wireless LAN systems and more general-purpose location detection applications.

  13. Community detection using preference networks

    Science.gov (United States)

    Tasgin, Mursel; Bingol, Haluk O.

    2018-04-01

    Community detection is the task of identifying clusters or groups of nodes in a network where nodes within the same group are more connected with each other than with nodes in different groups. It has practical uses in identifying similar functions or roles of nodes in many biological, social and computer networks. With the availability of very large networks in recent years, performance and scalability of community detection algorithms become crucial, i.e. if time complexity of an algorithm is high, it cannot run on large networks. In this paper, we propose a new community detection algorithm, which has a local approach and is able to run on large networks. It has a simple and effective method; given a network, algorithm constructs a preference network of nodes where each node has a single outgoing edge showing its preferred node to be in the same community with. In such a preference network, each connected component is a community. Selection of the preferred node is performed using similarity based metrics of nodes. We use two alternatives for this purpose which can be calculated in 1-neighborhood of nodes, i.e. number of common neighbors of selector node and its neighbors and, the spread capability of neighbors around the selector node which is calculated by the gossip algorithm of Lind et.al. Our algorithm is tested on both computer generated LFR networks and real-life networks with ground-truth community structure. It can identify communities accurately in a fast way. It is local, scalable and suitable for distributed execution on large networks.

  14. Change Detection in Social Networks

    National Research Council Canada - National Science Library

    McCulloh, Ian; Webb, Matthew; Graham, John; Carley, Kathleen; Horn, Daniel B

    2008-01-01

    .... This project proposes a new method for detecting change in social networks over time, by applying a cumulative sum statistical process control statistic to normally distributed network measures...

  15. Do social networks and technological capabilities help knowledge management?

    Directory of Open Access Journals (Sweden)

    Encarnación García-Sánchez

    2017-12-01

    Full Text Available Dynamic capabilities are currently becoming an important extension of the theory of resources and capabilities that enables companies to adapt better in the current competitive environment. This paper examines how knowledge management, a dynamic function related to management or administration of a set of knowledge flows, develops thanks to the greater dynamism of social networks. It then shows how this relationship is especially strengthened by different technological capabilities. To achieve these goals, the paper examines the main tools that permit companies to develop an ability to achieve competitive advantage relative to the technological capabilities of managers and workers, social networks and knowledge management.

  16. Immune networks: multitasking capabilities near saturation

    International Nuclear Information System (INIS)

    Agliari, E; Annibale, A; Barra, A; Coolen, A C C; Tantari, D

    2013-01-01

    Pattern-diluted associative networks were recently introduced as models for the immune system, with nodes representing T-lymphocytes and stored patterns representing signalling protocols between T- and B-lymphocytes. It was shown earlier that in the regime of extreme pattern dilution, a system with N T T-lymphocytes can manage a number N B =O(N T δ ) of B-lymphocytes simultaneously, with δ B = αN T , with a high degree of pattern dilution, in agreement with immunological findings. We use graph theory and statistical mechanical analysis based on replica methods to show that in the finite-connectivity regime, where each T-lymphocyte interacts with a finite number of B-lymphocytes as N T → ∞, the T-lymphocytes can coordinate effective immune responses to an extensive number of distinct antigen invasions in parallel. As α increases, the system eventually undergoes a second order transition to a phase with clonal cross-talk interference, where the system’s performance degrades gracefully. Mathematically, the model is equivalent to a spin system on a finitely connected graph with many short loops, so one would expect the available analytical methods, which all assume locally tree-like graphs, to fail. Yet it turns out to be solvable. Our results are supported by numerical simulations. (paper)

  17. Bubble Radiation Detection: Current and Future Capability

    International Nuclear Information System (INIS)

    Peurrung, A.J.; Craig, R.A.

    1999-01-01

    Despite a number of noteworthy achievements in other fields, superheated droplet detectors (SDDs) and bubble chambers (BCs) have not been used for nuclear nonproliferation and arms control. This report examines these two radiation-detection technologies in detail and answers the question of how they can be or should be ''adapted'' for use in national security applications. These technologies involve closely related approaches to radiation detection in which an energetic charged particle deposits sufficient energy to initiate the process of bubble nucleation in a superheated fluid. These detectors offer complete gamma-ray insensitivity when used to detect neutrons. They also provide controllable neutron-energy thresholds and excellent position resolution. SDDs are extraordinarily simple and inexpensive. BCs offer the promise of very high efficiency (∼75%). A notable drawback for both technologies is temperature sensitivity. As a result of this problem, the temperature must be controlled whenever high accuracy is required, or harsh environmental conditions are encountered. The primary findings of this work are listed and briefly summarized below: (1) SDDs are ready to function as electronics-free neutron detectors on demand for arms-control applications. The elimination of electronics at the weapon's location greatly eases the negotiability of radiation-detection technologies in general. (2) As a result of their high efficiency and sharp energy threshold, current BCs are almost ready for use in the development of a next-generation active assay system. Development of an instrument based on appropriately safe materials is warranted. (3) Both kinds of bubble detectors are ready for use whenever very high gamma-ray fields must be confronted. Spent fuel MPC and A is a good example where this need presents itself. (4) Both kinds of bubble detectors have the potential to function as low-cost replacements for conventional neutron detectors such as 3 He tubes. For SDDs

  18. Specifying Orchestrating Capability in Network Organization and Interfirm Innovation Networks

    DEFF Research Database (Denmark)

    Hu, Yimei; Sørensen, Olav Jull

    -tech industry. Besides interfirm networks, some organizational researchers are interested in the internal network organizational design. Prospector firms putting innovation on top of the agenda usually has a network organization which is more flexible. This paper analyzes how an SME from a traditional industry...

  19. Data Farming Process and Initial Network Analysis Capabilities

    Directory of Open Access Journals (Sweden)

    Gary Horne

    2016-01-01

    Full Text Available Data Farming, network applications and approaches to integrate network analysis and processes to the data farming paradigm are presented as approaches to address complex system questions. Data Farming is a quantified approach that examines questions in large possibility spaces using modeling and simulation. It evaluates whole landscapes of outcomes to draw insights from outcome distributions and outliers. Social network analysis and graph theory are widely used techniques for the evaluation of social systems. Incorporation of these techniques into the data farming process provides analysts examining complex systems with a powerful new suite of tools for more fully exploring and understanding the effect of interactions in complex systems. The integration of network analysis with data farming techniques provides modelers with the capability to gain insight into the effect of network attributes, whether the network is explicitly defined or emergent, on the breadth of the model outcome space and the effect of model inputs on the resultant network statistics.

  20. Error detecting capabilities of the shortened Hamming codes adopted for error detection in IEEE Standard 802.3

    Science.gov (United States)

    Fujiwara, Toru; Kasami, Tadao; Lin, Shu

    1989-09-01

    The error-detecting capabilities of the shortened Hamming codes adopted for error detection in IEEE Standard 802.3 are investigated. These codes are also used for error detection in the data link layer of the Ethernet, a local area network. The weight distributions for various code lengths are calculated to obtain the probability of undetectable error and that of detectable error for a binary symmetric channel with bit-error rate between 0.00001 and 1/2.

  1. Neural network modeling of a dolphin's sonar discrimination capabilities

    OpenAIRE

    Andersen, Lars Nonboe; René Rasmussen, A; Au, WWL; Nachtigall, PE; Roitblat, H.

    1994-01-01

    The capability of an echo-locating dolphin to discriminate differences in the wall thickness of cylinders was previously modeled by a counterpropagation neural network using only spectral information of the echoes [W. W. L. Au, J. Acoust. Soc. Am. 95, 2728–2735 (1994)]. In this study, both time and frequency information were used to model the dolphin discrimination capabilities. Echoes from the same cylinders were digitized using a broadband simulated dolphin sonar signal with the transducer ...

  2. A neural network approach to burst detection.

    Science.gov (United States)

    Mounce, S R; Day, A J; Wood, A S; Khan, A; Widdop, P D; Machell, J

    2002-01-01

    This paper describes how hydraulic and water quality data from a distribution network may be used to provide a more efficient leakage management capability for the water industry. The research presented concerns the application of artificial neural networks to the issue of detection and location of leakage in treated water distribution systems. An architecture for an Artificial Neural Network (ANN) based system is outlined. The neural network uses time series data produced by sensors to directly construct an empirical model for predication and classification of leaks. Results are presented using data from an experimental site in Yorkshire Water's Keighley distribution system.

  3. Interference suppression capabilities of smart cognitive-femto networks (SCFN)

    KAUST Repository

    Shakir, Muhammad; Atat, Rachad; Alouini, Mohamed-Slim

    2013-01-01

    Cognitive Radios are considered a standard part of future heterogeneous mobile network architectures. In this chapter, a two tier heterogeneous network with multiple Radio Access Technologies (RATs) is considered, namely (1) the secondary network, which comprises of Cognitive-Femto BS (CFBS), and (2) the macrocell network, which is considered a primary network. By exploiting the cooperation among the CFBS, the multiple CFBS can be considered a single base station with multiple geographically dispersed antennas, which can reduce the interference levels by directing the main beam toward the desired femtocell mobile user. The resultant network is referred to as Smart Cognitive-Femto Network (SCFN). In order to determine the effectiveness of the proposed smart network, the interference rejection capabilities of the SCFN is studied. It has been shown that the smart network offers significant performance improvements in interference suppression and Signal to Interference Ratio (SIR) and may be considered a promising solution to the interference management problems in future heterogeneous networks. © 2013, IGI Global.

  4. Neural network modeling of a dolphin's sonar discrimination capabilities

    DEFF Research Database (Denmark)

    Andersen, Lars Nonboe; René Rasmussen, A; Au, WWL

    1994-01-01

    The capability of an echo-locating dolphin to discriminate differences in the wall thickness of cylinders was previously modeled by a counterpropagation neural network using only spectral information of the echoes [W. W. L. Au, J. Acoust. Soc. Am. 95, 2728–2735 (1994)]. In this study, both time a...

  5. Neural network fusion capabilities for efficient implementation of tracking algorithms

    Science.gov (United States)

    Sundareshan, Malur K.; Amoozegar, Farid

    1997-03-01

    The ability to efficiently fuse information of different forms to facilitate intelligent decision making is one of the major capabilities of trained multilayer neural networks that is now being recognized. While development of innovative adaptive control algorithms for nonlinear dynamical plants that attempt to exploit these capabilities seems to be more popular, a corresponding development of nonlinear estimation algorithms using these approaches, particularly for application in target surveillance and guidance operations, has not received similar attention. We describe the capabilities and functionality of neural network algorithms for data fusion and implementation of tracking filters. To discuss details and to serve as a vehicle for quantitative performance evaluations, the illustrative case of estimating the position and velocity of surveillance targets is considered. Efficient target- tracking algorithms that can utilize data from a host of sensing modalities and are capable of reliably tracking even uncooperative targets executing fast and complex maneuvers are of interest in a number of applications. The primary motivation for employing neural networks in these applications comes from the efficiency with which more features extracted from different sensor measurements can be utilized as inputs for estimating target maneuvers. A system architecture that efficiently integrates the fusion capabilities of a trained multilayer neural net with the tracking performance of a Kalman filter is described. The innovation lies in the way the fusion of multisensor data is accomplished to facilitate improved estimation without increasing the computational complexity of the dynamical state estimator itself.

  6. Impact of self-healing capability on network robustness

    Science.gov (United States)

    Shang, Yilun

    2015-04-01

    A wide spectrum of real-life systems ranging from neurons to botnets display spontaneous recovery ability. Using the generating function formalism applied to static uncorrelated random networks with arbitrary degree distributions, the microscopic mechanism underlying the depreciation-recovery process is characterized and the effect of varying self-healing capability on network robustness is revealed. It is found that the self-healing capability of nodes has a profound impact on the phase transition in the emergence of percolating clusters, and that salient difference exists in upholding network integrity under random failures and intentional attacks. The results provide a theoretical framework for quantitatively understanding the self-healing phenomenon in varied complex systems.

  7. Enhancement of kalman filter single loss detection capability

    International Nuclear Information System (INIS)

    Morrison, G.W.; Downing, D.J.; Pike, D.H.

    1980-01-01

    A new technique to significantly increase the sensitivity of the Kalman filter to detect one-time losses for nuclear marterial accountability and control has been developed. The technique uses the innovations sequence obtained from a Kalman filter analysis of a material balance area. The innovations are distributed as zero mean independent Gaussion random variables with known variance. This property enables an estimator to be formed with enhanced one time loss detection capabilities. Simulation studies of a material balance area indicate the new estimator greatly enhances the one time loss detection capability of the Kalman filter

  8. Moving image compression and generalization capability of constructive neural networks

    Science.gov (United States)

    Ma, Liying; Khorasani, Khashayar

    2001-03-01

    To date numerous techniques have been proposed to compress digital images to ease their storage and transmission over communication channels. Recently, a number of image compression algorithms using Neural Networks NNs have been developed. Particularly, several constructive feed-forward neural networks FNNs have been proposed by researchers for image compression, and promising results have been reported. At the previous SPIE AeroSense conference 2000, we proposed to use a constructive One-Hidden-Layer Feedforward Neural Network OHL-FNN for compressing digital images. In this paper, we first investigate the generalization capability of the proposed OHL-FNN in the presence of additive noise for network training and/ or generalization. Extensive experimental results for different scenarios are presented. It is revealed that the constructive OHL-FNN is not as robust to additive noise in input image as expected. Next, the constructive OHL-FNN is applied to moving images, video sequences. The first, or other specified frame in a moving image sequence is used to train the network. The remaining moving images that follow are then generalized/compressed by this trained network. Three types of correlation-like criteria measuring the similarity of any two images are introduced. The relationship between the generalization capability of the constructed net and the similarity of images is investigated in some detail. It is shown that the constructive OHL-FNN is promising even for changing images such as those extracted from a football game.

  9. Evaluating late detection capability against diverse insider adversaries

    International Nuclear Information System (INIS)

    Sicherman, A.

    1987-01-01

    This paper describes a model for evaluating the late (after-the-fact) detection capability of material control and accountability (MCandA) systems against insider theft or diversion of special nuclear material. Potential insider cover-up strategies to defeat activities providing detection (e.g., inventories) are addressed by the model in a tractable manner. For each potential adversary and detection activity, two probabilities are assessed and used to fit the model. The model then computes the probability of detection for activities occurring periodically over time. The model provides insight into MCandA effectiveness and helps identify areas for safeguards improvement. 4 refs., 4 tabs

  10. Deep Learning for Real-Time Capable Object Detection and Localization on Mobile Platforms

    Science.gov (United States)

    Particke, F.; Kolbenschlag, R.; Hiller, M.; Patiño-Studencki, L.; Thielecke, J.

    2017-10-01

    Industry 4.0 is one of the most formative terms in current times. Subject of research are particularly smart and autonomous mobile platforms, which enormously lighten the workload and optimize production processes. In order to interact with humans, the platforms need an in-depth knowledge of the environment. Hence, it is required to detect a variety of static and non-static objects. Goal of this paper is to propose an accurate and real-time capable object detection and localization approach for the use on mobile platforms. A method is introduced to use the powerful detection capabilities of a neural network for the localization of objects. Therefore, detection information of a neural network is combined with depth information from a RGB-D camera, which is mounted on a mobile platform. As detection network, YOLO Version 2 (YOLOv2) is used on a mobile robot. In order to find the detected object in the depth image, the bounding boxes, predicted by YOLOv2, are mapped to the corresponding regions in the depth image. This provides a powerful and extremely fast approach for establishing a real-time-capable Object Locator. In the evaluation part, the localization approach turns out to be very accurate. Nevertheless, it is dependent on the detected object itself and some additional parameters, which are analysed in this paper.

  11. Network Communication as a Service-Oriented Capability

    Energy Technology Data Exchange (ETDEWEB)

    Johnston, William; Johnston, William; Metzger, Joe; Collins, Michael; Burrescia, Joseph; Dart, Eli; Gagliardi, Jim; Guok, Chin; Oberman, Kevin; O' Conner, Mike

    2008-01-08

    In widely distributed systems generally, and in science-oriented Grids in particular, software, CPU time, storage, etc., are treated as"services" -- they can be allocated and used with service guarantees that allows them to be integrated into systems that perform complex tasks. Network communication is currently not a service -- it is provided, in general, as a"best effort" capability with no guarantees and only statistical predictability. In order for Grids (and most types of systems with widely distributed components) to be successful in performing the sustained, complex tasks of large-scale science -- e.g., the multi-disciplinary simulation of next generation climate modeling and management and analysis of the petabytes of data that will come from the next generation of scientific instrument (which is very soon for the LHC at CERN) -- networks must provide communication capability that is service-oriented: That is it must be configurable, schedulable, predictable, and reliable. In order to accomplish this, the research and education network community is undertaking a strategy that involves changes in network architecture to support multiple classes of service; development and deployment of service-oriented communication services, and; monitoring and reporting in a form that is directly useful to the application-oriented system so that it may adapt to communications failures. In this paper we describe ESnet's approach to each of these -- an approach that is part of an international community effort to have intra-distributed system communication be based on a service-oriented capability.

  12. Assessing the weather monitoring capabilities of cellular microwave link networks

    Science.gov (United States)

    Fencl, Martin; Vrzba, Miroslav; Rieckermann, Jörg; Bareš, Vojtěch

    2016-04-01

    Using of microwave links for rainfall monitoring was suggested already by (Atlas and Ulbrich, 1977). However, this technique attracted broader attention of scientific community only in the recent decade, with the extensive growth of cellular microwave link (CML) networks, which form the backbone of today's cellular telecommunication infrastructure. Several studies have already shown that CMLs can be conveniently used as weather sensors and have potential to provide near-ground path-integrated observations of rainfall but also humidity or fog. However, although research is still focusing on algorithms to improve the weather sensing capabilities (Fencl et al., 2015), it is not clear how to convince cellular operators to provide the power levels of their network. One step in this direction is to show in which regions or municipalities the networks are sufficiently dense to provide/develop good services. In this contribution we suggest a standardized approach to evaluate CML networks in terms of rainfall observation and to identify suitable regions for CML rainfall monitoring. We estimate precision of single CML based on its sensitivity to rainfall, i.e. as a function of frequency, polarization and path length. Capability of a network to capture rainfall spatial patterns is estimated from the CML coverage and path lengths considering that single CML provides path-integrated rain rates. We also search for suitable predictors for regions where no network topologies are available. We test our approach on several European networks and discuss the results. Our results show that CMLs are very dense in urban areas (> 1 CML/km2), but less in rural areas (online tool. In summary, our results demonstrate that CML represent promising environmental observation network, suitable especially for urban rainfall monitoring. The developed approach integrated into an open source online tool can be conveniently used e.g. by local operators or authorities to evaluate the suitability of

  13. Social Network Change Detection

    National Research Council Canada - National Science Library

    McCulloh, Ian A; Carley, Kathleen M

    2008-01-01

    ... between group members. The ability to systematically, statistically, effectively and efficiently detect these changes has the potential to enable the anticipation of change, provide early warning of change, and enable...

  14. DNA-based species detection capabilities using laser transmission spectroscopy.

    Science.gov (United States)

    Mahon, A R; Barnes, M A; Li, F; Egan, S P; Tanner, C E; Ruggiero, S T; Feder, J L; Lodge, D M

    2013-01-06

    Early detection of invasive species is critical for effective biocontrol to mitigate potential ecological and economic damage. Laser transmission spectroscopy (LTS) is a powerful solution offering real-time, DNA-based species detection in the field. LTS can measure the size, shape and number of nanoparticles in a solution and was used here to detect size shifts resulting from hybridization of the polymerase chain reaction product to nanoparticles functionalized with species-specific oligonucleotide probes or with the species-specific oligonucleotide probes alone. We carried out a series of DNA detection experiments using the invasive freshwater quagga mussel (Dreissena bugensis) to evaluate the capability of the LTS platform for invasive species detection. Specifically, we tested LTS sensitivity to (i) DNA concentrations of a single target species, (ii) the presence of a target species within a mixed sample of other closely related species, (iii) species-specific functionalized nanoparticles versus species-specific oligonucleotide probes alone, and (iv) amplified DNA fragments versus unamplified genomic DNA. We demonstrate that LTS is a highly sensitive technique for rapid target species detection, with detection limits in the picomolar range, capable of successful identification in multispecies samples containing target and non-target species DNA. These results indicate that the LTS DNA detection platform will be useful for field application of target species. Additionally, we find that LTS detection is effective with species-specific oligonucleotide tags alone or when they are attached to polystyrene nanobeads and with both amplified and unamplified DNA, indicating that the technique may also have versatility for broader applications.

  15. Discrimination capability of avalanche counters detecting different ionizing particles

    International Nuclear Information System (INIS)

    Prete, G.; Viesti, G.; Padua Univ.

    1985-01-01

    The discrimination capability of avalanche counters to detect different ionizing particles has been studied using a 252 Cf source. Pulse height, pulse-height resolution and timing properties have been measured as a function of the reduced applied voltage for parallel-plate and parallel-grid avalanche counters. At the highest applied voltages, space charge effects shift the pulse-height signal of the avalanche counter away from being linearly proportional to the stopping power of the detected particles and cause the pulse-height resolution to deteriorate. To optimize the avalanche counter capability, without loss of time resolution, it appears better to operate the detector at voltages well below the breakdown threshold. Measurements with 32 S ions are also reported. (orig.)

  16. Evaluating late detection capability against diverse insider adversaries

    International Nuclear Information System (INIS)

    Sicherman, A.

    1987-01-01

    The threat of theft or diversion of special nuclear material (SNM) by insiders is a key concern for safeguards planners. Different types of employees having varying degrees of access to both SNM and safeguards systems pose a difficult challenge for theft detection. Safeguards planners rely on physical security, material control, and accountability to provide detection of a theft attempt. When detection occurs too late to prevent a theft, it is called a late detection or late alarm. Activities or events that many provide late detection usually belong to material control and accountability (MC ampersand A) activities. A model has been developed for evaluating the probability of late detection as a function of time elapsed since the theft. Late detection capability is beneficial if it is timely enough to improve the ability to determine the cause of an alarm, speed recovery of SNM, prevent an incorrect response to a threat demand, or promote assurance that no theft has occurred in the absence of an alarm. The model provides insight into the effectiveness of late detection safeguards components in place and helps to identify areas where the MC ampersand A can be most effectively improved

  17. Detecting Hierarchical Structure in Networks

    DEFF Research Database (Denmark)

    Herlau, Tue; Mørup, Morten; Schmidt, Mikkel Nørgaard

    2012-01-01

    Many real-world networks exhibit hierarchical organization. Previous models of hierarchies within relational data has focused on binary trees; however, for many networks it is unknown whether there is hierarchical structure, and if there is, a binary tree might not account well for it. We propose...... a generative Bayesian model that is able to infer whether hierarchies are present or not from a hypothesis space encompassing all types of hierarchical tree structures. For efficient inference we propose a collapsed Gibbs sampling procedure that jointly infers a partition and its hierarchical structure....... On synthetic and real data we demonstrate that our model can detect hierarchical structure leading to better link-prediction than competing models. Our model can be used to detect if a network exhibits hierarchical structure, thereby leading to a better comprehension and statistical account the network....

  18. Antecedents of network capability and their effects on innovation performance: an empirical test of hi-tech firms in China

    NARCIS (Netherlands)

    Fang, Gang; Ma, Xiang Yuan; Brouwers-Ren, Liqin; Zhou, Qing

    2014-01-01

    A firm’s competitive advantage can come not only from internal resources but also from inter-firm innovation networks. This paper shows that network capabilities (i.e., network visioning capability, network constructing capability, network operating capability and network centring capability) are

  19. Anomaly Detection in Dynamic Networks

    Energy Technology Data Exchange (ETDEWEB)

    Turcotte, Melissa [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2014-10-14

    Anomaly detection in dynamic communication networks has many important security applications. These networks can be extremely large and so detecting any changes in their structure can be computationally challenging; hence, computationally fast, parallelisable methods for monitoring the network are paramount. For this reason the methods presented here use independent node and edge based models to detect locally anomalous substructures within communication networks. As a first stage, the aim is to detect changes in the data streams arising from node or edge communications. Throughout the thesis simple, conjugate Bayesian models for counting processes are used to model these data streams. A second stage of analysis can then be performed on a much reduced subset of the network comprising nodes and edges which have been identified as potentially anomalous in the first stage. The first method assumes communications in a network arise from an inhomogeneous Poisson process with piecewise constant intensity. Anomaly detection is then treated as a changepoint problem on the intensities. The changepoint model is extended to incorporate seasonal behavior inherent in communication networks. This seasonal behavior is also viewed as a changepoint problem acting on a piecewise constant Poisson process. In a static time frame, inference is made on this extended model via a Gibbs sampling strategy. In a sequential time frame, where the data arrive as a stream, a novel, fast Sequential Monte Carlo (SMC) algorithm is introduced to sample from the sequence of posterior distributions of the change points over time. A second method is considered for monitoring communications in a large scale computer network. The usage patterns in these types of networks are very bursty in nature and don’t fit a Poisson process model. For tractable inference, discrete time models are considered, where the data are aggregated into discrete time periods and probability models are fitted to the

  20. Generative adversarial networks for brain lesion detection

    Science.gov (United States)

    Alex, Varghese; Safwan, K. P. Mohammed; Chennamsetty, Sai Saketh; Krishnamurthi, Ganapathy

    2017-02-01

    Manual segmentation of brain lesions from Magnetic Resonance Images (MRI) is cumbersome and introduces errors due to inter-rater variability. This paper introduces a semi-supervised technique for detection of brain lesion from MRI using Generative Adversarial Networks (GANs). GANs comprises of a Generator network and a Discriminator network which are trained simultaneously with the objective of one bettering the other. The networks were trained using non lesion patches (n=13,000) from 4 different MR sequences. The network was trained on BraTS dataset and patches were extracted from regions excluding tumor region. The Generator network generates data by modeling the underlying probability distribution of the training data, (PData). The Discriminator learns the posterior probability P (Label Data) by classifying training data and generated data as "Real" or "Fake" respectively. The Generator upon learning the joint distribution, produces images/patches such that the performance of the Discriminator on them are random, i.e. P (Label Data = GeneratedData) = 0.5. During testing, the Discriminator assigns posterior probability values close to 0.5 for patches from non lesion regions, while patches centered on lesion arise from a different distribution (PLesion) and hence are assigned lower posterior probability value by the Discriminator. On the test set (n=14), the proposed technique achieves whole tumor dice score of 0.69, sensitivity of 91% and specificity of 59%. Additionally the generator network was capable of generating non lesion patches from various MR sequences.

  1. IPSN monitoring capabilities and information networks in accidental situation

    International Nuclear Information System (INIS)

    Calmet, D.; Robeau, D.

    1992-01-01

    In 1989, a Radiological Transmission and Early Warning System (SYTAR) came into being in order to harmonize radioactivity surveillance methodologies and furthermore to trigger off an alert throughout the networks whenever an unusual degree of radioactivity is detected. SYTAR is a remote permanent system linking up a national electronic access and the radiation protection services of nuclear facilities located on the mainland. The structure of the network are particularly presented in the framework of a drill organized on october 1991 in the south-east of France. During this drill, an accident was simulated on a ghost nuclear power plant located in the Cadarache Nuclear Center. A large number of samples were taken from filtered aerosols, soils, grass, milk, vegetables, food stuff; they were contaminated with Caesium 137 and Iodine 131 before to be sent to laboratories for measurements. The results of measurements were transmitted to the actors of the drill using SYTAR network. They permit to determine the exclusion area, the radiological impacts and counter-measures. (author)

  2. Network anomaly detection a machine learning perspective

    CERN Document Server

    Bhattacharyya, Dhruba Kumar

    2013-01-01

    With the rapid rise in the ubiquity and sophistication of Internet technology and the accompanying growth in the number of network attacks, network intrusion detection has become increasingly important. Anomaly-based network intrusion detection refers to finding exceptional or nonconforming patterns in network traffic data compared to normal behavior. Finding these anomalies has extensive applications in areas such as cyber security, credit card and insurance fraud detection, and military surveillance for enemy activities. Network Anomaly Detection: A Machine Learning Perspective presents mach

  3. Enhancing the Radio Astronomy Capabilities at NASA's Deep Space Network

    Science.gov (United States)

    Lazio, Joseph; Teitelbaum, Lawrence; Franco, Manuel M.; Garcia-Miro, Cristina; Horiuchi, Shinji; Jacobs, Christopher; Kuiper, Thomas; Majid, Walid

    2015-08-01

    NASA's Deep Space Network (DSN) is well known for its role in commanding and communicating with spacecraft across the solar system that produce a steady stream of new discoveries in Astrophysics, Heliophysics, and Planetary Science. Equipped with a number of large antennas distributed across the world, the DSN also has a history of contributing to a number of leading radio astronomical projects. This paper summarizes a number of enhancements that are being implemented currently and that are aimed at increasing its capabilities to engage in a wide range of science observations. These enhancements include* A dual-beam system operating between 18 and 27 GHz (~ 1 cm) capable of conducting a variety of molecular line observations, searches for pulsars in the Galactic center, and continuum flux density (photometry) of objects such as nearby protoplanetary disks* Enhanced spectroscopy and pulsar processing backends for use at 1.4--1.9 GHz (20 cm), 18--27 GHz (1 cm), and 38--50 GHz (0.7 cm)* The DSN Transient Observatory (DTN), an automated, non-invasive backend for transient searching* Larger bandwidths (>= 0.5 GHz) for pulsar searching and timing; and* Improved data rates (2048 Mbps) and better instrumental response for very long baseline interferometric (VLBI) observations with the new DSN VLBI processor (DVP), which is providing unprecedented sensitivity for maintenance of the International Celestial Reference Frame (ICRF) and development of future versions.One of the results of these improvements is that the 70~m Deep Space Station 43 (DSS-43, Tidbinbilla antenna) is now the most sensitive radio antenna in the southern hemisphere. Proposals to use these systems are accepted from the international community.Part of this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics & Space Administration.

  4. Building capability through networking with investors and researchers

    DEFF Research Database (Denmark)

    Wang, Daojuan; Schøtt, Thomas

    A startup requires financing, typically, and the startup is based on innovation, often. Capabilities for innovation and financing may be built simultaneously and created jointly at inception. Co-creation of capabilities for financing and innovation is accounted for in this study. Co...

  5. Capabilities of unmanned aircraft vehicles for low altitude weed detection

    Science.gov (United States)

    Pflanz, Michael; Nordmeyer, Henning

    2014-05-01

    Sustainable crop production and food security require a consumer and environmental safe plant protection. It is recently known, that precise weed monitoring approaches could help apply pesticides corresponding to field variability. In this regard the site-specific weed management may contribute to an application of herbicides with higher ecologically aware and economical savings. First attempts of precision agriculture date back to the 1980's. Since that time, remote sensing from satellites or manned aircrafts have been investigated and used in agricultural practice, but are currently inadequate for the separation of weeds in an early growth stage from cultivated plants. In contrast, low-cost image capturing at low altitude from unmanned aircraft vehicles (UAV) provides higher spatial resolution and almost real-time processing. Particularly, rotary-wing aircrafts are suitable for precise path or stationary flight. This minimises motion blur and provides better image overlapping for stitching and mapping procedures. Through improved image analyses and the recent increase in the availability of microcontrollers and powerful batteries for UAVs, it can be expected that the spatial mapping of weeds will be enhanced in the future. A six rotors microcopter was equipped with a modified RGB camera taking images from agricultural fields. The hexacopter operates within predefined pathways at adjusted altitudes (from 5 to 10 m) by using GPS navigation. Different scenarios of optical weed detection have been carried out regarding to variable altitude, image resolution, weed and crop growth stages. Our experiences showed high capabilities for site-specific weed control. Image analyses with regard to recognition of weed patches can be used to adapt herbicide application to varying weed occurrence across a field.

  6. Towards a framework for a network warfare capability

    CSIR Research Space (South Africa)

    Veerasamy, N

    2008-07-01

    Full Text Available . These include the legal issues, ethical dilemmas, technical solutions, financial impact and skill/manpower investment. Logical constraints/implications have been grouped together in the discussion that follows. 5.1.1 Legal Ethical Issues As network warfare... but the underlying causes of crime also needs to be understood. Ethics and morals play a significant role in determining the personality traits of an individual. Users will need to balance ethical dilemmas before engaging in offensive network warfare. Computers...

  7. An analysis of network traffic classification for botnet detection

    DEFF Research Database (Denmark)

    Stevanovic, Matija; Pedersen, Jens Myrup

    2015-01-01

    of detecting botnet network traffic using three methods that target protocols widely considered as the main carriers of botnet Command and Control (C&C) and attack traffic, i.e. TCP, UDP and DNS. We propose three traffic classification methods based on capable Random Forests classifier. The proposed methods...

  8. Global operations networks in motion: Managing configurations and capabilities

    DEFF Research Database (Denmark)

    Slepniov, Dmitrij; Wæhrens, Brian Vejrum; Jørgensen, Claus

    2010-01-01

    In the past, the ‘Made in the World’ label, although capturing what may lie ahead, seemed awkward and futuristic. Today, it has become a reality. An ample array of global products are built up of numerous components and modules manufactured by global networks of differentiated partners rather than...... within the boundaries of one national entity. The purpose of this paper is to contribute to bridging the empirical gap in the area of global operations networks and provide insights into how they change over time. The paper is based on the cases of three Danish companies and their global operations...

  9. Dynamic Resource Allocation and QoS Control Capabilities of the Japanese Academic Backbone Network

    Directory of Open Access Journals (Sweden)

    Michihiro Aoki

    2010-08-01

    Full Text Available Dynamic resource control capabilities have become increasingly important for academic networks that must support big scientific research projects at the same time as less data intensive research and educational activities. This paper describes the dynamic resource allocation and QoS control capabilities of the Japanese academic backbone network, called SINET3, which supports a variety of academic applications with a wide range of network services. The article describes the network architecture, networking technologies, resource allocation, QoS control, and layer-1 bandwidth on-demand services. It also details typical services developed for scientific research, including the user interface, resource control, and management functions, and includes performance evaluations.

  10. Information dynamics algorithm for detecting communities in networks

    Science.gov (United States)

    Massaro, Emanuele; Bagnoli, Franco; Guazzini, Andrea; Lió, Pietro

    2012-11-01

    The problem of community detection is relevant in many scientific disciplines, from social science to statistical physics. Given the impact of community detection in many areas, such as psychology and social sciences, we have addressed the issue of modifying existing well performing algorithms by incorporating elements of the domain application fields, i.e. domain-inspired. We have focused on a psychology and social network-inspired approach which may be useful for further strengthening the link between social network studies and mathematics of community detection. Here we introduce a community-detection algorithm derived from the van Dongen's Markov Cluster algorithm (MCL) method [4] by considering networks' nodes as agents capable to take decisions. In this framework we have introduced a memory factor to mimic a typical human behavior such as the oblivion effect. The method is based on information diffusion and it includes a non-linear processing phase. We test our method on two classical community benchmark and on computer generated networks with known community structure. Our approach has three important features: the capacity of detecting overlapping communities, the capability of identifying communities from an individual point of view and the fine tuning the community detectability with respect to prior knowledge of the data. Finally we discuss how to use a Shannon entropy measure for parameter estimation in complex networks.

  11. BBN technical memorandum W1310 hydroacoustic network capability studies

    Energy Technology Data Exchange (ETDEWEB)

    Angell, J., LLNL

    1997-12-01

    This report summarizes work performed under contract to Lawrence Livermore National Laboratory during the period 1 August to 30 November 1997. Four separate tasks were undertaken during this period which investigated various aspects of hydroacoustic network performance using the Hydroacoustic Coverage Assessment Model (HydroCAM). The purpose of this report is to document each of these tasks.

  12. Immune networks: multi-tasking capabilities at medium load

    Science.gov (United States)

    Agliari, E.; Annibale, A.; Barra, A.; Coolen, A. C. C.; Tantari, D.

    2013-08-01

    Associative network models featuring multi-tasking properties have been introduced recently and studied in the low-load regime, where the number P of simultaneously retrievable patterns scales with the number N of nodes as P ˜ log N. In addition to their relevance in artificial intelligence, these models are increasingly important in immunology, where stored patterns represent strategies to fight pathogens and nodes represent lymphocyte clones. They allow us to understand the crucial ability of the immune system to respond simultaneously to multiple distinct antigen invasions. Here we develop further the statistical mechanical analysis of such systems, by studying the medium-load regime, P ˜ Nδ with δ ∈ (0, 1]. We derive three main results. First, we reveal the nontrivial architecture of these networks: they exhibit a high degree of modularity and clustering, which is linked to their retrieval abilities. Second, by solving the model we demonstrate for δ frameworks are required to achieve effective retrieval.

  13. Integrating Space Communication Network Capabilities via Web Portal Technologies

    Science.gov (United States)

    Johnston, Mark D.; Lee, Carlyn-Ann; Lau, Chi-Wung; Cheung, Kar-Ming; Levesque, Michael; Carruth, Butch; Coffman, Adam; Wallace, Mike

    2014-01-01

    We have developed a service portal prototype as part of an investigation into the feasibility of using Java portlet technology as a means of providing integrated access to NASA communications network services. Portal servers provide an attractive platform for this role due to the various built-in collaboration applications they can provide, combined with the possibility to develop custom inter-operating portlets to extent their functionality while preserving common presentation and behavior. This paper describes various options for integration of network services related to planning and scheduling, and results based on use of a popular open-source portal framework. Plans are underway to develop an operational SCaN Service Portal, building on the experiences reported here.

  14. Border detection in complex networks

    International Nuclear Information System (INIS)

    Travencolo, Bruno A N; Viana, Matheus Palhares; Costa, Luciano da Fontoura

    2009-01-01

    One important issue implied by the finite nature of real-world networks regards the identification of their more external (border) and internal nodes. The present work proposes a formal and objective definition of these properties, founded on the recently introduced concept of node diversity. It is shown that this feature does not exhibit any relevant correlation with several well-established complex networks measurements. A methodology for the identification of the borders of complex networks is described and illustrated with respect to theoretical (geographical and knitted networks) as well as real-world networks (urban and word association networks), yielding interesting results and insights in both cases.

  15. Immune networks: multi-tasking capabilities at medium load

    International Nuclear Information System (INIS)

    Agliari, E; Annibale, A; Barra, A; Coolen, A C C; Tantari, D

    2013-01-01

    Associative network models featuring multi-tasking properties have been introduced recently and studied in the low-load regime, where the number P of simultaneously retrievable patterns scales with the number N of nodes as P ∼ log N. In addition to their relevance in artificial intelligence, these models are increasingly important in immunology, where stored patterns represent strategies to fight pathogens and nodes represent lymphocyte clones. They allow us to understand the crucial ability of the immune system to respond simultaneously to multiple distinct antigen invasions. Here we develop further the statistical mechanical analysis of such systems, by studying the medium-load regime, P ∼ N δ with δ ∈ (0, 1]. We derive three main results. First, we reveal the nontrivial architecture of these networks: they exhibit a high degree of modularity and clustering, which is linked to their retrieval abilities. Second, by solving the model we demonstrate for δ < 1 the existence of large regions in the phase diagram where the network can retrieve all stored patterns simultaneously. Finally, in the high-load regime δ = 1 we find that the system behaves as a spin-glass, suggesting that finite-connectivity frameworks are required to achieve effective retrieval. (paper)

  16. Efficient Network Monitoring for Attack Detection

    OpenAIRE

    Limmer, Tobias

    2011-01-01

    Techniques for network-based intrusion detection have been evolving for years, and the focus of most research is on detection algorithms, although networks are distributed and dynamically managed nowadays. A data processing framework is required that allows to embed multiple detection techniques and to provide data with the needed aggregation levels. Within that framework, this work concentrates on methods that improve the interoperability of intrusion detection techniques and focuses on data...

  17. Prioritization Assessment for Capability Gaps in Weapon System of Systems Based on the Conditional Evidential Network

    Directory of Open Access Journals (Sweden)

    Dong Pei

    2018-02-01

    Full Text Available The prioritization of capability gaps for weapon system of systems is the basis for design and capability planning in the system of systems development process. In order to address input information uncertainties, the prioritization of capability gaps is computed in two steps using the conditional evidential network method. First, we evaluated the belief distribution of degree of required satisfaction for capabilities, and then calculated the reverse conditional belief function between capability hierarchies. We also provided verification for the feasibility and effectiveness of the proposed method through a prioritization of capability gaps calculation using an example of a spatial-navigation-and-positioning system of systems.

  18. Optical implementations of associative networks with versatile adaptive learning capabilities.

    Science.gov (United States)

    Fisher, A D; Lippincott, W L; Lee, J N

    1987-12-01

    Optical associative, parallel-processing architectures are being developed using a multimodule approach, where a number of smaller, adaptive, associative modules are nonlinearly interconnected and cascaded under the guidance of a variety of organizational principles to structure larger architectures for solving specific problems. A number of novel optical implementations with versatile adaptive learning capabilities are presented for the individual associative modules, including holographic configurations and five specific electrooptic configurations. The practical issues involved in real optical architectures are analyzed, and actual laboratory optical implementations of associative modules based on Hebbian and Widrow-Hoff learning rules are discussed, including successful experimental demonstrations of their operation.

  19. Network Intrusion Detection System using Apache Storm

    Directory of Open Access Journals (Sweden)

    Muhammad Asif Manzoor

    2017-06-01

    Full Text Available Network security implements various strategies for the identification and prevention of security breaches. Network intrusion detection is a critical component of network management for security, quality of service and other purposes. These systems allow early detection of network intrusion and malicious activities; so that the Network Security infrastructure can react to mitigate these threats. Various systems are proposed to enhance the network security. We are proposing to use anomaly based network intrusion detection system in this work. Anomaly based intrusion detection system can identify the new network threats. We also propose to use Real-time Big Data Stream Processing Framework, Apache Storm, for the implementation of network intrusion detection system. Apache Storm can help to manage the network traffic which is generated at enormous speed and size and the network traffic speed and size is constantly increasing. We have used Support Vector Machine in this work. We use Knowledge Discovery and Data Mining 1999 (KDD’99 dataset to test and evaluate our proposed solution.

  20. Social network analysis community detection and evolution

    CERN Document Server

    Missaoui, Rokia

    2015-01-01

    This book is devoted to recent progress in social network analysis with a high focus on community detection and evolution. The eleven chapters cover the identification of cohesive groups, core components and key players either in static or dynamic networks of different kinds and levels of heterogeneity. Other important topics in social network analysis such as influential detection and maximization, information propagation, user behavior analysis, as well as network modeling and visualization are also presented. Many studies are validated through real social networks such as Twitter. This edit

  1. Intelligent Surveillance Robot with Obstacle Avoidance Capabilities Using Neural Network

    Directory of Open Access Journals (Sweden)

    Widodo Budiharto

    2015-01-01

    Full Text Available For specific purpose, vision-based surveillance robot that can be run autonomously and able to acquire images from its dynamic environment is very important, for example, in rescuing disaster victims in Indonesia. In this paper, we propose architecture for intelligent surveillance robot that is able to avoid obstacles using 3 ultrasonic distance sensors based on backpropagation neural network and a camera for face recognition. 2.4 GHz transmitter for transmitting video is used by the operator/user to direct the robot to the desired area. Results show the effectiveness of our method and we evaluate the performance of the system.

  2. Detection of generalized synchronization using echo state networks

    Science.gov (United States)

    Ibáñez-Soria, D.; Garcia-Ojalvo, J.; Soria-Frisch, A.; Ruffini, G.

    2018-03-01

    Generalized synchronization between coupled dynamical systems is a phenomenon of relevance in applications that range from secure communications to physiological modelling. Here, we test the capabilities of reservoir computing and, in particular, echo state networks for the detection of generalized synchronization. A nonlinear dynamical system consisting of two coupled Rössler chaotic attractors is used to generate temporal series consisting of time-locked generalized synchronized sequences interleaved with unsynchronized ones. Correctly tuned, echo state networks are able to efficiently discriminate between unsynchronized and synchronized sequences even in the presence of relatively high levels of noise. Compared to other state-of-the-art techniques of synchronization detection, the online capabilities of the proposed Echo State Network based methodology make it a promising choice for real-time applications aiming to monitor dynamical synchronization changes in continuous signals.

  3. Electrochemical Detection in Stacked Paper Networks.

    Science.gov (United States)

    Liu, Xiyuan; Lillehoj, Peter B

    2015-08-01

    Paper-based electrochemical biosensors are a promising technology that enables rapid, quantitative measurements on an inexpensive platform. However, the control of liquids in paper networks is generally limited to a single sample delivery step. Here, we propose a simple method to automate the loading and delivery of liquid samples to sensing electrodes on paper networks by stacking multiple layers of paper. Using these stacked paper devices (SPDs), we demonstrate a unique strategy to fully immerse planar electrodes by aqueous liquids via capillary flow. Amperometric measurements of xanthine oxidase revealed that electrochemical sensors on four-layer SPDs generated detection signals up to 75% higher compared with those on single-layer paper devices. Furthermore, measurements could be performed with minimal user involvement and completed within 30 min. Due to its simplicity, enhanced automation, and capability for quantitative measurements, stacked paper electrochemical biosensors can be useful tools for point-of-care testing in resource-limited settings. © 2015 Society for Laboratory Automation and Screening.

  4. Mixture models with entropy regularization for community detection in networks

    Science.gov (United States)

    Chang, Zhenhai; Yin, Xianjun; Jia, Caiyan; Wang, Xiaoyang

    2018-04-01

    Community detection is a key exploratory tool in network analysis and has received much attention in recent years. NMM (Newman's mixture model) is one of the best models for exploring a range of network structures including community structure, bipartite and core-periphery structures, etc. However, NMM needs to know the number of communities in advance. Therefore, in this study, we have proposed an entropy regularized mixture model (called EMM), which is capable of inferring the number of communities and identifying network structure contained in a network, simultaneously. In the model, by minimizing the entropy of mixing coefficients of NMM using EM (expectation-maximization) solution, the small clusters contained little information can be discarded step by step. The empirical study on both synthetic networks and real networks has shown that the proposed model EMM is superior to the state-of-the-art methods.

  5. Network Anomaly Detection Based on Wavelet Analysis

    Directory of Open Access Journals (Sweden)

    Ali A. Ghorbani

    2008-11-01

    Full Text Available Signal processing techniques have been applied recently for analyzing and detecting network anomalies due to their potential to find novel or unknown intrusions. In this paper, we propose a new network signal modelling technique for detecting network anomalies, combining the wavelet approximation and system identification theory. In order to characterize network traffic behaviors, we present fifteen features and use them as the input signals in our system. We then evaluate our approach with the 1999 DARPA intrusion detection dataset and conduct a comprehensive analysis of the intrusions in the dataset. Evaluation results show that the approach achieves high-detection rates in terms of both attack instances and attack types. Furthermore, we conduct a full day's evaluation in a real large-scale WiFi ISP network where five attack types are successfully detected from over 30 millions flows.

  6. Network Anomaly Detection Based on Wavelet Analysis

    Science.gov (United States)

    Lu, Wei; Ghorbani, Ali A.

    2008-12-01

    Signal processing techniques have been applied recently for analyzing and detecting network anomalies due to their potential to find novel or unknown intrusions. In this paper, we propose a new network signal modelling technique for detecting network anomalies, combining the wavelet approximation and system identification theory. In order to characterize network traffic behaviors, we present fifteen features and use them as the input signals in our system. We then evaluate our approach with the 1999 DARPA intrusion detection dataset and conduct a comprehensive analysis of the intrusions in the dataset. Evaluation results show that the approach achieves high-detection rates in terms of both attack instances and attack types. Furthermore, we conduct a full day's evaluation in a real large-scale WiFi ISP network where five attack types are successfully detected from over 30 millions flows.

  7. Practical Algorithms for Subgroup Detection in Covert Networks

    DEFF Research Database (Denmark)

    Memon, Nasrullah; Wiil, Uffe Kock; Qureshi, Pir Abdul Rasool

    2010-01-01

    In this paper, we present algorithms for subgroup detection and demonstrated them with a real-time case study of USS Cole bombing terrorist network. The algorithms are demonstrated in an application by a prototype system. The system finds associations between terrorist and terrorist organisations...... and is capable of determining links between terrorism plots occurred in the past, their affiliation with terrorist camps, travel record, funds transfer, etc. The findings are represented by a network in the form of an Attributed Relational Graph (ARG). Paths from a node to any other node in the network indicate...

  8. Capability for intrusion detection at nuclear fuel sites

    International Nuclear Information System (INIS)

    1978-03-01

    A safeguards vulnerability assessment was conducted at three separate licensed nuclear processing facilities. Emphasis was placed on: (1) performance of the total intrusion detection system, and (2) vulnerability of the system to compromise by insiders. The security guards were interviewed to evaluate their effectiveness in executing their duties in accordance with the plant's security plan and to assess their knowledge regarding the operation of the security equipment. A review of the training schedule showed that the guards, along with the other plant employees, are required to periodically attend in-plant training sessions. The vulnerability assessments continued with interviews of the personnel responsible for maintaining the security equipment, with discussions of detector false alarm and maintenance problems. The second part of the vulnerability assessments was to evaluate the effectiveness of the intrusion detection systems including the interior and the perimeter sensors, CCTV surveillance devices and the exterior lighting. Two types of perimeter detectors are used at the sites, a fence disturbance sensor and an infrared barrier type detector. Infrared barrier type detectors have a higher probability of detection, especially in conjunction with dedicated CCTV cameras. The exterior lights satisfy the 0.2 footcandle illumination requirement. The interior intrusion detection systems included ultrasonic motion detectors, microwave motion detectors,balanced magnetic switches, and CCTV cameras. Entrance doors to the materials access areas and vital areas are protected with balanced magnetic switches. The interior intrusion detection systems at the three nuclear processing sites are considered satisfactory with the exception of the areas protected with ultrasonic motion detectors

  9. Apriori-based network intrusion detection system

    International Nuclear Information System (INIS)

    Wang Wenjin; Liu Junrong; Liu Baoxu

    2012-01-01

    With the development of network communication technology, more and more social activities run by Internet. In the meantime, the network information security is getting increasingly serious. Intrusion Detection System (IDS) has greatly improved the general security level of whole network. But there are still many problem exists in current IDS, e.g. high leak rate detection/false alarm rates and feature library need frequently upgrade. This paper presents an association-rule based IDS. This system can detect unknown attack by generate rules from training data. Experiment in last chapter proved the system has great accuracy on unknown attack detection. (authors)

  10. Security protection of DICOM medical images using dual-layer reversible watermarking with tamper detection capability.

    Science.gov (United States)

    Tan, Chun Kiat; Ng, Jason Changwei; Xu, Xiaotian; Poh, Chueh Loo; Guan, Yong Liang; Sheah, Kenneth

    2011-06-01

    Teleradiology applications and universal availability of patient records using web-based technology are rapidly gaining importance. Consequently, digital medical image security has become an important issue when images and their pertinent patient information are transmitted across public networks, such as the Internet. Health mandates such as the Health Insurance Portability and Accountability Act require healthcare providers to adhere to security measures in order to protect sensitive patient information. This paper presents a fully reversible, dual-layer watermarking scheme with tamper detection capability for medical images. The scheme utilizes concepts of public-key cryptography and reversible data-hiding technique. The scheme was tested using medical images in DICOM format. The results show that the scheme is able to ensure image authenticity and integrity, and to locate tampered regions in the images.

  11. Location capability of a sparse regional network (RSTN) using a multi-phase earthquake location algorithm (REGLOC)

    Energy Technology Data Exchange (ETDEWEB)

    Hutchings, L.

    1994-01-01

    The Regional Seismic Test Network (RSTN) was deployed by the US Department of Energy (DOE) to determine whether data recorded by a regional network could be used to detect and accurately locate seismic events that might be clandestine nuclear tests. The purpose of this paper is to evaluate the location capability of the RSTN. A major part of this project was the development of the location algorithm REGLOC and application of Basian a prior statistics for determining the accuracy of the location estimates. REGLOC utilizes all identifiable phases, including backazimuth, in the location. Ninty-four events, distributed throughout the network area, detected by both the RSTN and located by local networks were used in the study. The location capability of the RSTN was evaluated by estimating the location accuracy, error ellipse accuracy, and the percentage of events that could be located, as a function of magnitude. The location accuracy was verified by comparing the RSTN results for the 94 events with published locations based on data from the local networks. The error ellipse accuracy was evaluated by determining whether the error ellipse includes the actual location. The percentage of events located was assessed by combining detection capability with location capability to determine the percentage of events that could be located within the study area. Events were located with both an average crustal model for the entire region, and with regional velocity models along with station corrections obtained from master events. Most events with a magnitude <3.0 can only be located with arrivals from one station. Their average location errors are 453 and 414 km for the average- and regional-velocity model locations, respectively. Single station locations are very unreliable because they depend on accurate backazimuth estimates, and backazimuth proved to be a very unreliable computation.

  12. Capability of environmental sampling to detect undeclared cask openings

    International Nuclear Information System (INIS)

    Beckstead, L.W.; Efurd, D.W.; Hemberger, P.H.; Abhold, M.E.; Eccleston, G.W.

    1997-01-01

    The goal of this study is to determine the signatures that would allow monitors to detect diversion of nuclear fuel (by a diverter) from a storage area such as a geological repository. Due to the complexity of operations surrounding disposal of spent nuclear fuel in a geologic repository, there are several places that a diversion of fuel could take place. After the canister that contains the fuel rods is breached, the diverter would require a hot cell to process or repackage the fuel. A reference repository and possible diversion scenarios are discussed. When a canister is breached, or during reprocessing to extract nuclear weapons material (primarily Pu), several important isotopes or signatures including tritium, 85 Kr, and 129 I are released to the surrounding environment and have the potential for analysis. Estimates of release concentrations of the key signatures from the repository under a hypothetical diversion scenario are presented and discussed. Gas analysis data collected from above-ground storage casks at Idaho National Engineering and Environmental Laboratory (INEEL) Test Area North (TAN) are included and discussed in the report. In addition, LANL participated in gas sampling of one TAN cask, the Castor V/21, in July 1997. Results of xenon analysis from the cask gas sample are presented and discussed. The importance of global fallout and recent commercial reprocessing activities and their effects on repository monitoring are discussed. Monitoring and instrumental equipment for analysis of the key signature isotopes are discussed along with limits of detection. A key factor in determining if diversion activities are in progress at a repository is the timeliness of detection and analysis of the signatures. Once a clandestine operation is suspected, analytical data should be collected as quickly as possible to support any evidence of diversion

  13. Capability of environmental sampling to detect undeclared cask openings

    Energy Technology Data Exchange (ETDEWEB)

    Beckstead, L.W.; Efurd, D.W.; Hemberger, P.H.; Abhold, M.E.; Eccleston, G.W.

    1997-12-01

    The goal of this study is to determine the signatures that would allow monitors to detect diversion of nuclear fuel (by a diverter) from a storage area such as a geological repository. Due to the complexity of operations surrounding disposal of spent nuclear fuel in a geologic repository, there are several places that a diversion of fuel could take place. After the canister that contains the fuel rods is breached, the diverter would require a hot cell to process or repackage the fuel. A reference repository and possible diversion scenarios are discussed. When a canister is breached, or during reprocessing to extract nuclear weapons material (primarily Pu), several important isotopes or signatures including tritium, {sup 85}Kr, and {sup 129}I are released to the surrounding environment and have the potential for analysis. Estimates of release concentrations of the key signatures from the repository under a hypothetical diversion scenario are presented and discussed. Gas analysis data collected from above-ground storage casks at Idaho National Engineering and Environmental Laboratory (INEEL) Test Area North (TAN) are included and discussed in the report. In addition, LANL participated in gas sampling of one TAN cask, the Castor V/21, in July 1997. Results of xenon analysis from the cask gas sample are presented and discussed. The importance of global fallout and recent commercial reprocessing activities and their effects on repository monitoring are discussed. Monitoring and instrumental equipment for analysis of the key signature isotopes are discussed along with limits of detection. A key factor in determining if diversion activities are in progress at a repository is the timeliness of detection and analysis of the signatures. Once a clandestine operation is suspected, analytical data should be collected as quickly as possible to support any evidence of diversion.

  14. Detecting the influence of spreading in social networks with excitable sensor networks.

    Directory of Open Access Journals (Sweden)

    Sen Pei

    Full Text Available Detecting spreading outbreaks in social networks with sensors is of great significance in applications. Inspired by the formation mechanism of humans' physical sensations to external stimuli, we propose a new method to detect the influence of spreading by constructing excitable sensor networks. Exploiting the amplifying effect of excitable sensor networks, our method can better detect small-scale spreading processes. At the same time, it can also distinguish large-scale diffusion instances due to the self-inhibition effect of excitable elements. Through simulations of diverse spreading dynamics on typical real-world social networks (Facebook, coauthor, and email social networks, we find that the excitable sensor networks are capable of detecting and ranking spreading processes in a much wider range of influence than other commonly used sensor placement methods, such as random, targeted, acquaintance and distance strategies. In addition, we validate the efficacy of our method with diffusion data from a real-world online social system, Twitter. We find that our method can detect more spreading topics in practice. Our approach provides a new direction in spreading detection and should be useful for designing effective detection methods.

  15. Consumer preferences relative to the price and network capability of small urban vehicles

    Energy Technology Data Exchange (ETDEWEB)

    Burns, L.D.

    1979-09-01

    Preferences of consumers for small urban vehicle concepts differing only with respect to their hypothetical purchase prices and network capabilities (i.e., whether they are capable of operating on expressways, major arterials, or local streets) are analyzed using statistical techniques based on psychological scaling theories. Results from these analyses indicate that a vast majority of consumers are not readily willing to give up the accessibility provided by conventional automobiles. More specifically, over the range of hypothetical prices considered here, network capability dominates as a determinant of preferences for vehicle concepts. Also, the ability to operate vehicles on expressways is of utmost importance to consumers.

  16. Fingerprint Liveness Detection in the Presence of Capable Intruders

    Science.gov (United States)

    Sequeira, Ana F.; Cardoso, Jaime S.

    2015-01-01

    Fingerprint liveness detection methods have been developed as an attempt to overcome the vulnerability of fingerprint biometric systems to spoofing attacks. Traditional approaches have been quite optimistic about the behavior of the intruder assuming the use of a previously known material. This assumption has led to the use of supervised techniques to estimate the performance of the methods, using both live and spoof samples to train the predictive models and evaluate each type of fake samples individually. Additionally, the background was often included in the sample representation, completely distorting the decision process. Therefore, we propose that an automatic segmentation step should be performed to isolate the fingerprint from the background and truly decide on the liveness of the fingerprint and not on the characteristics of the background. Also, we argue that one cannot aim to model the fake samples completely since the material used by the intruder is unknown beforehand. We approach the design by modeling the distribution of the live samples and predicting as fake the samples very unlikely according to that model. Our experiments compare the performance of the supervised approaches with the semi-supervised ones that rely solely on the live samples. The results obtained differ from the ones obtained by the more standard approaches which reinforces our conviction that the results in the literature are misleadingly estimating the true vulnerability of the biometric system. PMID:26102491

  17. Fingerprint Liveness Detection in the Presence of Capable Intruders

    Directory of Open Access Journals (Sweden)

    Ana F. Sequeira

    2015-06-01

    Full Text Available Fingerprint liveness detection methods have been developed as an attempt to overcome the vulnerability of fingerprint biometric systems to spoofing attacks. Traditional approaches have been quite optimistic about the behavior of the intruder assuming the use of a previously known material. This assumption has led to the use of supervised techniques to estimate the performance of the methods, using both live and spoof samples to train the predictive models and evaluate each type of fake samples individually. Additionally, the background was often included in the sample representation, completely distorting the decision process. Therefore, we propose that an automatic segmentation step should be performed to isolate the fingerprint from the background and truly decide on the liveness of the fingerprint and not on the characteristics of the background. Also, we argue that one cannot aim to model the fake samples completely since the material used by the intruder is unknown beforehand. We approach the design by modeling the distribution of the live samples and predicting as fake the samples very unlikely according to that model. Our experiments compare the performance of the supervised approaches with the semi-supervised ones that rely solely on the live samples. The results obtained differ from the ones obtained by the more standard approaches which reinforces our conviction that the results in the literature are misleadingly estimating the true vulnerability of the biometric system.

  18. Detection of generalized synchronization using echo state networks

    OpenAIRE

    Ibáñez-Soria, D.; García Ojalvo, Jordi; Soria Frisch, Aureli; Ruffini, G.

    2018-01-01

    Generalized synchronization between coupled dynamical systems is a phenomenon of relevance in applications that range from secure communications to physiological modelling. Here, we test the capabilities of reservoir computing and, in particular, echo state networks for the detection of generalized synchronization. A nonlinear dynamical system consisting of two coupled Rössler chaotic attractors is used to generate temporal series consisting of time-locked generalized synchronized sequences i...

  19. Improving Air Force Active Network Defense Systems through an Analysis of Intrusion Detection Techniques

    National Research Council Canada - National Science Library

    Dunklee, David R

    2007-01-01

    .... The research then presents four recommendations to improve DCC operations. These include: Transition or improve the current signature-based IDS systems to include the capability to query and visualize network flows to detect malicious traffic...

  20. Comprehensive evaluation index system of total supply capability in distribution network

    Science.gov (United States)

    Zhang, Linyao; Wu, Guilian; Yang, Jingyuan; Jia, Shuangrui; Zhang, Wei; Sun, Weiqing

    2018-01-01

    Aiming at the lack of a comprehensive evaluation of the distribution network, based on the existing distribution network evaluation index system, combined with the basic principles of constructing the evaluation index, put forward a new evaluation index system of distribution network capacity. This paper is mainly based on the total supply capability of the distribution network, combining single index and various factors, into a multi-evaluation index of the distribution network, thus forming a reasonable index system, and various indicators of rational quantification make the evaluation results more intuitive. In order to have a comprehensive judgment of distribution network, this paper uses weights to analyse the importance of each index, verify the rationality of the index system through the example, it is proved that the rationality of the index system, so as to guide the direction of distribution network planning.

  1. Managerial capabilities of the home base in an intra-organisational global network

    DEFF Research Database (Denmark)

    Mykhaylenko, Alona

    of the HB change in the process of its global intra-organisational network evolution. In particular, the four papers constituting this thesis investigate how global intra-organisational networks evolve, how the types of network management capabilities of the HB change along with such network evolution....... This investigation was conducted through a retrospective longitudinal case study of one Danish original equipment manufacturer and its three subsidiaries in China, Slovakia, and the US. The findings, first of all, support, extend, and modify the revised Uppsala globalisation model with regard to the types...

  2. Multilayer Statistical Intrusion Detection in Wireless Networks

    Science.gov (United States)

    Hamdi, Mohamed; Meddeb-Makhlouf, Amel; Boudriga, Noureddine

    2008-12-01

    The rapid proliferation of mobile applications and services has introduced new vulnerabilities that do not exist in fixed wired networks. Traditional security mechanisms, such as access control and encryption, turn out to be inefficient in modern wireless networks. Given the shortcomings of the protection mechanisms, an important research focuses in intrusion detection systems (IDSs). This paper proposes a multilayer statistical intrusion detection framework for wireless networks. The architecture is adequate to wireless networks because the underlying detection models rely on radio parameters and traffic models. Accurate correlation between radio and traffic anomalies allows enhancing the efficiency of the IDS. A radio signal fingerprinting technique based on the maximal overlap discrete wavelet transform (MODWT) is developed. Moreover, a geometric clustering algorithm is presented. Depending on the characteristics of the fingerprinting technique, the clustering algorithm permits to control the false positive and false negative rates. Finally, simulation experiments have been carried out to validate the proposed IDS.

  3. Epileptiform spike detection via convolutional neural networks

    DEFF Research Database (Denmark)

    Johansen, Alexander Rosenberg; Jin, Jing; Maszczyk, Tomasz

    2016-01-01

    The EEG of epileptic patients often contains sharp waveforms called "spikes", occurring between seizures. Detecting such spikes is crucial for diagnosing epilepsy. In this paper, we develop a convolutional neural network (CNN) for detecting spikes in EEG of epileptic patients in an automated...

  4. Social Network Aided Plagiarism Detection

    Science.gov (United States)

    Zrnec, Aljaž; Lavbic, Dejan

    2017-01-01

    The prevalence of different kinds of electronic devices and the volume of content on the Web have increased the amount of plagiarism, which is considered an unethical act. If we want to be efficient in the detection and prevention of these acts, we have to improve today's methods of discovering plagiarism. The paper presents a research study where…

  5. Study of gamma detection capabilities of the REWARD mobile spectroscopic system

    Science.gov (United States)

    Balbuena, J. P.; Baptista, M.; Barros, S.; Dambacher, M.; Disch, C.; Fiederle, M.; Kuehn, S.; Parzefall, U.

    2017-07-01

    REWARD is a novel mobile spectroscopic radiation detector system for Homeland Security applications. The system integrates gamma and neutron detection equipped with wireless communication. A comprehensive simulation study on its gamma detection capabilities in different radioactive scenarios is presented in this work. The gamma detection unit consists of a precise energy resolution system based on two stacked (Cd,Zn)Te sensors working in coincidence sum mode. The volume of each of these CZT sensors is 1 cm3. The investigated energy windows used to determine the detection capabilities of the detector correspond to the gamma emissions from 137Cs and 60Co radioactive sources (662 keV and 1173/1333 keV respectively). Monte Carlo and Technology Computer-Aided Design (TCAD) simulations are combined to determine its sensing capabilities for different radiation sources and estimate the limits of detection of the sensing unit as a function of source activity for several shielding materials.

  6. Distributed clone detection in static wireless sensor networks: random walk with network division.

    Science.gov (United States)

    Khan, Wazir Zada; Aalsalem, Mohammed Y; Saad, N M

    2015-01-01

    Wireless Sensor Networks (WSNs) are vulnerable to clone attacks or node replication attacks as they are deployed in hostile and unattended environments where they are deprived of physical protection, lacking physical tamper-resistance of sensor nodes. As a result, an adversary can easily capture and compromise sensor nodes and after replicating them, he inserts arbitrary number of clones/replicas into the network. If these clones are not efficiently detected, an adversary can be further capable to mount a wide variety of internal attacks which can emasculate the various protocols and sensor applications. Several solutions have been proposed in the literature to address the crucial problem of clone detection, which are not satisfactory as they suffer from some serious drawbacks. In this paper we propose a novel distributed solution called Random Walk with Network Division (RWND) for the detection of node replication attack in static WSNs which is based on claimer-reporter-witness framework and combines a simple random walk with network division. RWND detects clone(s) by following a claimer-reporter-witness framework and a random walk is employed within each area for the selection of witness nodes. Splitting the network into levels and areas makes clone detection more efficient and the high security of witness nodes is ensured with moderate communication and memory overheads. Our simulation results show that RWND outperforms the existing witness node based strategies with moderate communication and memory overheads.

  7. Distributed clone detection in static wireless sensor networks: random walk with network division.

    Directory of Open Access Journals (Sweden)

    Wazir Zada Khan

    Full Text Available Wireless Sensor Networks (WSNs are vulnerable to clone attacks or node replication attacks as they are deployed in hostile and unattended environments where they are deprived of physical protection, lacking physical tamper-resistance of sensor nodes. As a result, an adversary can easily capture and compromise sensor nodes and after replicating them, he inserts arbitrary number of clones/replicas into the network. If these clones are not efficiently detected, an adversary can be further capable to mount a wide variety of internal attacks which can emasculate the various protocols and sensor applications. Several solutions have been proposed in the literature to address the crucial problem of clone detection, which are not satisfactory as they suffer from some serious drawbacks. In this paper we propose a novel distributed solution called Random Walk with Network Division (RWND for the detection of node replication attack in static WSNs which is based on claimer-reporter-witness framework and combines a simple random walk with network division. RWND detects clone(s by following a claimer-reporter-witness framework and a random walk is employed within each area for the selection of witness nodes. Splitting the network into levels and areas makes clone detection more efficient and the high security of witness nodes is ensured with moderate communication and memory overheads. Our simulation results show that RWND outperforms the existing witness node based strategies with moderate communication and memory overheads.

  8. Network-Capable Application Process and Wireless Intelligent Sensors for ISHM

    Science.gov (United States)

    Figueroa, Fernando; Morris, Jon; Turowski, Mark; Wang, Ray

    2011-01-01

    Intelligent sensor technology and systems are increasingly becoming attractive means to serve as frameworks for intelligent rocket test facilities with embedded intelligent sensor elements, distributed data acquisition elements, and onboard data acquisition elements. Networked intelligent processors enable users and systems integrators to automatically configure their measurement automation systems for analog sensors. NASA and leading sensor vendors are working together to apply the IEEE 1451 standard for adding plug-and-play capabilities for wireless analog transducers through the use of a Transducer Electronic Data Sheet (TEDS) in order to simplify sensor setup, use, and maintenance, to automatically obtain calibration data, and to eliminate manual data entry and error. A TEDS contains the critical information needed by an instrument or measurement system to identify, characterize, interface, and properly use the signal from an analog sensor. A TEDS is deployed for a sensor in one of two ways. First, the TEDS can reside in embedded, nonvolatile memory (typically flash memory) within the intelligent processor. Second, a virtual TEDS can exist as a separate file, downloadable from the Internet. This concept of virtual TEDS extends the benefits of the standardized TEDS to legacy sensors and applications where the embedded memory is not available. An HTML-based user interface provides a visual tool to interface with those distributed sensors that a TEDS is associated with, to automate the sensor management process. Implementing and deploying the IEEE 1451.1-based Network-Capable Application Process (NCAP) can achieve support for intelligent process in Integrated Systems Health Management (ISHM) for the purpose of monitoring, detection of anomalies, diagnosis of causes of anomalies, prediction of future anomalies, mitigation to maintain operability, and integrated awareness of system health by the operator. It can also support local data collection and storage. This

  9. The facilitation of groups and networks: capabilities to shape creative cooperation

    DEFF Research Database (Denmark)

    Rasmussen, Lauge Baungaard

    2003-01-01

    The facilitator, defined as a process guide of creative cooperation, is becoming more and more in focus to assist groups,teams and networks to meet these challenges. The author defines and exemplifies different levels of creative coorperation. Core capabilities of facilitation are defined...

  10. Saliency detection by conditional generative adversarial network

    Science.gov (United States)

    Cai, Xiaoxu; Yu, Hui

    2018-04-01

    Detecting salient objects in images has been a fundamental problem in computer vision. In recent years, deep learning has shown its impressive performance in dealing with many kinds of vision tasks. In this paper, we propose a new method to detect salient objects by using Conditional Generative Adversarial Network (GAN). This type of network not only learns the mapping from RGB images to salient regions, but also learns a loss function for training the mapping. To the best of our knowledge, this is the first time that Conditional GAN has been used in salient object detection. We evaluate our saliency detection method on 2 large publicly available datasets with pixel accurate annotations. The experimental results have shown the significant and consistent improvements over the state-of-the-art method on a challenging dataset, and the testing speed is much faster.

  11. Detection of Intelligent Intruders in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Yun Wang

    2016-01-01

    Full Text Available Most of the existing research works on the intrusion detection problem in a wireless sensor network (WSN assume linear or random mobility patterns in abstracting intruders’ models in traversing the WSN field. However, in real-life WSN applications, an intruder is usually an intelligent mobile robot with environment learning and detection avoidance capability (i.e., the capability to avoid surrounding sensors. Due to this, the literature results based on the linear or random mobility models may not be applied to the real-life WSN design and deployment for efficient and effective intrusion detection in practice. This motivates us to investigate the impact of intruder’s intelligence on the intrusion detection problem in a WSN for various applications. To be specific, we propose two intrusion algorithms, the pinball and flood-fill algorithms, to mimic the intelligent motion and behaviors of a mobile intruder in detecting and circumventing nearby sensors for detection avoidance while heading for its destination. The two proposed algorithms are integrated into a WSN framework for intrusion detection analysis in various circumstances. Monte Carlo simulations are conducted, and the results indicate that: (1 the performance of a WSN drastically changes as a result of the intruder’s intelligence in avoiding sensor detections and intrusion algorithms; (2 network parameters, including node density, sensing range and communication range, play a crucial part in the effectiveness of the intruder’s intrusion algorithms; and (3 it is imperative to integrate intruder’s intelligence in the WSN research for intruder detection problems under various application circumstances.

  12. On the interference suppression capabilities of cognitive enabled femto cellular networks

    KAUST Repository

    Shakir, Muhammad

    2012-06-01

    Cognitive Radios are considered as a standard part of future Heterogeneous mobile network architecture. In this paper, we consider a two tier Heterogeneous network with multiple radio access technologies (RATS) namely; (i) the secondary network which comprises of cognitive enabled femto base stations which are referred to as cognitive-femto BS (CFBS) such that each of the BS are equipped with a single antenna and (ii) the macrocell network which is considered as a primary network. The effectiveness of the cognitive transmission is based on the efficient spectrum sensing algorithms which determine the availability of the spectrum holes. However, it is equally important for the cognitive network to minimize the cross-tier interference particularly during (i) the spectrum sensing and (ii) the cognitive transmission if spectrum is available. By exploiting the cooperation among the CFBS, the multiple CFBS can be considered as a single base station with multiple geographically dispersed antennas. In this context, we proposed a smart network where CFBS collaborates to reduce the cross-tier interference level by directing the main beam toward the desired femtocell mobile user and creating toward the cross-tier interference. The resultant network is referred to as Smart cognitive-femto network (SCFN) which requires the CFBS to be self-aware such that the CFBS are aware of their surroundings and adapt accordingly to maintain a reliable and efficient communication link. In order to determine the effectiveness of the proposed smart network, we study the interference rejection (or suppression) capabilities of the SCFN. It has been shown that the proposed smart network offers significant performance improvements in interference suppression and signal to interference ratio (SIR) and may be considered as a promising solution to the interference management problems in Heterogeneous network. © 2012 IEEE.

  13. Wireless sensor network for sodium leak detection

    International Nuclear Information System (INIS)

    Satya Murty, S.A.V.; Raj, Baldev; Sivalingam, Krishna M.; Ebenezer, Jemimah; Chandran, T.; Shanmugavel, M.; Rajan, K.K.

    2012-01-01

    Highlights: ► Early detection of sodium leak is mandatory in any reactor handling liquid sodium. ► Wireless sensor networking technology has been introduced for detecting sodium leak. ► We designed and developed a wireless sensor node in-house. ► We deployed a pilot wireless sensor network for handling nine sodium leak signals. - Abstract: To study the mechanical properties of Prototype Fast Breeder Reactor component materials under the influence of sodium, the IN Sodium Test (INSOT) facility has been erected and commissioned at Indira Gandhi Centre for Atomic Research. Sodium reacts violently with air/moisture leading to fire. Hence early detection of sodium leak if any is mandatory for such plants and almost 140 sodium leak detectors are placed throughout the loop. All these detectors are wired to the control room for data collection and monitoring. To reduce the cost, space and maintenance that are involved in cabling, the wireless sensor networking technology has been introduced in the sodium leak detection system of INSOT. This paper describes about the deployment details of the pilot wireless sensor network and the measures taken for the successful deployment.

  14. Anomaly-based Network Intrusion Detection Methods

    Directory of Open Access Journals (Sweden)

    Pavel Nevlud

    2013-01-01

    Full Text Available The article deals with detection of network anomalies. Network anomalies include everything that is quite different from the normal operation. For detection of anomalies were used machine learning systems. Machine learning can be considered as a support or a limited type of artificial intelligence. A machine learning system usually starts with some knowledge and a corresponding knowledge organization so that it can interpret, analyse, and test the knowledge acquired. There are several machine learning techniques available. We tested Decision tree learning and Bayesian networks. The open source data-mining framework WEKA was the tool we used for testing the classify, cluster, association algorithms and for visualization of our results. The WEKA is a collection of machine learning algorithms for data mining tasks.

  15. Neural Network Based Intrusion Detection System for Critical Infrastructures

    Energy Technology Data Exchange (ETDEWEB)

    Todd Vollmer; Ondrej Linda; Milos Manic

    2009-07-01

    Resiliency and security in control systems such as SCADA and Nuclear plant’s in today’s world of hackers and malware are a relevant concern. Computer systems used within critical infrastructures to control physical functions are not immune to the threat of cyber attacks and may be potentially vulnerable. Tailoring an intrusion detection system to the specifics of critical infrastructures can significantly improve the security of such systems. The IDS-NNM – Intrusion Detection System using Neural Network based Modeling, is presented in this paper. The main contributions of this work are: 1) the use and analyses of real network data (data recorded from an existing critical infrastructure); 2) the development of a specific window based feature extraction technique; 3) the construction of training dataset using randomly generated intrusion vectors; 4) the use of a combination of two neural network learning algorithms – the Error-Back Propagation and Levenberg-Marquardt, for normal behavior modeling. The presented algorithm was evaluated on previously unseen network data. The IDS-NNM algorithm proved to be capable of capturing all intrusion attempts presented in the network communication while not generating any false alerts.

  16. Detecting TLEs using a massive all-sky camera network

    Science.gov (United States)

    Garnung, M. B.; Celestin, S. J.

    2017-12-01

    Transient Luminous Events (TLEs) are large-scale optical events occurring in the upper-atmosphere from the top of thunderclouds up to the ionosphere. TLEs may have important effects in local, regional, and global scales, and many features of TLEs are not fully understood yet [e.g, Pasko, JGR, 115, A00E35, 2010]. Moreover, meteor events have been suggested to play a role in sprite initiation by producing ionospheric irregularities [e.g, Qin et al., Nat. Commun., 5, 3740, 2014]. The French Fireball Recovery and InterPlanetary Observation Network (FRIPON, https://www.fripon.org/?lang=en), is a national all-sky 30 fps camera network designed to continuously detect meteor events. We seek to make use of this network to observe TLEs over unprecedented space and time scales ( 1000×1000 km with continuous acquisition). To do so, we had to significantly modify FRIPON's triggering software Freeture (https://github.com/fripon/freeture) while leaving the meteor detection capability uncompromised. FRIPON has a great potential in the study of TLEs. Not only could it produce new results about spatial and time distributions of TLEs over a very large area, it could also be used to validate and complement observations from future space missions such as ASIM (ESA) and TARANIS (CNES). In this work, we present an original image processing algorithm that can detect sprites using all-sky cameras while strongly limiting the frequency of false positives and our ongoing work on sprite triangulation using the FRIPON network.

  17. Impact of distance on the network management capability of the home base firm

    DEFF Research Database (Denmark)

    Mykhaylenko, Alona; Wæhrens, Brian Vejrum; Slepniov, Dmitrij

    For many globally dispersed organizations the home base (HB) is historically the locus of integrative, coordinating and innovating efforts, important for the overall performance. The growing concerns about the offshoring strategies posing threats to the capabilities of the HB draw attention to how...... a HB can continuously sustain its centrality. The well-known challenges of distance in the distributed working arrangements may be regarded as a major threat to the network management capabilities (NMCs) of the HB. Therefore, this paper investigates what role does distance between the HB and its...

  18. Neural network approach to radiologic lesion detection

    International Nuclear Information System (INIS)

    Newman, F.D.; Raff, U.; Stroud, D.

    1989-01-01

    An area of artificial intelligence that has gained recent attention is the neural network approach to pattern recognition. The authors explore the use of neural networks in radiologic lesion detection with what is known in the literature as the novelty filter. This filter uses a linear model; images of normal patterns become training vectors and are stored as columns of a matrix. An image of an abnormal pattern is introduced and the abnormality or novelty is extracted. A VAX 750 was used to encode the novelty filter, and two experiments have been examined

  19. Networked gamma radiation detection system for tactical deployment

    Science.gov (United States)

    Mukhopadhyay, Sanjoy; Maurer, Richard; Wolff, Ronald; Smith, Ethan; Guss, Paul; Mitchell, Stephen

    2015-08-01

    A networked gamma radiation detection system with directional sensitivity and energy spectral data acquisition capability is being developed by the National Security Technologies, LLC, Remote Sensing Laboratory to support the close and intense tactical engagement of law enforcement who carry out counterterrorism missions. In the proposed design, three clusters of 2″ × 4″ × 16″ sodium iodide crystals (4 each) with digiBASE-E (for list mode data collection) would be placed on the passenger side of a minivan. To enhance localization and facilitate rapid identification of isotopes, advanced smart real-time localization and radioisotope identification algorithms like WAVRAD (wavelet-assisted variance reduction for anomaly detection) and NSCRAD (nuisance-rejection spectral comparison ratio anomaly detection) will be incorporated. We will test a collection of algorithms and analysis that centers on the problem of radiation detection with a distributed sensor network. We will study the basic characteristics of a radiation sensor network and focus on the trade-offs between false positive alarm rates, true positive alarm rates, and time to detect multiple radiation sources in a large area. Empirical and simulation analyses of critical system parameters, such as number of sensors, sensor placement, and sensor response functions, will be examined. This networked system will provide an integrated radiation detection architecture and framework with (i) a large nationally recognized search database equivalent that would help generate a common operational picture in a major radiological crisis; (ii) a robust reach back connectivity for search data to be evaluated by home teams; and, finally, (iii) a possibility of integrating search data from multi-agency responders.

  20. Structures and Infrastructures of International R&D Networks: A Capability Maturity Perspective

    DEFF Research Database (Denmark)

    Niang, Mohamed; Wæhrens, Brian Vejrum

    Purpose: This paper explores the process towards globally distributing R&D activities with an emphasis on organizational maturity. It discusses emerging configurations by asking how the structure and infrastructure of international R&D networks evolve along with the move from a strong R&D center...... to dispersed development. Design/Methodology/Approach: This is a qualitative study of the process of distributing R&D. By comparing selected firms, the researchers identify a pattern of dispersion of R&D activities in three Danish firms. Findings and Discussion: Drawing from the case studies, the researchers...... present a capability maturity model. Furthermore, understanding the interaction between new structures and infrastructures of the dispersed networks is viewed as a key requirement for developing organizational capabilities and formulating adequate strategies that leverage dispersed R&D. Organizational...

  1. Capability-based Access Control Delegation Model on the Federated IoT Network

    DEFF Research Database (Denmark)

    Anggorojati, Bayu; Mahalle, Parikshit N.; Prasad, Neeli R.

    2012-01-01

    Flexibility is an important property for general access control system and especially in the Internet of Things (IoT), which can be achieved by access or authority delegation. Delegation mechanisms in access control that have been studied until now have been intended mainly for a system that has...... no resource constraint, such as a web-based system, which is not very suitable for a highly pervasive system such as IoT. To this end, this paper presents an access delegation method with security considerations based on Capability-based Context Aware Access Control (CCAAC) model intended for federated...... machine-to-machine communication or IoT networks. The main idea of our proposed model is that the access delegation is realized by means of a capability propagation mechanism, and incorporating the context information as well as secure capability propagation under federated IoT environments. By using...

  2. Profile-based adaptive anomaly detection for network security.

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Pengchu C. (Sandia National Laboratories, Albuquerque, NM); Durgin, Nancy Ann

    2005-11-01

    As information systems become increasingly complex and pervasive, they become inextricably intertwined with the critical infrastructure of national, public, and private organizations. The problem of recognizing and evaluating threats against these complex, heterogeneous networks of cyber and physical components is a difficult one, yet a solution is vital to ensuring security. In this paper we investigate profile-based anomaly detection techniques that can be used to address this problem. We focus primarily on the area of network anomaly detection, but the approach could be extended to other problem domains. We investigate using several data analysis techniques to create profiles of network hosts and perform anomaly detection using those profiles. The ''profiles'' reduce multi-dimensional vectors representing ''normal behavior'' into fewer dimensions, thus allowing pattern and cluster discovery. New events are compared against the profiles, producing a quantitative measure of how ''anomalous'' the event is. Most network intrusion detection systems (IDSs) detect malicious behavior by searching for known patterns in the network traffic. This approach suffers from several weaknesses, including a lack of generalizability, an inability to detect stealthy or novel attacks, and lack of flexibility regarding alarm thresholds. Our research focuses on enhancing current IDS capabilities by addressing some of these shortcomings. We identify and evaluate promising techniques for data mining and machine-learning. The algorithms are ''trained'' by providing them with a series of data-points from ''normal'' network traffic. A successful algorithm can be trained automatically and efficiently, will have a low error rate (low false alarm and miss rates), and will be able to identify anomalies in ''pseudo real-time'' (i.e., while the intrusion is still in progress

  3. Multihop Capability Analysis in Wireless Information and Power Transfer Multirelay Cooperative Networks

    Directory of Open Access Journals (Sweden)

    Qilin Wu

    2018-01-01

    Full Text Available We study simultaneous wireless information and power transfer (SWIPT in multihop wireless cooperative networks, where the multihop capability that denotes the largest number of transmission hops is investigated. By utilizing the broadcast nature of multihop wireless networks, we first propose a cooperative forwarding power (CFP scheme. In CFP scheme, the multiple relays and receiver have distinctly different tasks. Specifically, multiple relays close to the transmitter harvest power from the transmitter first and then cooperatively forward the power (not the information towards the receiver. The receiver receives the information (not the power from the transmitter first, and then it harvests the power from the relays and is taken as the transmitter of the next hop. Furthermore, for performance comparison, we suggest two schemes: cooperative forwarding information and power (CFIP and direct receiving information and power (DFIP. Also, we construct an analysis model to investigate the multihop capabilities of CFP, CFIP, and DFIP schemes under the given targeted throughput requirement. Finally, simulation results validate the analysis model and show that the multihop capability of CFP is better than CFIP and DFIP, and for improving the multihop capabilities, it is best effective to increase the average number of relay nodes in cooperative set.

  4. Artificial intelligence based event detection in wireless sensor networks

    NARCIS (Netherlands)

    Bahrepour, M.

    2013-01-01

    Wireless sensor networks (WSNs) are composed of large number of small, inexpensive devices, called sensor nodes, which are equipped with sensing, processing, and communication capabilities. While traditional applications of wireless sensor networks focused on periodic monitoring, the focus of more

  5. Deep Neural Network Detects Quantum Phase Transition

    Science.gov (United States)

    Arai, Shunta; Ohzeki, Masayuki; Tanaka, Kazuyuki

    2018-03-01

    We detect the quantum phase transition of a quantum many-body system by mapping the observed results of the quantum state onto a neural network. In the present study, we utilized the simplest case of a quantum many-body system, namely a one-dimensional chain of Ising spins with the transverse Ising model. We prepared several spin configurations, which were obtained using repeated observations of the model for a particular strength of the transverse field, as input data for the neural network. Although the proposed method can be employed using experimental observations of quantum many-body systems, we tested our technique with spin configurations generated by a quantum Monte Carlo simulation without initial relaxation. The neural network successfully identified the strength of transverse field only from the spin configurations, leading to consistent estimations of the critical point of our model Γc = J.

  6. Community detection in networks with unequal groups.

    Science.gov (United States)

    Zhang, Pan; Moore, Cristopher; Newman, M E J

    2016-01-01

    Recently, a phase transition has been discovered in the network community detection problem below which no algorithm can tell which nodes belong to which communities with success any better than a random guess. This result has, however, so far been limited to the case where the communities have the same size or the same average degree. Here we consider the case where the sizes or average degrees differ. This asymmetry allows us to assign nodes to communities with better-than-random success by examining their local neighborhoods. Using the cavity method, we show that this removes the detectability transition completely for networks with four groups or fewer, while for more than four groups the transition persists up to a critical amount of asymmetry but not beyond. The critical point in the latter case coincides with the point at which local information percolates, causing a global transition from a less-accurate solution to a more-accurate one.

  7. Multiscale Convolutional Neural Networks for Hand Detection

    Directory of Open Access Journals (Sweden)

    Shiyang Yan

    2017-01-01

    Full Text Available Unconstrained hand detection in still images plays an important role in many hand-related vision problems, for example, hand tracking, gesture analysis, human action recognition and human-machine interaction, and sign language recognition. Although hand detection has been extensively studied for decades, it is still a challenging task with many problems to be tackled. The contributing factors for this complexity include heavy occlusion, low resolution, varying illumination conditions, different hand gestures, and the complex interactions between hands and objects or other hands. In this paper, we propose a multiscale deep learning model for unconstrained hand detection in still images. Deep learning models, and deep convolutional neural networks (CNNs in particular, have achieved state-of-the-art performances in many vision benchmarks. Developed from the region-based CNN (R-CNN model, we propose a hand detection scheme based on candidate regions generated by a generic region proposal algorithm, followed by multiscale information fusion from the popular VGG16 model. Two benchmark datasets were applied to validate the proposed method, namely, the Oxford Hand Detection Dataset and the VIVA Hand Detection Challenge. We achieved state-of-the-art results on the Oxford Hand Detection Dataset and had satisfactory performance in the VIVA Hand Detection Challenge.

  8. A study on the multiple dynamic wavelength distribution for gigabit capable passive optical networks

    Directory of Open Access Journals (Sweden)

    Gustavo Adolfo Puerto Leguizamón

    2014-04-01

    Full Text Available This paper presents a data traffic based study aiming at evaluating the impact of dynamic wavelength allocation on a Gigabit capable Passive Optical Network (GPON. In Passive Optical Networks (PON, an Optical Line Terminal (OLT feeds different PONs in such a way that a given wavelength channel is evenly distributed between the Optical Network Units (ONU at each PON. However, PONs do not specify any kind of dynamic behavior on the way the wavelengths are allocated in the network, a completely static distribution is implemented instead. In thispaper we evaluate the network performance in terms of packet losses and throughput for a number of ONUs being out-of-profile while featuring a given percentage of traffic in excess for a fixed wavelength distribution and for multiple dynamic wavelength allocation. Results show that for a multichannel operation with four wavelengths, the network throughput increases up to a rough value of 19% while the packet losses drop from 22 % to 1.8 % as compared with a static wavelength distribution.

  9. Traffic routing for multicomputer networks with virtual cut-through capability

    Science.gov (United States)

    Kandlur, Dilip D.; Shin, Kang G.

    1992-01-01

    Consideration is given to the problem of selecting routes for interprocess communication in a network with virtual cut-through capability, while balancing the network load and minimizing the number of times that a message gets buffered. An approach is proposed that formulates the route selection problem as a minimization problem with a link cost function that depends upon the traffic through the link. The form of this cost function is derived using the probability of establishing a virtual cut-through route. The route selection problem is shown to be NP-hard, and an algorithm is developed to incrementally reduce the cost by rerouting the traffic. The performance of this algorithm is exemplified by two network topologies: the hypercube and the C-wrapped hexagonal mesh.

  10. Sensor Fusion-based Event Detection in Wireless Sensor Networks

    NARCIS (Netherlands)

    Bahrepour, M.; Meratnia, Nirvana; Havinga, Paul J.M.

    2009-01-01

    Recently, Wireless Sensor Networks (WSN) community has witnessed an application focus shift. Although, monitoring was the initial application of wireless sensor networks, in-network data processing and (near) real-time actuation capability have made wireless sensor networks suitable candidate for

  11. Network Community Detection on Metric Space

    Directory of Open Access Journals (Sweden)

    Suman Saha

    2015-08-01

    Full Text Available Community detection in a complex network is an important problem of much interest in recent years. In general, a community detection algorithm chooses an objective function and captures the communities of the network by optimizing the objective function, and then, one uses various heuristics to solve the optimization problem to extract the interesting communities for the user. In this article, we demonstrate the procedure to transform a graph into points of a metric space and develop the methods of community detection with the help of a metric defined for a pair of points. We have also studied and analyzed the community structure of the network therein. The results obtained with our approach are very competitive with most of the well-known algorithms in the literature, and this is justified over the large collection of datasets. On the other hand, it can be observed that time taken by our algorithm is quite less compared to other methods and justifies the theoretical findings.

  12. Geographic wormhole detection in wireless sensor networks.

    Directory of Open Access Journals (Sweden)

    Mehdi Sookhak

    Full Text Available Wireless sensor networks (WSNs are ubiquitous and pervasive, and therefore; highly susceptible to a number of security attacks. Denial of Service (DoS attack is considered the most dominant and a major threat to WSNs. Moreover, the wormhole attack represents one of the potential forms of the Denial of Service (DoS attack. Besides, crafting the wormhole attack is comparatively simple; though, its detection is nontrivial. On the contrary, the extant wormhole defense methods need both specialized hardware and strong assumptions to defend against static and dynamic wormhole attack. The ensuing paper introduces a novel scheme to detect wormhole attacks in a geographic routing protocol (DWGRP. The main contribution of this paper is to detect malicious nodes and select the best and the most reliable neighbors based on pairwise key pre-distribution technique and the beacon packet. Moreover, this novel technique is not subject to any specific assumption, requirement, or specialized hardware, such as a precise synchronized clock. The proposed detection method is validated by comparisons with several related techniques in the literature, such as Received Signal Strength (RSS, Authentication of Nodes Scheme (ANS, Wormhole Detection uses Hound Packet (WHOP, and Wormhole Detection with Neighborhood Information (WDI using the NS-2 simulator. The analysis of the simulations shows promising results with low False Detection Rate (FDR in the geographic routing protocols.

  13. Body-Sensor-Network-Based Spasticity Detection.

    Science.gov (United States)

    Misgeld, Berno J E; Luken, Markus; Heitzmann, Daniel; Wolf, Sebastian I; Leonhardt, Steffen

    2016-05-01

    Spasticity is a common disorder of the skeletal muscle with a high incidence in industrialised countries. A quantitative measure of spasticity using body-worn sensors is important in order to assess rehabilitative motor training and to adjust the rehabilitative therapy accordingly. We present a new approach to spasticity detection using the Integrated Posture and Activity Network by Medit Aachen body sensor network (BSN). For this, a new electromyography (EMG) sensor node was developed and employed in human locomotion. Following an analysis of the clinical gait data of patients with unilateral cerebral palsy, a novel algorithm was developed based on the idea to detect coactivation of antagonistic muscle groups as observed in the exaggerated stretch reflex with associated joint rigidity. The algorithm applies a cross-correlation function to the EMG signals of two antagonistically working muscles and subsequent weighting using a Blackman window. The result is a coactivation index which is also weighted by the signal equivalent energy to exclude positive detection of inactive muscles. Our experimental study indicates good performance in the detection of coactive muscles associated with spasticity from clinical data as well as measurements from a BSN in qualitative comparison with the Modified Ashworth Scale as classified by clinical experts. Possible applications of the new algorithm include (but are not limited to) use in robotic sensorimotor therapy to reduce the effect of spasticity.

  14. Exploiting the In-Network Capabilities of Multicast to Discover Proximate IPTV Channels

    Directory of Open Access Journals (Sweden)

    William Donnelly

    2010-09-01

    Full Text Available IPTV has become the next generation of television due, in part, to its ability to support features that have been lacking in conventional broadcasting—for example, end-user interactivity, personalisation and localisation. Providers are also searching for the most efficient delivery methods to provide the greatest amount of contents at the lowest cost. At present IPTV uses IP multicast to deliver live TV channels in an over-provisioned walled-garden network due to issues of deploying multicast and QoS challenges in the public Internet. However, IPTV is likely to shift into some parts of the public Internet in the future as a managed service. Multicast routing is performed on a per-session destination-address basis so each router maintains a table of all of the multicast addresses to which the content is being forwarded. We exploit this information to discover and join the in-progress channels of geographically proximate users and to create a new incentivised premium service in future IPTV networks called ProxyTV. This approach is expected to minimise network bandwidth requirements as it enables ISPs to optimise bandwidth on their edge networks. This becomes increasingly significant as TV content consumes more and more bandwidth, especially with the onset of HD and 3D capabilities. In this paper, we have presented in detail the concept with the results of a survey and an analysis of network traffic to justify the proposed approach.

  15. Directed Design of Experiments for Validating Probability of Detection Capability of NDE Systems (DOEPOD)

    Science.gov (United States)

    Generazio, Edward R.

    2015-01-01

    Directed Design of Experiments for Validating Probability of Detection Capability of NDE Systems (DOEPOD) Manual v.1.2 The capability of an inspection system is established by applications of various methodologies to determine the probability of detection (POD). One accepted metric of an adequate inspection system is that there is 95% confidence that the POD is greater than 90% (90/95 POD). Design of experiments for validating probability of detection capability of nondestructive evaluation (NDE) systems (DOEPOD) is a methodology that is implemented via software to serve as a diagnostic tool providing detailed analysis of POD test data, guidance on establishing data distribution requirements, and resolving test issues. DOEPOD demands utilization of observance of occurrences. The DOEPOD capability has been developed to provide an efficient and accurate methodology that yields observed POD and confidence bounds for both Hit-Miss or signal amplitude testing. DOEPOD does not assume prescribed POD logarithmic or similar functions with assumed adequacy over a wide range of flaw sizes and inspection system technologies, so that multi-parameter curve fitting or model optimization approaches to generate a POD curve are not required. DOEPOD applications for supporting inspector qualifications is included.

  16. Intrusion detection in wireless ad-hoc networks

    CERN Document Server

    Chaki, Nabendu

    2014-01-01

    Presenting cutting-edge research, Intrusion Detection in Wireless Ad-Hoc Networks explores the security aspects of the basic categories of wireless ad-hoc networks and related application areas. Focusing on intrusion detection systems (IDSs), it explains how to establish security solutions for the range of wireless networks, including mobile ad-hoc networks, hybrid wireless networks, and sensor networks.This edited volume reviews and analyzes state-of-the-art IDSs for various wireless ad-hoc networks. It includes case studies on honesty-based intrusion detection systems, cluster oriented-based

  17. Comparison of High Performance Network Options: EDR InfiniBand vs.100Gb RDMA Capable Ethernet

    Energy Technology Data Exchange (ETDEWEB)

    Kachelmeier, Luke Anthony [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Univ. of New Mexico, Albuquerque, NM (United States); Van Wig, Faith Virginia [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Missouri Univ. of Science and Technology, Rolla, MO (United States); Erickson, Kari Natania [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); New Mexico Inst. of Mining and Technology, Socorro, NM (United States)

    2016-08-08

    These are the slides for a presentation at the HPC Mini Showcase. This is a comparison of two different high performance network options: EDR InfiniBand and 100Gb RDMA capable ethernet. The conclusion of this comparison is the following: there is good potential, as shown with the direct results; 100Gb technology is too new and not standardized, thus deployment effort is complex for both options; different companies are not necessarily compatible; if you want 100Gb/s, you must get it all from one place.

  18. Effect of signal noise on the learning capability of an artificial neural network

    International Nuclear Information System (INIS)

    Vega, J.J.; Reynoso, R.; Calvet, H. Carrillo

    2009-01-01

    Digital Pulse Shape Analysis (DPSA) by artificial neural networks (ANN) is becoming an important tool to extract relevant information from digitized signals in different areas. In this paper, we present a systematic evidence of how the concomitant noise that distorts the signals or patterns to be identified by an ANN set limits to its learning capability. Also, we present evidence that explains overtraining as a competition between the relevant pattern features, on the one side, against the signal noise, on the other side, as the main cause defining the shape of the error surface in weight space and, consequently, determining the steepest descent path that controls the ANN adaptation process.

  19. Advanced Measurements of the Aggregation Capability of the MPT Network Layer Multipath Communication Library

    Directory of Open Access Journals (Sweden)

    Gábor Lencse

    2015-05-01

    Full Text Available The MPT network layer multipath communicationlibrary is a novel solution for several problems including IPv6transition, reliable data transmission using TCP, real-time transmissionusing UDP and also wireless network layer routingproblems. MPT can provide an IPv4 or an IPv6 tunnel overone or more IPv4 or IPv6 communication channels. MPT canalso aggregate the capacity of multiple physical channels. In thispaper, the channel aggregation capability of the MPT libraryis measured up to twelve 100Mbps speed channels. Differentscenarios are used: both IPv4 and IPv6 are used as the underlyingand also as the encapsulated protocols and also both UDP andTCP are used as transport protocols. In addition, measurementsare taken with both 32-bit and 64-bit version of the MPT library.In all cases, the number of the physical channels is increased from1 to 12 and the aggregated throughput is measured.

  20. Defect detection on videos using neural network

    Directory of Open Access Journals (Sweden)

    Sizyakin Roman

    2017-01-01

    Full Text Available In this paper, we consider a method for defects detection in a video sequence, which consists of three main steps; frame compensation, preprocessing by a detector, which is base on the ranking of pixel values, and the classification of all pixels having anomalous values using convolutional neural networks. The effectiveness of the proposed method shown in comparison with the known techniques on several frames of the video sequence with damaged in natural conditions. The analysis of the obtained results indicates the high efficiency of the proposed method. The additional use of machine learning as postprocessing significantly reduce the likelihood of false alarm.

  1. Addressing ageing of the workforce issues by enabling knowledge management systems with social networks analysis capabilities

    International Nuclear Information System (INIS)

    Perisic, I.

    2004-01-01

    Full text: A method of addressing ageing of the workforce and knowledge transfer issues, especially in the area of potential loss of knowledge, is presented through the integration of social networks analysis capabilities within knowledge management systems. In the context of the ageing of the workforce, a key component is the identification of not only the individuals that are about to retire, but also the knowledge and the knowledge transfer capabilities that they will take with them they do so. This loss impacts decisions made about human resources 'supply side' programs such as education, but also programs for building 'communities of practices' within the IAEA community to foster development and research across regions and countries. Within this context, an integrated social network analysis component provides the ability to map out the network of knowledge on any specific topic. The stability of the network itself is a measure of the robustness of the knowledge within the selected IAEA community. Further, the network, by identifying 'brokers' and 'bridges', pinpoints key weaknesses that have to be addressed. In the case of ageing of the workforce, balancing, stabilizing and building redundancies within this social network is key to maintaining a safe nuclear policy. The core of the method relies on a system that has a holistic view of the body of knowledge accumulated within the IAEA community. For scalability issues, this system cannot replicate the plethora of potential sources of information, but rather has to harvest from each of them a set of metadata which in turn enables the knowledge management system. This metadata is defined and stored in a way to allow the rendering of a complete picture stored within the sub-systems. A key component used by the social network analysis component is, of course, the name of all individuals tied to any knowledge object within the database, but also their affiliation, country, seniority or 'age to retirement' (when

  2. Addressing ageing of the workforce issues by enabling knowledge management systems with social networks analysis capabilities

    International Nuclear Information System (INIS)

    Perisic, I.

    2004-01-01

    Full text: A method of addressing ageing of the workforce and knowledge transfer issues, especially in the area of potential loss of knowledge, is presented through the integration of social networks analysis capabilities within knowledge management systems. In the context of the ageing of the workforce, a key component is the identification of not only the individuals that are about to retire, but also the knowledge and the knowledge transfer capabilities that they will take with them they do so. This loss impacts decisions made about human resources 'supply side' programs such as education, but also programs for building 'communities of practices' within the IAEA community to foster development and research across regions and countries. Within this context, an integrated social network analysis component provides the ability to map out the network of knowledge on any specific topic. The stability of the network itself is a measure of the robustness of the knowledge within the selected IAEA community. Further, the network, by identifying 'brokers' and 'bridges', pinpoints key weaknesses that have to be addressed. In the case of ageing of the workforce, balancing, stabilizing and building redundancies within this social network is key to maintaining a safe nuclear policy. The core of the method relies on a system that has a holistic view of the body of knowledge accumulated within the IAEA community. For scalability issues, this system cannot replicate the plethora of potential sources of information, but rather has to harvest from each of them a set of metadata which in turn enables the knowledge management system. This metadata is defined and stored in a way to allow the rendering of a complete picture stored within the subsystems. A key component used by the social network analysis component is, of course, the name of all individuals tied to any knowledge object within the database, but also their affiliation, country, seniority or 'age to retirement' (when

  3. Detection capabilities and accuracy requirements of concentrations of radioactive material in air for radiation protection purposes

    International Nuclear Information System (INIS)

    Brodsky, A.

    1987-01-01

    Recent developments in the formulation of detection capability and accuracy criteria for bioassay measurements will be interpreted and adapted to provide similar criteria for the measurement of air concentrations of radioactive material for radiation protection purposes. Considerations of accuracy will be related to the known variability of measurement processes, as well as the uncertainties in the calculated limits of intake that serve as the basis of regulatory and voluntary standards of practice. Formulations and criteria will be presented for minimum detection amounts (MDA) and precision and bias of measurements for radiation protection purposes. 17 references

  4. CONTEXT-CAPTURE MULTI-VALUED DECISION FUSION WITH FAULT TOLERANT CAPABILITY FOR WIRELESS SENSOR NETWORKS

    OpenAIRE

    Jun Wu; Shigeru Shimamoto

    2011-01-01

    Wireless sensor networks (WSNs) are usually utilized to perform decision fusion of event detection. Current decision fusion schemes are based on binary valued decision and do not consider bursty contextcapture. However, bursty context and multi-valued data are important characteristics of WSNs. One on hand, the local decisions from sensors usually have bursty and contextual characteristics. Fusion center must capture the bursty context information from the sensors. On the other hand, in pract...

  5. Water Pollution Detection Based on Hypothesis Testing in Sensor Networks

    Directory of Open Access Journals (Sweden)

    Xu Luo

    2017-01-01

    Full Text Available Water pollution detection is of great importance in water conservation. In this paper, the water pollution detection problems of the network and of the node in sensor networks are discussed. The detection problems in both cases of the distribution of the monitoring noise being normal and nonnormal are considered. The pollution detection problems are analyzed based on hypothesis testing theory firstly; then, the specific detection algorithms are given. Finally, two implementation examples are given to illustrate how the proposed detection methods are used in the water pollution detection in sensor networks and prove the effectiveness of the proposed detection methods.

  6. Spatial anomaly detection in sensor networks using neighborhood information

    NARCIS (Netherlands)

    Bosman, H.H.W.J.; Iacca, G.; Tejada, A.; Wörtche, H.J.; Liotta, A.

    The field of wireless sensor networks (WSNs), embedded systems with sensing and networking capability, has now matured after a decade-long research effort and technological advances in electronics and networked systems. An important remaining challenge now is to extract meaningful information from

  7. Artificial neural network detects human uncertainty

    Science.gov (United States)

    Hramov, Alexander E.; Frolov, Nikita S.; Maksimenko, Vladimir A.; Makarov, Vladimir V.; Koronovskii, Alexey A.; Garcia-Prieto, Juan; Antón-Toro, Luis Fernando; Maestú, Fernando; Pisarchik, Alexander N.

    2018-03-01

    Artificial neural networks (ANNs) are known to be a powerful tool for data analysis. They are used in social science, robotics, and neurophysiology for solving tasks of classification, forecasting, pattern recognition, etc. In neuroscience, ANNs allow the recognition of specific forms of brain activity from multichannel EEG or MEG data. This makes the ANN an efficient computational core for brain-machine systems. However, despite significant achievements of artificial intelligence in recognition and classification of well-reproducible patterns of neural activity, the use of ANNs for recognition and classification of patterns in neural networks still requires additional attention, especially in ambiguous situations. According to this, in this research, we demonstrate the efficiency of application of the ANN for classification of human MEG trials corresponding to the perception of bistable visual stimuli with different degrees of ambiguity. We show that along with classification of brain states associated with multistable image interpretations, in the case of significant ambiguity, the ANN can detect an uncertain state when the observer doubts about the image interpretation. With the obtained results, we describe the possible application of ANNs for detection of bistable brain activity associated with difficulties in the decision-making process.

  8. A new fault detection method for computer networks

    International Nuclear Information System (INIS)

    Lu, Lu; Xu, Zhengguo; Wang, Wenhai; Sun, Youxian

    2013-01-01

    Over the past few years, fault detection for computer networks has attracted extensive attentions for its importance in network management. Most existing fault detection methods are based on active probing techniques which can detect the occurrence of faults fast and precisely. But these methods suffer from the limitation of traffic overhead, especially in large scale networks. To relieve traffic overhead induced by active probing based methods, a new fault detection method, whose key is to divide the detection process into multiple stages, is proposed in this paper. During each stage, only a small region of the network is detected by using a small set of probes. Meanwhile, it also ensures that the entire network can be covered after multiple detection stages. This method can guarantee that the traffic used by probes during each detection stage is small sufficiently so that the network can operate without severe disturbance from probes. Several simulation results verify the effectiveness of the proposed method

  9. Scientific Lightning Detection Network for Kazakhstan

    Science.gov (United States)

    Streltsov, A. V.; Lozbin, A.; Inchin, A.; Shpadi, Y.; Inchin, P.; Shpadi, M.; Ayazbayev, G.; Bykayev, R.; Mailibayeva, L.

    2015-12-01

    In the frame of grant financing of the scientific research in 2015-2017 the project "To Develop Electromagnetic System for lightning location and atmosphere-lithosphere coupling research" was found. The project was start in January, 2015 and should be done during 3 years. The purpose is to create a system of electromagnetic measurements for lightning location and atmosphere-lithosphere coupling research consisting of a network of electric and magnetic sensors and the dedicated complex for data processing and transfer to the end user. The main tasks are to set several points for electromagnetic measurements with 100-200 km distance between them, to develop equipment for these points, to develop the techniques and software for lightning location (Time-of-arrival and Direction Finding (TOA+DF)) and provide a lightning activity research in North Tien-Shan region with respect to seismicity and other natural and manmade activities. Also, it is planned to use lightning data for Global Electric Circuit (GEC) investigation. Currently, there are lightning detection networks in many countries. In Kazakhstan we have only separate units in airports. So, we don't have full lightning information for our region. It is planned, to setup 8-10 measurement points with magnetic and electric filed antennas for VLF range. The final data set should be including each stroke location, time, type (CG+, CG-, CC+ or CC-) and waveform from each station. As the magnetic field lightning antenna the ferrite rod VLF antenna will be used. As the electric field antenna the wide range antenna with specific frequencies filters will be used. For true event detection TOA and DF methods needs detected stroke from minimum 4 stations. In this case we can get location accuracy about 2-3 km and better.

  10. On Emulation-Based Network Intrusion Detection Systems

    NARCIS (Netherlands)

    Abbasi, Ali; Wetzel, Jos; Bokslag, Wouter; Zambon, Emmanuele; Etalle, Sandro

    2014-01-01

    Emulation-based network intrusion detection systems have been devised to detect the presence of shellcode in network traffic by trying to execute (portions of) the network packet payloads in an in- strumented environment and checking the execution traces for signs of shellcode activity.

  11. On emulation-based network intrusion detection systems

    NARCIS (Netherlands)

    Abbasi, A.; Wetzels, J.; Bokslag, W.; Zambon, E.; Etalle, S.; Stavrou, A.; Bos, H.; Portokalidis, G.

    2014-01-01

    Emulation-based network intrusion detection systems have been devised to detect the presence of shellcode in network traffic by trying to execute (portions of) the network packet payloads in an instrumented environment and checking the execution traces for signs of shellcode activity.

  12. A Robust Optimization Based Energy-Aware Virtual Network Function Placement Proposal for Small Cell 5G Networks with Mobile Edge Computing Capabilities

    OpenAIRE

    Blanco, Bego; Taboada, Ianire; Fajardo, Jose Oscar; Liberal, Fidel

    2017-01-01

    In the context of cloud-enabled 5G radio access networks with network function virtualization capabilities, we focus on the virtual network function placement problem for a multitenant cluster of small cells that provide mobile edge computing services. Under an emerging distributed network architecture and hardware infrastructure, we employ cloud-enabled small cells that integrate microservers for virtualization execution, equipped with additional hardware appliances. We develop an energy-awa...

  13. Data Fault Detection in Medical Sensor Networks

    Directory of Open Access Journals (Sweden)

    Yang Yang

    2015-03-01

    Full Text Available Medical body sensors can be implanted or attached to the human body to monitor the physiological parameters of patients all the time. Inaccurate data due to sensor faults or incorrect placement on the body will seriously influence clinicians’ diagnosis, therefore detecting sensor data faults has been widely researched in recent years. Most of the typical approaches to sensor fault detection in the medical area ignore the fact that the physiological indexes of patients aren’t changing synchronously at the same time, and fault values mixed with abnormal physiological data due to illness make it difficult to determine true faults. Based on these facts, we propose a Data Fault Detection mechanism in Medical sensor networks (DFD-M. Its mechanism includes: (1 use of a dynamic-local outlier factor (D-LOF algorithm to identify outlying sensed data vectors; (2 use of a linear regression model based on trapezoidal fuzzy numbers to predict which readings in the outlying data vector are suspected to be faulty; (3 the proposal of a novel judgment criterion of fault state according to the prediction values. The simulation results demonstrate the efficiency and superiority of DFD-M.

  14. Failure detection studies by layered neural network

    International Nuclear Information System (INIS)

    Ciftcioglu, O.; Seker, S.; Turkcan, E.

    1991-06-01

    Failure detection studies by layered neural network (NN) are described. The particular application area is an operating nuclear power plant and the failure detection is of concern as result of system surveillance in real-time. The NN system is considered to be consisting of 3 layers, one of which being hidden, and the NN parameters are determined adaptively by the backpropagation (BP) method, the process being the training phase. Studies are performed using the power spectra of the pressure signal of the primary system of an operating nuclear power plant of PWR type. The studies revealed that, by means of NN approach, failure detection can effectively be carried out using the redundant information as well as this is the case in this work; namely, from measurement of the primary pressure signals one can estimate the primary system coolant temperature and hence the deviation from the operational temperature state, the operational status identified in the training phase being referred to as normal. (author). 13 refs.; 4 figs.; 2 tabs

  15. Evaluating the Capability of High-Altitude Infrasound Platforms to Cover Gaps in Existing Networks.

    Energy Technology Data Exchange (ETDEWEB)

    Bowman, Daniel [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2017-09-01

    A variety of Earth surface and atmospheric sources generate low frequency sound waves that can travel great distances. Despite a rich history of ground-based sensor studies, very few experiments have investigated the prospects of free floating microphone arrays at high altitudes. However, recent initiatives have shown that such networks have very low background noise and may sample an acoustic wave field that is fundamentally different than that at the Earth's surface. The experiments have been limited to at most two stations at altitude, limiting their utility in acoustic event detection and localization. We describe the deployment of five drifting microphone stations at altitudes between 21 and 24 km above sea level. The stations detected one of two regional ground-based explosions as well as the ocean microbarom while traveling almost 500 km across the American Southwest. The explosion signal consisted of multiple arrivals; signal amplitudes did not correlate with sensor elevation or source range. A sparse network method that employed curved wave front corrections was able to determine the backazimuth from the free flying network to the acoustic source. Episodic broad band signals similar to those seen on previous flights in the same region were noted as well, but their source remains unclear. Background noise levels were commensurate with those on infrasound stations in the International Monitoring System (IMS) below 2 seconds, but sensor self noise appears to dominate at higher frequencies.

  16. Research on capability of detecting ballistic missile by near space infrared system

    Science.gov (United States)

    Lu, Li; Sheng, Wen; Jiang, Wei; Jiang, Feng

    2018-01-01

    The infrared detection technology of ballistic missile based on near space platform can effectively make up the shortcomings of high-cost of traditional early warning satellites and the limited earth curvature of ground-based early warning radar. In terms of target detection capability, aiming at the problem that the formula of the action distance based on contrast performance ignores the background emissivity in the calculation process and the formula is only valid for the monochromatic light, an improved formula of the detecting range based on contrast performance is proposed. The near space infrared imaging system parameters are introduced, the expression of the contrastive action distance formula based on the target detection of the near space platform is deduced. The detection range of the near space infrared system for the booster stage ballistic missile skin, the tail nozzle and the tail flame is calculated. The simulation results show that the near-space infrared system has the best effect on the detection of tail-flame radiation.

  17. GPR capabilities for ice thickness sampling of low salinity ice and for detecting oil in ice

    Energy Technology Data Exchange (ETDEWEB)

    Lalumiere, Louis [Sensors by Design Ltd. (Canada)

    2011-07-01

    This report discusses the performance and capabilities test of two airborne ground-penetrating radar (GPR) systems of the Bedford Institute of Oceanography (BIO), Noggin 1000 and Noggin 500, for monitoring low salinity snow and ice properties which was used to measure the thickness of brackish ice on Lake Melville in Labrador and on a tidal river in Prince Edward Island. The work of other researchers is documented and the measurement techniques proposed are compared to the actual GPR approach. Different plots of GPR data taken over snow and freshwater ice and over ice with changing salinity are discussed. An interpretation of brackish ice GPR plots done by the Noggin 1000 and Noggin 500 systems is given based on resolution criterion. Additionally, the capability of the BIO helicopter-borne GPR to detect oil-in-ice has been also investigated, and an opinion on the likelihood of the success of GPR as an oil-in-ice detector is given.

  18. fraud detection in mobile communications networks using user

    African Journals Online (AJOL)

    DEPT OF AGRICULTURAL ENGINEERING

    Fraud detection is an important application, since network operators lose a relevant portion of their revenue to fraud. ... testing the methods with data from real mobile communications networks. Keywords: Call data, fraud ...... Ph. D. thesis. Pur-.

  19. Overlapping community detection in weighted networks via a Bayesian approach

    Science.gov (United States)

    Chen, Yi; Wang, Xiaolong; Xiang, Xin; Tang, Buzhou; Chen, Qingcai; Fan, Shixi; Bu, Junzhao

    2017-02-01

    Complex networks as a powerful way to represent complex systems have been widely studied during the past several years. One of the most important tasks of complex network analysis is to detect communities embedded in networks. In the real world, weighted networks are very common and may contain overlapping communities where a node is allowed to belong to multiple communities. In this paper, we propose a novel Bayesian approach, called the Bayesian mixture network (BMN) model, to detect overlapping communities in weighted networks. The advantages of our method are (i) providing soft-partition solutions in weighted networks; (ii) providing soft memberships, which quantify 'how strongly' a node belongs to a community. Experiments on a large number of real and synthetic networks show that our model has the ability in detecting overlapping communities in weighted networks and is competitive with other state-of-the-art models at shedding light on community partition.

  20. Wireless Sensor Networks for Detection of IED Emplacement

    Science.gov (United States)

    2009-06-01

    unclassified Standard Form 298 (Rev. 8-98) Prescribed by ANSI Std Z39-18 Abstract We are investigating the use of wireless nonimaging -sensor...networks for the difficult problem of detection of suspicious behavior related to IED emplacement. Hardware for surveillance by nonimaging -sensor networks...with people crossing a live sensor network. We conclude that nonimaging -sensor networks can detect a variety of suspicious behavior, but

  1. COMPETITIVENESS, MARKETING ACCESS, AND NETWORK CAPABILITY AND ITS IMPACTS ON MARKETING PERFORMANCE

    Directory of Open Access Journals (Sweden)

    Teguh Iman Sayekti

    2016-12-01

    Full Text Available This study is to determine what factors influencingthe performance of SMEs. The population in this study is SMEs in the Central Java. Sample collection was conducted with a purposive sampling method. Criteriaused to take into accountis the SMEs that are already running at leastfortwo years. The sample in this study is intended as the representative of total population,67 (sixty seven. The data were processed and analyzed by computer program of SPSS 20 for Windows. Based on the results of regression analysis, it can be concluded that competitiveness has positive and significant impact on marketing performance marketing access has positive and significant impact on marketing performance. It means that the higher competitiveness and marketing access, the higher its performance. Competitiveness and marketing access have influence on SMEs’ performance through network capability.   

  2. Capabilities and prospects of the East Asia Very Long Baseline Interferometry Network

    Science.gov (United States)

    An, T.; Sohn, B. W.; Imai, H.

    2018-02-01

    The very long baseline interferometry (VLBI) technique offers angular resolutions superior to any other instruments at other wavelengths, enabling unique science applications of high-resolution imaging of radio sources and high-precision astrometry. The East Asia VLBI Network (EAVN) is a collaborative effort in the East Asian region. The EAVN currently consists of 21 telescopes with diverse equipment configurations and frequency setups, allowing flexible subarrays for specific science projects. The EAVN provides the highest resolution of 0.5 mas at 22 GHz, allowing the fine imaging of jets in active galactic nuclei, high-accuracy astrometry of masers and pulsars, and precise spacecraft positioning. The soon-to-be-operational Five-hundred-meter Aperture Spherical radio Telescope (FAST) will open a new era for the EAVN. This state-of-the-art VLBI array also provides easy access to and crucial training for the burgeoning Asian astronomical community. This Perspective summarizes the status, capabilities and prospects of the EAVN.

  3. Joint preprocesser-based detector for cooperative networks with limited hardware processing capability

    KAUST Repository

    Abuzaid, Abdulrahman I.

    2015-02-01

    In this letter, a joint detector for cooperative communication networks is proposed when the destination has limited hardware processing capability. The transmitter sends its symbols with the help of L relays. As the destination has limited hardware, only U out of L signals are processed and the energy of the remaining relays is lost. To solve this problem, a joint preprocessing based detector is proposed. This joint preprocessor based detector operate on the principles of minimizing the symbol error rate (SER). For a realistic assessment, pilot symbol aided channel estimation is incorporated for this proposed detector. From our simulations, it can be observed that our proposed detector achieves the same SER performance as that of the maximum likelihood (ML) detector with all participating relays. Additionally, our detector outperforms selection combining (SC), channel shortening (CS) scheme and reduced-rank techniques when using the same U. Our proposed scheme has low computational complexity.

  4. Experimental integration of quantum key distribution and gigabit-capable passive optical network

    Science.gov (United States)

    Sun, Wei; Wang, Liu-Jun; Sun, Xiang-Xiang; Mao, Yingqiu; Yin, Hua-Lei; Wang, Bi-Xiao; Chen, Teng-Yun; Pan, Jian-Wei

    2018-01-01

    Quantum key distribution (QKD) ensures information-theoretic security for the distribution of random bits between two remote parties. To extend QKD applications to fiber-to-the-home optical communications, such as gigabit-capable passive optical networks (GPONs), an effective method is the use of wavelength-division multiplexing. However, the Raman scattering noise from intensive classical traffic and the huge loss introduced by the beam splitter in a GPON severely limits the performance of QKD. Here, we demonstrate the integration of QKD and a commercial GPON system with fiber lengths up to 14 km, in which the maximum splitting ratio of the beam splitter reaches 1:64. By placing the QKD transmitter on the optical line terminal side, we reduce the Raman noise collected at the QKD receiver. Using a bypass structure, the loss of the beam splitter is circumvented effectively. Our results pave the way to extending the applications of QKD to last-mile communications.

  5. Vessel network detection using contour evolution and color components

    Energy Technology Data Exchange (ETDEWEB)

    Ushizima, Daniela; Medeiros, Fatima; Cuadros, Jorge; Martins, Charles

    2011-06-22

    Automated retinal screening relies on vasculature segmentation before the identification of other anatomical structures of the retina. Vasculature extraction can also be input to image quality ranking, neovascularization detection and image registration, among other applications. There is an extensive literature related to this problem, often excluding the inherent heterogeneity of ophthalmic clinical images. The contribution of this paper relies on an algorithm using front propagation to segment the vessel network. The algorithm includes a penalty in the wait queue on the fast marching heap to minimize leakage of the evolving interface. The method requires no manual labeling, a minimum number of parameters and it is capable of segmenting color ocular fundus images in real scenarios, where multi-ethnicity and brightness variations are parts of the problem.

  6. Towards a Global Hub and a Network for Collaborative Advancing of Space Weather Predictive Capabilities.

    Science.gov (United States)

    Kuznetsova, M. M.; Heynderickz, D.; Grande, M.; Opgenoorth, H. J.

    2017-12-01

    The COSPAR/ILWS roadmap on space weather published in 2015 (Advances in Space Research, 2015: DOI: 10.1016/j.asr.2015.03.023) prioritizes steps to be taken to advance understanding of space environment phenomena and to improve space weather forecasting capabilities. General recommendations include development of a comprehensive space environment specification, assessment of the state of the field on a 5-yr basis, standardization of meta-data and product metrics. To facilitate progress towards roadmap goals there is a need for a global hub for collaborative space weather capabilities assessment and development that brings together research, engineering, operational, educational, and end-user communities. The COSPAR Panel on Space Weather is aiming to build upon past progress and to facilitate coordination of established and new international space weather research and development initiatives. Keys to the success include creating flexible, collaborative, inclusive environment and engaging motivated groups and individuals committed to active participation in international multi-disciplinary teams focused on topics addressing emerging needs and challenges in the rapidly growing field of space weather. Near term focus includes comprehensive assessment of the state of the field and establishing an internationally recognized process to quantify and track progress over time, development of a global network of distributed web-based resources and interconnected interactive services required for space weather research, analysis, forecasting and education.

  7. Microaneurysm detection using fully convolutional neural networks.

    Science.gov (United States)

    Chudzik, Piotr; Majumdar, Somshubra; Calivá, Francesco; Al-Diri, Bashir; Hunter, Andrew

    2018-05-01

    Diabetic retinopathy is a microvascular complication of diabetes that can lead to sight loss if treated not early enough. Microaneurysms are the earliest clinical signs of diabetic retinopathy. This paper presents an automatic method for detecting microaneurysms in fundus photographies. A novel patch-based fully convolutional neural network with batch normalization layers and Dice loss function is proposed. Compared to other methods that require up to five processing stages, it requires only three. Furthermore, to the best of the authors' knowledge, this is the first paper that shows how to successfully transfer knowledge between datasets in the microaneurysm detection domain. The proposed method was evaluated using three publicly available and widely used datasets: E-Ophtha, DIARETDB1, and ROC. It achieved better results than state-of-the-art methods using the FROC metric. The proposed algorithm accomplished highest sensitivities for low false positive rates, which is particularly important for screening purposes. Performance, simplicity, and robustness of the proposed method demonstrates its suitability for diabetic retinopathy screening applications. Copyright © 2018 Elsevier B.V. All rights reserved.

  8. Multiple Gamma-Ray Detection Capability of a CeBr3 Detector for Gamma Spectroscopy

    Directory of Open Access Journals (Sweden)

    A. A. Naqvi

    2017-01-01

    Full Text Available The newly developed cerium tribromide (CeBr3 detector has reduced intrinsic gamma-ray activity with gamma energy restricted to 1400–2200 keV energy range. This narrower region of background gamma rays allows the CeBr3 detector to detect more than one gamma ray to analyze the gamma-ray spectrum. Use of multiple gamma-ray intensities in elemental analysis instead of a single one improves the accuracy of the estimated results. Multigamma-ray detection capability of a cylindrical 75 mm × 75 mm (diameter × height CeBr3 detector has been tested by analyzing the chlorine concentration in water samples using eight chlorine prompt gamma rays over 517 to 8578 keV energies utilizing a D-D portable neutron generator-based PGNAA setup and measuring the corresponding minimum detection limit (MDC of chlorine. The measured MDC of chlorine for gamma rays with 517–8578 keV energies varies from 0.07 ± 0.02 wt% to 0.80 ± 0.24. The best value of MDC was measured to be 0.07 ± 0.02 wt% for 788 keV gamma rays. The experimental results are in good agreement with Monte Carlo calculations. The study has shown excellent detection capabilities of the CeBr3 detector for eight prompt gamma rays over 517–8578 keV energy range without significant background interference.

  9. Enhancement of loss detection capability using a combination of the Kalman Filter/Linear Smoother and controllable unit accounting approach

    International Nuclear Information System (INIS)

    Pike, D.H.; Morrison, G.W.

    1979-01-01

    An approach to loss detection is presented which combines the optimal loss detection capability of state estimation techniques with a controllable unit accounting approach. The state estimation theory makes use of a linear system model which is capable of modeling the interaction of various controllable unit areas within a given facility. An example is presented which illustrates the increase in loss detection probability which is realizable with state estimation techniques. Comparisons are made with a Shewhart Control Chart and the CUSUM statistic

  10. Detection of network attacks based on adaptive resonance theory

    Science.gov (United States)

    Bukhanov, D. G.; Polyakov, V. M.

    2018-05-01

    The paper considers an approach to intrusion detection systems using a neural network of adaptive resonant theory. It suggests the structure of an intrusion detection system consisting of two types of program modules. The first module manages connections of user applications by preventing the undesirable ones. The second analyzes the incoming network traffic parameters to check potential network attacks. After attack detection, it notifies the required stations using a secure transmission channel. The paper describes the experiment on the detection and recognition of network attacks using the test selection. It also compares the obtained results with similar experiments carried out by other authors. It gives findings and conclusions on the sufficiency of the proposed approach. The obtained information confirms the sufficiency of applying the neural networks of adaptive resonant theory to analyze network traffic within the intrusion detection system.

  11. Binomial Test Method for Determining Probability of Detection Capability for Fracture Critical Applications

    Science.gov (United States)

    Generazio, Edward R.

    2011-01-01

    The capability of an inspection system is established by applications of various methodologies to determine the probability of detection (POD). One accepted metric of an adequate inspection system is that for a minimum flaw size and all greater flaw sizes, there is 0.90 probability of detection with 95% confidence (90/95 POD). Directed design of experiments for probability of detection (DOEPOD) has been developed to provide an efficient and accurate methodology that yields estimates of POD and confidence bounds for both Hit-Miss or signal amplitude testing, where signal amplitudes are reduced to Hit-Miss by using a signal threshold Directed DOEPOD uses a nonparametric approach for the analysis or inspection data that does require any assumptions about the particular functional form of a POD function. The DOEPOD procedure identifies, for a given sample set whether or not the minimum requirement of 0.90 probability of detection with 95% confidence is demonstrated for a minimum flaw size and for all greater flaw sizes (90/95 POD). The DOEPOD procedures are sequentially executed in order to minimize the number of samples needed to demonstrate that there is a 90/95 POD lower confidence bound at a given flaw size and that the POD is monotonic for flaw sizes exceeding that 90/95 POD flaw size. The conservativeness of the DOEPOD methodology results is discussed. Validated guidelines for binomial estimation of POD for fracture critical inspection are established.

  12. Field detection capability of immunochemical assays during criminal investigations involving the use of TNT.

    Science.gov (United States)

    Romolo, Francesco Saverio; Ferri, Elida; Mirasoli, Mara; D'Elia, Marcello; Ripani, Luigi; Peluso, Giuseppe; Risoluti, Roberta; Maiolini, Elisabetta; Girotti, Stefano

    2015-01-01

    The capability to collect timely information about the substances employed on-site at a crime scene is of fundamental importance during scientific investigations in crimes involving the use of explosives. TNT (2,4,6-trinitrotoluene) is one of the most employed explosives in the 20th century. Despite the growing use of improvised explosives, criminal use and access to TNT is not expected to decrease. Immunoassays are simple and selective analytical tests able to detect molecules and their immunoreactions can occur in portable formats for use on-site. This work demonstrates the application of three immunochemical assays capable of detecting TNT to typical forensic samples from experimental tests: an indirect competitive ELISA with chemiluminescent detection (CL-ELISA), a colorimetric lateral flow immunoassay (LFIA) based on colloidal gold nanoparticles label, and a chemiluminescent-LFIA (CL-LFIA). Under optimised working conditions, the LOD of the colorimetric LFIA and CL-LFIA were 1 μg mL(-1) and 0.05 μg mL(-1), respectively. The total analysis time for LFIAs was 15 min. ELISA proved to be a very effective laboratory approach, showing very good sensitivity (LOD of 0.4 ng mL(-1)) and good reproducibility (CV value about 7%). Samples tested included various materials involved in controlled explosions of improvised explosive devices (IEDs), as well as hand swabs collected after TNT handling tests. In the first group of tests, targets covered with six different materials (metal, plastic, cardboard, carpet fabric, wood and adhesive tape) were fixed on top of wooden poles (180 cm high). Samples of soil from the explosion area and different materials covering the targets were collected after each explosion and analysed. In the second group of tests, hand swabs were collected with and without hand washing after volunteers simulated the manipulation of small charges of TNT. The small amount of solution required for each assay allows for several analyses. Results of

  13. Trojan detection model based on network behavior analysis

    International Nuclear Information System (INIS)

    Liu Junrong; Liu Baoxu; Wang Wenjin

    2012-01-01

    Based on the analysis of existing Trojan detection technology, this paper presents a Trojan detection model based on network behavior analysis. First of all, we abstract description of the Trojan network behavior, then according to certain rules to establish the characteristic behavior library, and then use the support vector machine algorithm to determine whether a Trojan invasion. Finally, through the intrusion detection experiments, shows that this model can effectively detect Trojans. (authors)

  14. Patrol Detection for Replica Attacks on Wireless Sensor Networks

    OpenAIRE

    Wang, Liang-Min; Shi, Yang

    2011-01-01

    Replica attack is a critical concern in the security of wireless sensor networks. We employ mobile nodes as patrollers to detect replicas distributed in different zones in a network, in which a basic patrol detection protocol and two detection algorithms for stationary and mobile modes are presented. Then we perform security analysis to discuss the defense strategies against the possible attacks on the proposed detection protocol. Moreover, we show the advantages of the proposed protocol by d...

  15. Evaluating detection and estimation capabilities of magnetometer-based vehicle sensors

    Science.gov (United States)

    Slater, David M.; Jacyna, Garry M.

    2013-05-01

    In an effort to secure the northern and southern United States borders, MITRE has been tasked with developing Modeling and Simulation (M&S) tools that accurately capture the mapping between algorithm-level Measures of Performance (MOP) and system-level Measures of Effectiveness (MOE) for current/future surveillance systems deployed by the the Customs and Border Protection Office of Technology Innovations and Acquisitions (OTIA). This analysis is part of a larger M&S undertaking. The focus is on two MOPs for magnetometer-based Unattended Ground Sensors (UGS). UGS are placed near roads to detect passing vehicles and estimate properties of the vehicle's trajectory such as bearing and speed. The first MOP considered is the probability of detection. We derive probabilities of detection for a network of sensors over an arbitrary number of observation periods and explore how the probability of detection changes when multiple sensors are employed. The performance of UGS is also evaluated based on the level of variance in the estimation of trajectory parameters. We derive the Cramer-Rao bounds for the variances of the estimated parameters in two cases: when no a priori information is known and when the parameters are assumed to be Gaussian with known variances. Sample results show that UGS perform significantly better in the latter case.

  16. Community Detection for Multiplex Social Networks Based on Relational Bayesian Networks

    DEFF Research Database (Denmark)

    Jiang, Jiuchuan; Jaeger, Manfred

    2014-01-01

    Many techniques have been proposed for community detection in social networks. Most of these techniques are only designed for networks defined by a single relation. However, many real networks are multiplex networks that contain multiple types of relations and different attributes on the nodes...

  17. VoIP attacks detection engine based on neural network

    Science.gov (United States)

    Safarik, Jakub; Slachta, Jiri

    2015-05-01

    The security is crucial for any system nowadays, especially communications. One of the most successful protocols in the field of communication over IP networks is Session Initiation Protocol. It is an open-source project used by different kinds of applications, both open-source and proprietary. High penetration and text-based principle made SIP number one target in IP telephony infrastructure, so security of SIP server is essential. To keep up with hackers and to detect potential malicious attacks, security administrator needs to monitor and evaluate SIP traffic in the network. But monitoring and following evaluation could easily overwhelm the security administrator in networks, typically in networks with a number of SIP servers, users and logically or geographically separated networks. The proposed solution lies in automatic attack detection systems. The article covers detection of VoIP attacks through a distributed network of nodes. Then the gathered data analyze aggregation server with artificial neural network. Artificial neural network means multilayer perceptron network trained with a set of collected attacks. Attack data could also be preprocessed and verified with a self-organizing map. The source data is detected by distributed network of detection nodes. Each node contains a honeypot application and traffic monitoring mechanism. Aggregation of data from each node creates an input for neural networks. The automatic classification on a centralized server with low false positive detection reduce the cost of attack detection resources. The detection system uses modular design for easy deployment in final infrastructure. The centralized server collects and process detected traffic. It also maintains all detection nodes.

  18. Artificial Neural Network Application for Power Transfer Capability and Voltage Calculations in Multi-Area Power System

    Directory of Open Access Journals (Sweden)

    Palukuru NAGENDRA

    2010-12-01

    Full Text Available In this study, the use of artificial neural network (ANN based model, multi-layer perceptron (MLP network, to compute the transfer capabilities in a multi-area power system was explored. The input for the ANN is load status and the outputs are the transfer capability among the system areas, voltage magnitudes and voltage angles at concerned buses of the areas under consideration. The repeated power flow (RPF method is used in this paper for calculating the power transfer capability, voltage magnitudes and voltage angles necessary for the generation of input-output patterns for training the proposed MLP neural network. Preliminary investigations on a three area 30-bus system reveal that the proposed model is computationally faster than the conventional method.

  19. A divisive spectral method for network community detection

    International Nuclear Information System (INIS)

    Cheng, Jianjun; Li, Longjie; Yao, Yukai; Chen, Xiaoyun; Leng, Mingwei; Lu, Weiguo

    2016-01-01

    Community detection is a fundamental problem in the domain of complex network analysis. It has received great attention, and many community detection methods have been proposed in the last decade. In this paper, we propose a divisive spectral method for identifying community structures from networks which utilizes a sparsification operation to pre-process the networks first, and then uses a repeated bisection spectral algorithm to partition the networks into communities. The sparsification operation makes the community boundaries clearer and sharper, so that the repeated spectral bisection algorithm extract high-quality community structures accurately from the sparsified networks. Experiments show that the combination of network sparsification and a spectral bisection algorithm is highly successful, the proposed method is more effective in detecting community structures from networks than the others. (paper: interdisciplinary statistical mechanics)

  20. Radial basis function neural network in fault detection of automotive ...

    African Journals Online (AJOL)

    Radial basis function neural network in fault detection of automotive engines. ... Five faults have been simulated on the MVEM, including three sensor faults, one component fault and one actuator fault. The three sensor faults ... Keywords: Automotive engine, independent RBFNN model, RBF neural network, fault detection

  1. Network Intrusion Detection through Stacking Dilated Convolutional Autoencoders

    Directory of Open Access Journals (Sweden)

    Yang Yu

    2017-01-01

    Full Text Available Network intrusion detection is one of the most important parts for cyber security to protect computer systems against malicious attacks. With the emergence of numerous sophisticated and new attacks, however, network intrusion detection techniques are facing several significant challenges. The overall objective of this study is to learn useful feature representations automatically and efficiently from large amounts of unlabeled raw network traffic data by using deep learning approaches. We propose a novel network intrusion model by stacking dilated convolutional autoencoders and evaluate our method on two new intrusion detection datasets. Several experiments were carried out to check the effectiveness of our approach. The comparative experimental results demonstrate that the proposed model can achieve considerably high performance which meets the demand of high accuracy and adaptability of network intrusion detection systems (NIDSs. It is quite potential and promising to apply our model in the large-scale and real-world network environments.

  2. On the interference suppression capabilities of cognitive enabled femto cellular networks

    KAUST Repository

    Shakir, Muhammad; Atat, Rachad; Alouini, Mohamed-Slim

    2012-01-01

    Cognitive Radios are considered as a standard part of future Heterogeneous mobile network architecture. In this paper, we consider a two tier Heterogeneous network with multiple radio access technologies (RATS) namely; (i) the secondary network

  3. Wireless Sensor Network for Forest Fire Detection 2

    OpenAIRE

    João Gilberto Fernandes Gonçalves Teixeira

    2017-01-01

    The main purpose for this project is the development of a semi-autonomous wireless sensor network for fire detection in remote territory. Making use of the IEEE 802.15.4 standard, a wireless standard for low-power, low-rate wireless sensor networks, a real sensor network and web application will be developed and deployed with the ability to monitor sensor data, detect a fire occurrence and generate early fire alerts.

  4. Social Circles Detection from Ego Network and Profile Information

    Science.gov (United States)

    2014-12-19

    way of organizing contacts in personal networks . They are therefore currently implemented in the major social net- working systems, such as Facebook ...0704-0188 3. DATES COVERED (From - To) - UU UU UU UU Approved for public release; distribution is unlimited. Social Circles Detection from Ego Network ...structural network information but also the contents of social interactions, with the aim to detect copying communities. The views, opinions and/or findings

  5. Designing Networks that are Capable of Self-Healing and Adapting

    Science.gov (United States)

    2017-04-01

    battlefield, and utility infrastructure in cities . Networks that are easy to repair after many breaks. The origi- nal networks (left), the networks after...8725 John J. Kingman Road, MS 6201 Fort Belvoir, VA 22060-6201 T E C H N IC A L R E P O R T DTRA-TR-15-78 Designing Networks that are...from statistical mechanics, combinatorics, boolean networks , and numerical simulations, and inspired by design principles from biological networks , we

  6. Infrared interference patterns for new capabilities in laser end point detection

    International Nuclear Information System (INIS)

    Heason, D J; Spencer, A G

    2003-01-01

    Standard laser interferometry is used in dry etch fabrication of semiconductor and MEMS devices to measure etch depth, rate and to detect the process end point. However, many wafer materials, such as silicon are absorbing at probing wavelengths in the visible, severely limiting the amount of information that can be obtained using this technique. At infrared (IR) wavelengths around 1500 nm and above, silicon is highly transparent. In this paper we describe an instrument that can be used to monitor etch depth throughout a thru-wafer etch. The provision of this information could eliminate the requirement of an 'etch stop' layer and improve the performance of fabricated devices. We have added a further new capability by using tuneable lasers to scan through wavelengths in the near IR to generate an interference pattern. Fitting a theoretical curve to this interference pattern gives in situ measurement of film thickness. Whereas conventional interferometry would only allow etch depth to be monitored in real time, we can use a pre-etch thickness measurement to terminate the etch on a remaining thickness of film material. This paper discusses the capabilities of, and the opportunities offered by, this new technique and gives examples of applications in MEMS and waveguides

  7. Overlapping community detection in networks with positive and negative links

    International Nuclear Information System (INIS)

    Chen, Y; Wang, X L; Yuan, B; Tang, B Z

    2014-01-01

    Complex networks considering both positive and negative links have gained considerable attention during the past several years. Community detection is one of the main challenges for complex network analysis. Most of the existing algorithms for community detection in a signed network aim at providing a hard-partition of the network where any node should belong to a community or not. However, they cannot detect overlapping communities where a node is allowed to belong to multiple communities. The overlapping communities widely exist in many real-world networks. In this paper, we propose a signed probabilistic mixture (SPM) model for overlapping community detection in signed networks. Compared with the existing models, the advantages of our methodology are (i) providing soft-partition solutions for signed networks; (ii) providing soft memberships of nodes. Experiments on a number of signed networks show that our SPM model: (i) can identify assortative structures or disassortative structures as the same as other state-of-the-art models; (ii) can detect overlapping communities; (iii) outperforms other state-of-the-art models at shedding light on the community detection in synthetic signed networks. (paper)

  8. On the Capability of Smartphones to Perform as Communication Gateways in Medical Wireless Personal Area Networks

    Directory of Open Access Journals (Sweden)

    María José Morón

    2014-01-01

    Full Text Available This paper evaluates and characterizes the technical performance of medicalwireless personal area networks (WPANs that are based on smartphones. For this purpose,a prototype of a health telemonitoring system is presented. The prototype incorporates acommercial Android smartphone, which acts as a relay point, or “gateway”, between a setof wireless medical sensors and a data server. Additionally, the paper investigates if theconventional capabilities of current commercial smartphones can be affected by their useas gateways or “Holters” in health monitoring applications. Specifically, the profiling hasfocused on the CPU and power consumption of the mobile devices. These metrics havebeen measured under several test conditions modifying the smartphone model, the type ofsensors connected to the WPAN, the employed Bluetooth profile (SPP (serial port profile orHDP (health device profile, the use of other peripherals, such as a GPS receiver, the impactof the use of theWi-Fi interface or the employed method to encode and forward the data thatare collected from the sensors.

  9. Detecting P2P Botnet in Software Defined Networks

    Directory of Open Access Journals (Sweden)

    Shang-Chiuan Su

    2018-01-01

    Full Text Available Software Defined Network separates the control plane from network equipment and has great advantage in network management as compared with traditional approaches. With this paradigm, the security issues persist to exist and could become even worse because of the flexibility on handling the packets. In this paper we propose an effective framework by integrating SDN and machine learning to detect and categorize P2P network traffics. This work provides experimental evidence showing that our approach can automatically analyze network traffic and flexibly change flow entries in OpenFlow switches through the SDN controller. This can effectively help the network administrators manage related security problems.

  10. On Resource Description Capabilities of On-Board Tools for Resource Management in Cloud Networking and NFV Infrastructures

    OpenAIRE

    Tutschku, Kurt; Ahmadi Mehri, Vida; Carlsson, Anders; Chivukula, Krishna Varaynya; Johan, Christenson

    2016-01-01

    The rapid adoption of networks that are based on "cloudification" and Network Function Virtualisation (NFV) comes from the anticipated high cost savings of up to 70% in their build and operation. The high savings are founded in the use of general standard servers, instead of single-purpose hardware, and by efficiency resource sharing through virtualisation concepts. In this paper, we discuss the capabilities of resource description of "on-board" tools, i.e. using standard Linux commands, to e...

  11. An Entropy-Based Network Anomaly Detection Method

    Directory of Open Access Journals (Sweden)

    Przemysław Bereziński

    2015-04-01

    Full Text Available Data mining is an interdisciplinary subfield of computer science involving methods at the intersection of artificial intelligence, machine learning and statistics. One of the data mining tasks is anomaly detection which is the analysis of large quantities of data to identify items, events or observations which do not conform to an expected pattern. Anomaly detection is applicable in a variety of domains, e.g., fraud detection, fault detection, system health monitoring but this article focuses on application of anomaly detection in the field of network intrusion detection.The main goal of the article is to prove that an entropy-based approach is suitable to detect modern botnet-like malware based on anomalous patterns in network. This aim is achieved by realization of the following points: (i preparation of a concept of original entropy-based network anomaly detection method, (ii implementation of the method, (iii preparation of original dataset, (iv evaluation of the method.

  12. Stochastic Tools for Network Intrusion Detection

    OpenAIRE

    Yu, Lu; Brooks, Richard R.

    2017-01-01

    With the rapid development of Internet and the sharp increase of network crime, network security has become very important and received a lot of attention. We model security issues as stochastic systems. This allows us to find weaknesses in existing security systems and propose new solutions. Exploring the vulnerabilities of existing security tools can prevent cyber-attacks from taking advantages of the system weaknesses. We propose a hybrid network security scheme including intrusion detecti...

  13. NETWORK-CENTRIC WARFARE AND NETWORK ENABLED CAPABILITY IMPLICATIONS OVER THE C4ISR TYPE INFORMATION NETWORKS IN THE ROMANIAN ARMED FORCES

    Directory of Open Access Journals (Sweden)

    Cristea DUMITRU

    2015-06-01

    Full Text Available The last two decades mark out the humankind evolution toward the Information Age, a new stage of societal development where the modern society is affected, among other factors, by the explosive technological changes. Within the context, technology represents the main changing driver. To be more specific, small innovations appeared in the information technology and communications are considered to be responsible global transformations in the economy, politics or culture structure. This assertion also extends its validity over the military phenomenon, which is just another human behavior. The use on large scale of the information technology and communications led to a cybernetic battlefield and the change of the waging war philosophy, with the arising of new concepts that better describe the new reality: Network Centric Warfare, and Network Enabled Capabilities.

  14. Lidar Cloud Detection with Fully Convolutional Networks

    Science.gov (United States)

    Cromwell, E.; Flynn, D.

    2017-12-01

    The vertical distribution of clouds from active remote sensing instrumentation is a widely used data product from global atmospheric measuring sites. The presence of clouds can be expressed as a binary cloud mask and is a primary input for climate modeling efforts and cloud formation studies. Current cloud detection algorithms producing these masks do not accurately identify the cloud boundaries and tend to oversample or over-represent the cloud. This translates as uncertainty for assessing the radiative impact of clouds and tracking changes in cloud climatologies. The Atmospheric Radiation Measurement (ARM) program has over 20 years of micro-pulse lidar (MPL) and High Spectral Resolution Lidar (HSRL) instrument data and companion automated cloud mask product at the mid-latitude Southern Great Plains (SGP) and the polar North Slope of Alaska (NSA) atmospheric observatory. Using this data, we train a fully convolutional network (FCN) with semi-supervised learning to segment lidar imagery into geometric time-height cloud locations for the SGP site and MPL instrument. We then use transfer learning to train a FCN for (1) the MPL instrument at the NSA site and (2) for the HSRL. In our semi-supervised approach, we pre-train the classification layers of the FCN with weakly labeled lidar data. Then, we facilitate end-to-end unsupervised pre-training and transition to fully supervised learning with ground truth labeled data. Our goal is to improve the cloud mask accuracy and precision for the MPL instrument to 95% and 80%, respectively, compared to the current cloud mask algorithms of 89% and 50%. For the transfer learning based FCN for the HSRL instrument, our goal is to achieve a cloud mask accuracy of 90% and a precision of 80%.

  15. Early Wheel Train Damage Detection Using Wireless Sensor Network Antenna

    Science.gov (United States)

    Fazilah, A. F. M.; Azemi, S. N.; Azremi, A. A. H.; Soh, P. J.; Kamarudin, L. M.

    2018-03-01

    Antenna for a wireless sensor network for early wheel trains damage detection has successfully developed and fabricated with the aim to minimize the risk and increase the safety guaranty for train. Current antenna design is suffered in gain and big in size. For the sensor, current existing sensor only detect when the wheel malfunction. Thus, a compact microstrip patch antenna with operating frequency at 2.45GHz is design with high gain of 4.95dB will attach to the wireless sensor device. Simulation result shows that the antenna is working at frequency 2.45GHz and the return loss at -34.46dB are in a good agreement. The result also shows the good radiation pattern and almost ideal VSWR which is 1.04. The Arduino Nano, LM35DZ and ESP8266-07 Wi-Fi module is applied to the core system with capability to sense the temperature and send the data wirelessly to the cloud. An android application has been created to monitor the temperature reading based on the real time basis. The mainly focuses for the future improvement is by minimize the size of the antenna in order to make in more compact. In addition, upgrade an android application that can collect the raw data from cloud and make an alarm system to alert the loco pilot.

  16. Anomaly Detection in the Bitcoin System - A Network Perspective

    OpenAIRE

    Pham, Thai; Lee, Steven

    2016-01-01

    The problem of anomaly detection has been studied for a long time, and many Network Analysis techniques have been proposed as solutions. Although some results appear to be quite promising, no method is clearly to be superior to the rest. In this paper, we particularly consider anomaly detection in the Bitcoin transaction network. Our goal is to detect which users and transactions are the most suspicious; in this case, anomalous behavior is a proxy for suspicious behavior. To this end, we use ...

  17. Automated multi-radionuclide separation and analysis with combined detection capability

    Science.gov (United States)

    Plionis, Alexander Asterios

    The radiological dispersal device (RDD) is a weapon of great concern to those agencies responsible for protecting the public from the modern age of terrorism. In order to effectively respond to an RDD event, these agencies need to possess the capability to rapidly identify the radiological agents involved in the incident and assess the uptake of each individual victim. Since medical treatment for internal radiation poisoning is radionuclide-specific, it is critical to identify and quantify the radiological uptake of each individual victim. This dissertation describes the development of automated analytical components that could be used to determine and quantify multiple radionuclides in human urine bioassays. This is accomplished through the use of extraction chromatography that is plumbed in-line with one of a variety of detection instruments. Flow scintillation analysis is used for 90Sr and 210Po determination, flow gamma analysis is used assess 60 Co and 137Cs, and inductively coupled plasma mass spectrometry is used to determine actinides. Detection limits for these analytes were determined for the appropriate technique and related to their implications for health physics.

  18. Enhanced subject-specific resting-state network detection and extraction with fast fMRI.

    Science.gov (United States)

    Akin, Burak; Lee, Hsu-Lei; Hennig, Jürgen; LeVan, Pierre

    2017-02-01

    Resting-state networks have become an important tool for the study of brain function. An ultra-fast imaging technique that allows to measure brain function, called Magnetic Resonance Encephalography (MREG), achieves an order of magnitude higher temporal resolution than standard echo-planar imaging (EPI). This new sequence helps to correct physiological artifacts and improves the sensitivity of the fMRI analysis. In this study, EPI is compared with MREG in terms of capability to extract resting-state networks. Healthy controls underwent two consecutive resting-state scans, one with EPI and the other with MREG. Subject-level independent component analyses (ICA) were performed separately for each of the two datasets. Using Stanford FIND atlas parcels as network templates, the presence of ICA maps corresponding to each network was quantified in each subject. The number of detected individual networks was significantly higher in the MREG data set than for EPI. Moreover, using short time segments of MREG data, such as 50 seconds, one can still detect and track consistent networks. Fast fMRI thus results in an increased capability to extract distinct functional regions at the individual subject level for the same scan times, and also allow the extraction of consistent networks within shorter time intervals than when using EPI, which is notably relevant for the analysis of dynamic functional connectivity fluctuations. Hum Brain Mapp 38:817-830, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  19. Communication network structure parameters and new knowledge generation capabilities in companies engaged in industry control system engineering projects

    Directory of Open Access Journals (Sweden)

    Titov Sergei

    2016-01-01

    Full Text Available Engineering companies engaged in business of industry control systems need to manage the processes of generation of innovations within and across their projects. Generation and diffusion of innovations materialize through the communication networks of project teams. Therefore, it is possible to hypothesize that the characteristics of communication networks play role in generation of new knowledge. With the data from 14 industry control system projects of a Russian engineering company the communication network structure characteristics were calculated and the analysis of correlation between these characteristics and knowledge generation capabilities was performed. As a result correlation between centralization of communication and the number of new technical solutions developed in projects was discovered.

  20. Alerts Visualization and Clustering in Network-based Intrusion Detection

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Dr. Li [University of Tennessee; Gasior, Wade C [ORNL; Dasireddy, Swetha [University of Tennessee

    2010-04-01

    Today's Intrusion detection systems when deployed on a busy network overload the network with huge number of alerts. This behavior of producing too much raw information makes it less effective. We propose a system which takes both raw data and Snort alerts to visualize and analyze possible intrusions in a network. Then we present with two models for the visualization of clustered alerts. Our first model gives the network administrator with the logical topology of the network and detailed information of each node that involves its associated alerts and connections. In the second model, flocking model, presents the network administrator with the visual representation of IDS data in which each alert is represented in different color and the alerts with maximum similarity move together. This gives network administrator with the idea of detecting various of intrusions through visualizing the alert patterns.

  1. Arctic Observing Network Data Management: Current Capabilities and Their Promise for the Future

    Science.gov (United States)

    Collins, J.; Fetterer, F.; Moore, J. A.

    2008-12-01

    CADIS (the Cooperative Arctic Data and Information Service) serves as the data management, discovery and delivery component of the Arctic Observing Network (AON). As an International Polar Year (IPY) initiative, AON comprises 34 land, atmosphere and ocean observation sites, and will acquire much of the data coming from the interagency Study of Environmental Arctic Change (SEARCH). CADIS is tasked with ensuring that these observational data are managed for long term use by members of the entire Earth System Science community. Portions of CADIS are either in use by the community or available for testing. We now have an opportunity to evaluate the feedback received from our users, to identify any design shortcomings, and to identify those elements which serve their purpose well and will support future development. This presentation will focus on the nuts-and-bolts of the CADIS development to date, with an eye towards presenting lessons learned and best practices based on our experiences so far. The topics include: - How did we assess our users' needs, and how are those contributions reflected in the end product and its capabilities? - Why did we develop a CADIS metadata profile, and how does it allow CADIS to support preservation and scientific interoperability? - How can we shield the user from metadata complexities (especially those associated with various standards) while still obtaining the metadata needed to support an effective data management system? - How can we bridge the gap between the data storage formats considered convenient by researchers in the field, and those which are necessary to provide data interoperability? - What challenges have been encountered in our efforts to provide access to federated data (data stored outside of the CADIS system)? - What are the data browsing and visualization needs of the AON community, and which tools and technologies are most promising in terms of supporting those needs? A live demonstration of the current

  2. Patrol Detection for Replica Attacks on Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Yang Shi

    2011-02-01

    Full Text Available Replica attack is a critical concern in the security of wireless sensor networks. We employ mobile nodes as patrollers to detect replicas distributed in different zones in a network, in which a basic patrol detection protocol and two detection algorithms for stationary and mobile modes are presented. Then we perform security analysis to discuss the defense strategies against the possible attacks on the proposed detection protocol. Moreover, we show the advantages of the proposed protocol by discussing and comparing the communication cost and detection probability with some existing methods.

  3. Intrusion detection and monitoring for wireless networks.

    Energy Technology Data Exchange (ETDEWEB)

    Thomas, Eric D.; Van Randwyk, Jamie A.; Lee, Erik J.; Stephano, Amanda (Indiana University); Tabriz, Parisa (University of Illinois at Urbana-Champaign); Pelon, Kristen (Cedarville University); McCoy, Damon (University of Colorado, Boulder); Lodato, Mark (Lafayette College); Hemingway, Franklin (University of New Mexico); Custer, Ryan P.; Averin, Dimitry (Polytechnic University); Franklin, Jason (Carnegie Mellon University); Kilman, Dominique Marie

    2005-11-01

    Wireless computer networks are increasing exponentially around the world. They are being implemented in both the unlicensed radio frequency (RF) spectrum (IEEE 802.11a/b/g) and the licensed spectrum (e.g., Firetide [1] and Motorola Canopy [2]). Wireless networks operating in the unlicensed spectrum are by far the most popular wireless computer networks in existence. The open (i.e., proprietary) nature of the IEEE 802.11 protocols and the availability of ''free'' RF spectrum have encouraged many producers of enterprise and common off-the-shelf (COTS) computer networking equipment to jump into the wireless arena. Competition between these companies has driven down the price of 802.11 wireless networking equipment and has improved user experiences with such equipment. The end result has been an increased adoption of the equipment by businesses and consumers, the establishment of the Wi-Fi Alliance [3], and widespread use of the Alliance's ''Wi-Fi'' moniker to describe these networks. Consumers use 802.11 equipment at home to reduce the burden of running wires in existing construction, facilitate the sharing of broadband Internet services with roommates or neighbors, and increase their range of ''connectedness''. Private businesses and government entities (at all levels) are deploying wireless networks to reduce wiring costs, increase employee mobility, enable non-employees to access the Internet, and create an added revenue stream to their existing business models (coffee houses, airports, hotels, etc.). Municipalities (Philadelphia; San Francisco; Grand Haven, MI) are deploying wireless networks so they can bring broadband Internet access to places lacking such access; offer limited-speed broadband access to impoverished communities; offer broadband in places, such as marinas and state parks, that are passed over by traditional broadband providers; and provide themselves with higher quality, more

  4. System for Malicious Node Detection in IPv6-Based Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Kresimir Grgic

    2016-01-01

    Full Text Available The trend of implementing the IPv6 into wireless sensor networks (WSNs has recently occurred as a consequence of a tendency of their integration with other types of IP-based networks. The paper deals with the security aspects of these IPv6-based WSNs. A brief analysis of security threats and attacks which are present in the IPv6-based WSN is given. The solution to an adaptive distributed system for malicious node detection in the IPv6-based WSN is proposed. The proposed intrusion detection system is based on distributed algorithms and a collective decision-making process. It introduces an innovative concept of probability estimation for malicious behaviour of sensor nodes. The proposed system is implemented and tested through several different scenarios in three different network topologies. Finally, the performed analysis showed that the proposed system is energy efficient and has a good capability to detect malicious nodes.

  5. ADAPTIVE MONITORING TO ENHANCE WATER SENSOR CAPABILITIES FOR CHEMICAL AND BIOLOGICAL CONTAMINANT DETECTION IN DRINKING WATER SYSTEMS

    Science.gov (United States)

    Optoelectronic and other conventional water quality sensors offer a potential for real-time online detection of chemical and biological contaminants in a drinking water supply and distribution system. The nature of the application requires sensors of detection capabilities at lo...

  6. DETECTION OF TOPOLOGICAL PATTERNS IN PROTEIN NETWORKS.

    Energy Technology Data Exchange (ETDEWEB)

    MASLOV,S.SNEPPEN,K.

    2003-11-17

    Complex networks appear in biology on many different levels: (1) All biochemical reactions taking place in a single cell constitute its metabolic network, where nodes are individual metabolites, and edges are metabolic reactions converting them to each other. (2) Virtually every one of these reactions is catalyzed by an enzyme and the specificity of this catalytic function is ensured by the key and lock principle of its physical interaction with the substrate. Often the functional enzyme is formed by several mutually interacting proteins. Thus the structure of the metabolic network is shaped by the network of physical interactions of cell's proteins with their substrates and each other. (3) The abundance and the level of activity of each of the proteins in the physical interaction network in turn is controlled by the regulatory network of the cell. Such regulatory network includes all of the multiple mechanisms in which proteins in the cell control each other including transcriptional and translational regulation, regulation of mRNA editing and its transport out of the nucleus, specific targeting of individual proteins for degradation, modification of their activity e.g. by phosphorylation/dephosphorylation or allosteric regulation, etc. To get some idea about the complexity and interconnectedness of protein-protein regulations in baker's yeast Saccharomyces Cerevisiae in Fig. 1 we show a part of the regulatory network corresponding to positive or negative regulations that regulatory proteins exert on each other. (4) On yet higher level individual cells of a multicellular organism exchange signals with each other. This gives rise to several new networks such as e.g. nervous, hormonal, and immune systems of animals. The intercellular signaling network stages the development of a multicellular organism from the fertilized egg. (5) Finally, on the grandest scale, the interactions between individual species in ecosystems determine their food webs. An

  7. Assessment of leak detection capability of Candu 6 annulus gas system using moisture injection tests

    International Nuclear Information System (INIS)

    Nho, Ki Man; Kim, Wang Bae

    1998-01-01

    The Candu 6 reactor assembly consists of an array of 380 pressure tubes, which are installed horizontally in a large cylindrical vessel, the Calandria, containing the low pressure heavy water moderator. The pressure tube is located inside calandria tube and the annulus between these tubes, which forms a closed loop with CO 2 gas recirculating, is called the Annulus Gas System (AGS). It is designed to give an alarm to the operator even for a small pressure tube leak by a very sensitive dew point meter so that he can take a preventive action for the pressure tbe rupture incident. To judge whether the operator action time is enough or not in the design of Wolsung 2, 3, and 4, the Leak Before Break (LBB) assessment is required for the analysis of the pressure tube failure accident. In order to provide the required data for the LBB assessment of Wolsung Units 2, 3, 4, a series of leak detection capability tests was performed by injecting controlled rates of heavy water vapour. The data of increased dew point and rates of rise were measured to determine the alarm set point for dew point rate of rise of Wolsung Unit 2. It was found that the response of the dew point depends on the moisture injection rate, CO 2 gas flow rate and the leak location. The test showed that Candu 6 AGS can detect the very small leaks less than few g/hr and dew point rate of rise alarm can be the most reliable alarm signal to warn the operator. Considering the present results, the first response time of dew point to the AGS CO 2 flow rate is approximated. (author)

  8. Network Traffic Features for Anomaly Detection in Specific Industrial Control System Network

    Directory of Open Access Journals (Sweden)

    Matti Mantere

    2013-09-01

    Full Text Available The deterministic and restricted nature of industrial control system networks sets them apart from more open networks, such as local area networks in office environments. This improves the usability of network security, monitoring approaches that would be less feasible in more open environments. One of such approaches is machine learning based anomaly detection. Without proper customization for the special requirements of the industrial control system network environment, many existing anomaly or misuse detection systems will perform sub-optimally. A machine learning based approach could reduce the amount of manual customization required for different industrial control system networks. In this paper we analyze a possible set of features to be used in a machine learning based anomaly detection system in the real world industrial control system network environment under investigation. The network under investigation is represented by architectural drawing and results derived from network trace analysis. The network trace is captured from a live running industrial process control network and includes both control data and the data flowing between the control network and the office network. We limit the investigation to the IP traffic in the traces.

  9. fraud detection in mobile communications networks using user

    African Journals Online (AJOL)

    DEPT OF AGRICULTURAL ENGINEERING

    testing the methods with data from real mobile communications networks. Keywords: Call .... System. Monitoring. Database. Database. Fig. 3: Mobile communication detection tools ..... receiver operating characteristic curve (ROC). ROC is a ...

  10. Change Detection Algorithms for Information Assurance of Computer Networks

    National Research Council Canada - National Science Library

    Cardenas, Alvaro A

    2002-01-01

    .... In this thesis, the author will focus on the detection of three attack scenarios: the spreading of active worms throughout the Internet, distributed denial of service attacks, and routing attacks to wireless ad hoc networks...

  11. Ensemble of classifiers based network intrusion detection system performance bound

    CSIR Research Space (South Africa)

    Mkuzangwe, Nenekazi NP

    2017-11-01

    Full Text Available This paper provides a performance bound of a network intrusion detection system (NIDS) that uses an ensemble of classifiers. Currently researchers rely on implementing the ensemble of classifiers based NIDS before they can determine the performance...

  12. A Novel Congestion Detection Scheme in TCP Over OBS Networks

    KAUST Repository

    Shihada, Basem; Ho, Pin-Han; Zhang, Qiong

    2009-01-01

    This paper introduces a novel congestion detection scheme for high-bandwidth TCP flows over optical burst switching (OBS) networks, called statistical additive increase multiplicative decrease (SAIMD). SAIMD maintains and analyzes a number

  13. On Event Detection and Localization in Acyclic Flow Networks

    KAUST Repository

    Suresh, Mahima Agumbe; Stoleru, Radu; Zechman, Emily M.; Shihada, Basem

    2013-01-01

    Acyclic flow networks, present in many infrastructures of national importance (e.g., oil and gas and water distribution systems), have been attracting immense research interest. Existing solutions for detecting and locating attacks against

  14. Efficient Cancer Detection Using Multiple Neural Networks.

    Science.gov (United States)

    Shell, John; Gregory, William D

    2017-01-01

    The inspection of live excised tissue specimens to ascertain malignancy is a challenging task in dermatopathology and generally in histopathology. We introduce a portable desktop prototype device that provides highly accurate neural network classification of malignant and benign tissue. The handheld device collects 47 impedance data samples from 1 Hz to 32 MHz via tetrapolar blackened platinum electrodes. The data analysis was implemented with six different backpropagation neural networks (BNN). A data set consisting of 180 malignant and 180 benign breast tissue data files in an approved IRB study at the Aurora Medical Center, Milwaukee, WI, USA, were utilized as a neural network input. The BNN structure consisted of a multi-tiered consensus approach autonomously selecting four of six neural networks to determine a malignant or benign classification. The BNN analysis was then compared with the histology results with consistent sensitivity of 100% and a specificity of 100%. This implementation successfully relied solely on statistical variation between the benign and malignant impedance data and intricate neural network configuration. This device and BNN implementation provides a novel approach that could be a valuable tool to augment current medical practice assessment of the health of breast, squamous, and basal cell carcinoma and other excised tissue without requisite tissue specimen expertise. It has the potential to provide clinical management personnel with a fast non-invasive accurate assessment of biopsied or sectioned excised tissue in various clinical settings.

  15. Power to Detect Intervention Effects on Ensembles of Social Networks

    Science.gov (United States)

    Sweet, Tracy M.; Junker, Brian W.

    2016-01-01

    The hierarchical network model (HNM) is a framework introduced by Sweet, Thomas, and Junker for modeling interventions and other covariate effects on ensembles of social networks, such as what would be found in randomized controlled trials in education research. In this article, we develop calculations for the power to detect an intervention…

  16. Outlier Detection Techniques For Wireless Sensor Networks: A Survey

    NARCIS (Netherlands)

    Zhang, Y.; Meratnia, Nirvana; Havinga, Paul J.M.

    2008-01-01

    In the field of wireless sensor networks, measurements that significantly deviate from the normal pattern of sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the network. Traditional outlier detection techniques are

  17. Fusion of Heterogeneous Intrusion Detection Systems for Network Attack Detection

    Directory of Open Access Journals (Sweden)

    Jayakumar Kaliappan

    2015-01-01

    Full Text Available An intrusion detection system (IDS helps to identify different types of attacks in general, and the detection rate will be higher for some specific category of attacks. This paper is designed on the idea that each IDS is efficient in detecting a specific type of attack. In proposed Multiple IDS Unit (MIU, there are five IDS units, and each IDS follows a unique algorithm to detect attacks. The feature selection is done with the help of genetic algorithm. The selected features of the input traffic are passed on to the MIU for processing. The decision from each IDS is termed as local decision. The fusion unit inside the MIU processes all the local decisions with the help of majority voting rule and makes the final decision. The proposed system shows a very good improvement in detection rate and reduces the false alarm rate.

  18. Detection Capability Evaluation on Chang'e-5 Lunar Mineralogical Spectrometer (LMS)

    Science.gov (United States)

    Liu, Bin; Ren, Xin; Yan, Wei; Xu, Xuesen; Cai, Tingni; Liu, Dawei; Liu, Jianjun; Li, Chunlai

    2016-04-01

    The Chang'e-5 (CE-5) lunar sample return mission is scheduled to launch in 2017 to bring back lunar regolith and drill samples. The Chang'e-5 Lunar Mineralogical Spectrometer (LMS), as one of the three sets of scientific payload installed on the lander, is used to collect in-situ spectrum and analyze the mineralogical composition of the sampling site. It can also help to select the sampling site , and to compare the measured laboratory spectrum of returned sample with in-situ data. LMS employs acousto-optic tunable filters (AOTFs) and is composed of a VIS/NIR module (0.48μm-1.45μm) and an IR module (1.4μm -3.2μm). It has spectral resolution ranging from 3 to 25 nm, with a field of view (FOV) of 4.24°×4.24°. Unlike Chang'e-3 VIS/NIR Imaging Spectrometer (VNIS), the spectral coverage of LMS is extended from 2.4μm to 3.2μm, which has capability to identify H2O/OH absorption features around 2.7μm. An aluminum plate and an Infragold plate are fixed in the dust cover, being used as calibration targets in the VIS/NIR and IR spectral range respectively when the dust cover is open. Before launch, a ground verification test of LMS needs to be conducted in order to: 1) test and verify the detection capability of LMS through evaluation on the quality of image and spectral data collected for the simulated lunar samples; and 2) evaluate the accuracy of data processing methods by the simulation of instrument working on the moon. The ground verification test will be conducted both in the lab and field. The spectra of simulated lunar regolith/mineral samples will be collected simultaneously by the LMS and two calibrated spectrometers: a FTIR spectrometer (Model 102F) and an ASD FieldSpec 4 Hi-Res spectrometer. In this study, the results of the LMS ground verification test will be reported including the evaluation on the LMS spectral and image data quality, mineral identification and inversion ability, accuracy of calibration and geometric positioning .

  19. An Ensemble of HMMs for Cognitive Fault Detection in Distributed Sensor Networks

    OpenAIRE

    Roveri , Manuel; Trovò , Francesco

    2014-01-01

    Part 3: Social Media and Mobile Applications of AI; International audience; Distributed sensor networks working in harsh environmental conditions can suffer from permanent or transient faults affecting the embedded electronics or the sensors. Fault Diagnosis Systems (FDSs) have been widely studied in the literature to detect, isolate, identify, and possibly accommodate faults. Recently introduced cognitive FDSs, which represent a novel generation of FDSs, are characterized by the capability t...

  20. A novel community detection method in bipartite networks

    Science.gov (United States)

    Zhou, Cangqi; Feng, Liang; Zhao, Qianchuan

    2018-02-01

    Community structure is a common and important feature in many complex networks, including bipartite networks, which are used as a standard model for many empirical networks comprised of two types of nodes. In this paper, we propose a two-stage method for detecting community structure in bipartite networks. Firstly, we extend the widely-used Louvain algorithm to bipartite networks. The effectiveness and efficiency of the Louvain algorithm have been proved by many applications. However, there lacks a Louvain-like algorithm specially modified for bipartite networks. Based on bipartite modularity, a measure that extends unipartite modularity and that quantifies the strength of partitions in bipartite networks, we fill the gap by developing the Bi-Louvain algorithm that iteratively groups the nodes in each part by turns. This algorithm in bipartite networks often produces a balanced network structure with equal numbers of two types of nodes. Secondly, for the balanced network yielded by the first algorithm, we use an agglomerative clustering method to further cluster the network. We demonstrate that the calculation of the gain of modularity of each aggregation, and the operation of joining two communities can be compactly calculated by matrix operations for all pairs of communities simultaneously. At last, a complete hierarchical community structure is unfolded. We apply our method to two benchmark data sets and a large-scale data set from an e-commerce company, showing that it effectively identifies community structure in bipartite networks.

  1. Airplane detection in remote sensing images using convolutional neural networks

    Science.gov (United States)

    Ouyang, Chao; Chen, Zhong; Zhang, Feng; Zhang, Yifei

    2018-03-01

    Airplane detection in remote sensing images remains a challenging problem and has also been taking a great interest to researchers. In this paper we propose an effective method to detect airplanes in remote sensing images using convolutional neural networks. Deep learning methods show greater advantages than the traditional methods with the rise of deep neural networks in target detection, and we give an explanation why this happens. To improve the performance on detection of airplane, we combine a region proposal algorithm with convolutional neural networks. And in the training phase, we divide the background into multi classes rather than one class, which can reduce false alarms. Our experimental results show that the proposed method is effective and robust in detecting airplane.

  2. Botnet detection and prevention in anonymous networks

    NARCIS (Netherlands)

    Kuhnert, Katharina; Steinberger, Jessica; Baier, Harald

    Botnets are a major threat to the Internet landscape and have been responsible for large scale distributed attacks on online services. To make take down measures more difficult, Botnet operators started to incorporate anonymous networks into their software to protect their users and their Botnets.

  3. Designing Networks that are Capable of Self-Healing and Adapting

    Science.gov (United States)

    2017-04-01

    from statistical mechanics, combinatorics, boolean networks, and numerical simulations, and inspired by design principles from biological networks, we... principles for self-healing networks, and applications, and construct an all-possible-paths model for network adaptation. 2015-11-16 UNIT CONVERSION...kg m –3 ) pound-force (lbf avoirdupois) 4.448 222 newton (N) Energy/Work/Power electron volt (eV) 1.602 177 × 10 –19 joule (J) erg 1 × 10 –7

  4. Survey of the WHO-REMPAN network's capability for strengthening preparedness for radiological and nuclear emergencies

    Energy Technology Data Exchange (ETDEWEB)

    Kumagai, A [Takashi Nagai Memorial International Hibakusha Medical Centre, Nagasaki Univ. Hospital, Nagasaki (Japan); Carr, Z [Dept. of Public Health and Environment, Health Security and Environment Cluster, World Health Organization, Geneva (Switzerland); Akira, O [Takashi Nagai Memorial International Hibakusha Medical Centre, Nagasaki Univ. Hospital, Nagasaki (Japan); Christie, D [Dept. of Public Health and Environment, Health Security and Environment Cluster, World Health Organization, Geneva (Switzerland); Yamashita, S [Takashi Nagai Memorial International Hibakusha Medical Centre, Nagasaki Univ. Hospital, Nagasaki (Japan); Fukushima Medical Univ. (Japan)

    2012-07-01

    This paper investigates the capacity of the World Health Organization (WHO)-REMPAN network in responding to radiological incidents and nuclear emergencies. A survey developed by the WHO Secretariat and Nagasaki Univ. was sent to all 40 WHO-REMPAN collaborating centres and liaison institutes in order to verify the current situation of the network, identify needs and collect suggestions for future improvements. Most of the responding institutions said they were satisfied with the current status of the network. However, several responses to the survey indicate that better internal communication is needed, as well as a position document to specify the roles, rights and responsibilities of the network members. (authors)

  5. Survey of the WHO-REMPAN network's capability for strengthening preparedness for radiological and nuclear emergencies

    International Nuclear Information System (INIS)

    Kumagai, A.; Carr, Z.; Akira, O.; Christie, D.; Yamashita, S.

    2012-01-01

    This paper investigates the capacity of the World Health Organization (WHO)-REMPAN network in responding to radiological incidents and nuclear emergencies. A survey developed by the WHO Secretariat and Nagasaki Univ. was sent to all 40 WHO-REMPAN collaborating centres and liaison institutes in order to verify the current situation of the network, identify needs and collect suggestions for future improvements. Most of the responding institutions said they were satisfied with the current status of the network. However, several responses to the survey indicate that better internal communication is needed, as well as a position document to specify the roles, rights and responsibilities of the network members. (authors)

  6. Cellular telephone-based wide-area radiation detection network

    Science.gov (United States)

    Craig, William W [Pittsburg, CA; Labov, Simon E [Berkeley, CA

    2009-06-09

    A network of radiation detection instruments, each having a small solid state radiation sensor module integrated into a cellular phone for providing radiation detection data and analysis directly to a user. The sensor module includes a solid-state crystal bonded to an ASIC readout providing a low cost, low power, light weight compact instrument to detect and measure radiation energies in the local ambient radiation field. In particular, the photon energy, time of event, and location of the detection instrument at the time of detection is recorded for real time transmission to a central data collection/analysis system. The collected data from the entire network of radiation detection instruments are combined by intelligent correlation/analysis algorithms which map the background radiation and detect, identify and track radiation anomalies in the region.

  7. Data Fusion for Network Intrusion Detection: A Review

    Directory of Open Access Journals (Sweden)

    Guoquan Li

    2018-01-01

    Full Text Available Rapid progress of networking technologies leads to an exponential growth in the number of unauthorized or malicious network actions. As a component of defense-in-depth, Network Intrusion Detection System (NIDS has been expected to detect malicious behaviors. Currently, NIDSs are implemented by various classification techniques, but these techniques are not advanced enough to accurately detect complex or synthetic attacks, especially in the situation of facing massive high-dimensional data. Besides, the inherent defects of NIDSs, namely, high false alarm rate and low detection rate, have not been effectively solved. In order to solve these problems, data fusion (DF has been applied into network intrusion detection and has achieved good results. However, the literature still lacks thorough analysis and evaluation on data fusion techniques in the field of intrusion detection. Therefore, it is necessary to conduct a comprehensive review on them. In this article, we focus on DF techniques for network intrusion detection and propose a specific definition to describe it. We review the recent advances of DF techniques and propose a series of criteria to compare their performance. Finally, based on the results of the literature review, a number of open issues and future research directions are proposed at the end of this work.

  8. Low-complexity object detection with deep convolutional neural network for embedded systems

    Science.gov (United States)

    Tripathi, Subarna; Kang, Byeongkeun; Dane, Gokce; Nguyen, Truong

    2017-09-01

    We investigate low-complexity convolutional neural networks (CNNs) for object detection for embedded vision applications. It is well-known that consolidation of an embedded system for CNN-based object detection is more challenging due to computation and memory requirement comparing with problems like image classification. To achieve these requirements, we design and develop an end-to-end TensorFlow (TF)-based fully-convolutional deep neural network for generic object detection task inspired by one of the fastest framework, YOLO.1 The proposed network predicts the localization of every object by regressing the coordinates of the corresponding bounding box as in YOLO. Hence, the network is able to detect any objects without any limitations in the size of the objects. However, unlike YOLO, all the layers in the proposed network is fully-convolutional. Thus, it is able to take input images of any size. We pick face detection as an use case. We evaluate the proposed model for face detection on FDDB dataset and Widerface dataset. As another use case of generic object detection, we evaluate its performance on PASCAL VOC dataset. The experimental results demonstrate that the proposed network can predict object instances of different sizes and poses in a single frame. Moreover, the results show that the proposed method achieves comparative accuracy comparing with the state-of-the-art CNN-based object detection methods while reducing the model size by 3× and memory-BW by 3 - 4× comparing with one of the best real-time CNN-based object detectors, YOLO. Our 8-bit fixed-point TF-model provides additional 4× memory reduction while keeping the accuracy nearly as good as the floating-point model. Moreover, the fixed- point model is capable of achieving 20× faster inference speed comparing with the floating-point model. Thus, the proposed method is promising for embedded implementations.

  9. Detecting earthquakes over a seismic network using single-station similarity measures

    Science.gov (United States)

    Bergen, Karianne J.; Beroza, Gregory C.

    2018-06-01

    New blind waveform-similarity-based detection methods, such as Fingerprint and Similarity Thresholding (FAST), have shown promise for detecting weak signals in long-duration, continuous waveform data. While blind detectors are capable of identifying similar or repeating waveforms without templates, they can also be susceptible to false detections due to local correlated noise. In this work, we present a set of three new methods that allow us to extend single-station similarity-based detection over a seismic network; event-pair extraction, pairwise pseudo-association, and event resolution complete a post-processing pipeline that combines single-station similarity measures (e.g. FAST sparse similarity matrix) from each station in a network into a list of candidate events. The core technique, pairwise pseudo-association, leverages the pairwise structure of event detections in its network detection model, which allows it to identify events observed at multiple stations in the network without modeling the expected moveout. Though our approach is general, we apply it to extend FAST over a sparse seismic network. We demonstrate that our network-based extension of FAST is both sensitive and maintains a low false detection rate. As a test case, we apply our approach to 2 weeks of continuous waveform data from five stations during the foreshock sequence prior to the 2014 Mw 8.2 Iquique earthquake. Our method identifies nearly five times as many events as the local seismicity catalogue (including 95 per cent of the catalogue events), and less than 1 per cent of these candidate events are false detections.

  10. Power-Aware Intrusion Detection in Mobile Ad Hoc Networks

    Science.gov (United States)

    Şen, Sevil; Clark, John A.; Tapiador, Juan E.

    Mobile ad hoc networks (MANETs) are a highly promising new form of networking. However they are more vulnerable to attacks than wired networks. In addition, conventional intrusion detection systems (IDS) are ineffective and inefficient for highly dynamic and resource-constrained environments. Achieving an effective operational MANET requires tradeoffs to be made between functional and non-functional criteria. In this paper we show how Genetic Programming (GP) together with a Multi-Objective Evolutionary Algorithm (MOEA) can be used to synthesise intrusion detection programs that make optimal tradeoffs between security criteria and the power they consume.

  11. Dynamic baseline detection method for power data network service

    Science.gov (United States)

    Chen, Wei

    2017-08-01

    This paper proposes a dynamic baseline Traffic detection Method which is based on the historical traffic data for the Power data network. The method uses Cisco's NetFlow acquisition tool to collect the original historical traffic data from network element at fixed intervals. This method uses three dimensions information including the communication port, time, traffic (number of bytes or number of packets) t. By filtering, removing the deviation value, calculating the dynamic baseline value, comparing the actual value with the baseline value, the method can detect whether the current network traffic is abnormal.

  12. Direct observation of single-charge-detection capability of nanowire field-effect transistors.

    Science.gov (United States)

    Salfi, J; Savelyev, I G; Blumin, M; Nair, S V; Ruda, H E

    2010-10-01

    A single localized charge can quench the luminescence of a semiconductor nanowire, but relatively little is known about the effect of single charges on the conductance of the nanowire. In one-dimensional nanostructures embedded in a material with a low dielectric permittivity, the Coulomb interaction and excitonic binding energy are much larger than the corresponding values when embedded in a material with the same dielectric permittivity. The stronger Coulomb interaction is also predicted to limit the carrier mobility in nanowires. Here, we experimentally isolate and study the effect of individual localized electrons on carrier transport in InAs nanowire field-effect transistors, and extract the equivalent charge sensitivity. In the low carrier density regime, the electrostatic potential produced by one electron can create an insulating weak link in an otherwise conducting nanowire field-effect transistor, modulating its conductance by as much as 4,200% at 31 K. The equivalent charge sensitivity, 4 × 10(-5) e Hz(-1/2) at 25 K and 6 × 10(-5) e Hz(-1/2) at 198 K, is orders of magnitude better than conventional field-effect transistors and nanoelectromechanical systems, and is just a factor of 20-30 away from the record sensitivity for state-of-the-art single-electron transistors operating below 4 K (ref. 8). This work demonstrates the feasibility of nanowire-based single-electron memories and illustrates a physical process of potential relevance for high performance chemical sensors. The charge-state-detection capability we demonstrate also makes the nanowire field-effect transistor a promising host system for impurities (which may be introduced intentionally or unintentionally) with potentially long spin lifetimes, because such transistors offer more sensitive spin-to-charge conversion readout than schemes based on conventional field-effect transistors.

  13. Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security.

    Directory of Open Access Journals (Sweden)

    Min-Joo Kang

    Full Text Available A novel intrusion detection system (IDS using a deep neural network (DNN is proposed to enhance the security of in-vehicular network. The parameters building the DNN structure are trained with probability-based feature vectors that are extracted from the in-vehicular network packets. For a given packet, the DNN provides the probability of each class discriminating normal and attack packets, and, thus the sensor can identify any malicious attack to the vehicle. As compared to the traditional artificial neural network applied to the IDS, the proposed technique adopts recent advances in deep learning studies such as initializing the parameters through the unsupervised pre-training of deep belief networks (DBN, therefore improving the detection accuracy. It is demonstrated with experimental results that the proposed technique can provide a real-time response to the attack with a significantly improved detection ratio in controller area network (CAN bus.

  14. Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security.

    Science.gov (United States)

    Kang, Min-Joo; Kang, Je-Won

    2016-01-01

    A novel intrusion detection system (IDS) using a deep neural network (DNN) is proposed to enhance the security of in-vehicular network. The parameters building the DNN structure are trained with probability-based feature vectors that are extracted from the in-vehicular network packets. For a given packet, the DNN provides the probability of each class discriminating normal and attack packets, and, thus the sensor can identify any malicious attack to the vehicle. As compared to the traditional artificial neural network applied to the IDS, the proposed technique adopts recent advances in deep learning studies such as initializing the parameters through the unsupervised pre-training of deep belief networks (DBN), therefore improving the detection accuracy. It is demonstrated with experimental results that the proposed technique can provide a real-time response to the attack with a significantly improved detection ratio in controller area network (CAN) bus.

  15. Business-IT alignment in PSS value networks : a capability-based framework

    NARCIS (Netherlands)

    Bagheri, S.; Kusters, R.J.; Trienekens, J.J.M.; Camarinha-Matos, L.M.; Afsarmanesh, H.

    2014-01-01

    Advanced information technology (IT) is regarded as a foundation for the operation of product-service system (PSS) value networks. This requires alignment between IT and PSS business strategy. Business‐IT alignment (BIA) in a value network can raise the ability of partners to collaborate effectively

  16. Methods of Profile Cloning Detection in Online Social Networks

    Directory of Open Access Journals (Sweden)

    Zabielski Michał

    2016-01-01

    Full Text Available With the arrival of online social networks, the importance of privacy on the Internet has increased dramatically. Thus, it is important to develop mechanisms that will prevent our hidden personal data from unauthorized access and use. In this paper an attempt was made to present a concept of profile cloning detection in Online Social Networks (OSN using Graph and Networks Theory. By analysing structural similarity of network and value of attributes of user personal profile, we will be able to search for attackers which steal our identity.

  17. Detecting Distributed Network Traffic Anomaly with Network-Wide Correlation Analysis

    Directory of Open Access Journals (Sweden)

    Yang Dan

    2008-12-01

    Full Text Available Distributed network traffic anomaly refers to a traffic abnormal behavior involving many links of a network and caused by the same source (e.g., DDoS attack, worm propagation. The anomaly transiting in a single link might be unnoticeable and hard to detect, while the anomalous aggregation from many links can be prevailing, and does more harm to the networks. Aiming at the similar features of distributed traffic anomaly on many links, this paper proposes a network-wide detection method by performing anomalous correlation analysis of traffic signals' instantaneous parameters. In our method, traffic signals' instantaneous parameters are firstly computed, and their network-wide anomalous space is then extracted via traffic prediction. Finally, an anomaly is detected by a global correlation coefficient of anomalous space. Our evaluation using Abilene traffic traces demonstrates the excellent performance of this approach for distributed traffic anomaly detection.

  18. Anomaly detection in an automated safeguards system using neural networks

    International Nuclear Information System (INIS)

    Whiteson, R.; Howell, J.A.

    1992-01-01

    An automated safeguards system must be able to detect an anomalous event, identify the nature of the event, and recommend a corrective action. Neural networks represent a new way of thinking about basic computational mechanisms for intelligent information processing. In this paper, we discuss the issues involved in applying a neural network model to the first step of this process: anomaly detection in materials accounting systems. We extend our previous model to a 3-tank problem and compare different neural network architectures and algorithms. We evaluate the computational difficulties in training neural networks and explore how certain design principles affect the problems. The issues involved in building a neural network architecture include how the information flows, how the network is trained, how the neurons in a network are connected, how the neurons process information, and how the connections between neurons are modified. Our approach is based on the demonstrated ability of neural networks to model complex, nonlinear, real-time processes. By modeling the normal behavior of the processes, we can predict how a system should be behaving and, therefore, detect when an abnormality occurs

  19. Patch layout generation by detecting feature networks

    KAUST Repository

    Cao, Yuanhao; Yan, Dongming; Wonka, Peter

    2015-01-01

    The patch layout of 3D surfaces reveals the high-level geometric and topological structures. In this paper, we study the patch layout computation by detecting and enclosing feature loops on surfaces. We present a hybrid framework which combines

  20. Incorporating profile information in community detection for online social networks

    Science.gov (United States)

    Fan, W.; Yeung, K. H.

    2014-07-01

    Community structure is an important feature in the study of complex networks. It is because nodes of the same community may have similar properties. In this paper we extend two popular community detection methods to partition online social networks. In our extended methods, the profile information of users is used for partitioning. We apply the extended methods in several sample networks of Facebook. Compared with the original methods, the community structures we obtain have higher modularity. Our results indicate that users' profile information is consistent with the community structure of their friendship network to some extent. To the best of our knowledge, this paper is the first to discuss how profile information can be used to improve community detection in online social networks.

  1. Utilizing Weak Indicators to Detect Anomalous Behaviors in Networks

    Energy Technology Data Exchange (ETDEWEB)

    Egid, Adin [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2017-11-01

    We consider the use of a novel weak in- dicator alongside more commonly used weak indicators to help detect anomalous behavior in a large computer network. The data of the network which we are studying in this research paper concerns remote log-in information (Virtual Private Network, or VPN sessions) from the internal network of Los Alamos National Laboratory (LANL). The novel indicator we are utilizing is some- thing which, while novel in its application to data science/cyber security research, is a concept borrowed from the business world. The Her ndahl-Hirschman Index (HHI) is a computationally trivial index which provides a useful heuristic for regulatory agencies to ascertain the relative competitiveness of a particular industry. Using this index as a lagging indicator in the monthly format we have studied could help to detect anomalous behavior by a particular or small set of users on the network.

  2. Utilizing Weak Indicators to Detect Anomalous Behaviors in Networks

    Energy Technology Data Exchange (ETDEWEB)

    Egid, Adin Ezra [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2018-02-06

    We consider the use of a novel weak in- dicator alongside more commonly used weak indicators to help detect anomalous behavior in a large computer network. The data of the network which we are studying in this research paper concerns remote log-in information (Virtual Private Network, or VPN sessions) from the internal network of Los Alamos National Laboratory (LANL). The novel indicator we are utilizing is some- thing which, while novel in its application to data science/cyber security research, is a concept borrowed from the business world. The Her ndahl-Hirschman Index (HHI) is a computationally trivial index which provides a useful heuristic for regulatory agencies to ascertain the relative competitiveness of a particular industry. Using this index as a lagging indicator in the monthly format we have studied could help to detect anomalous behavior by a particular or small set of users on the network. Additionally, we study indicators related to the speed of movement of a user based on the physical location of their current and previous logins. This data can be ascertained from the IP addresses of the users, and is likely very similar to the fraud detection schemes regularly utilized by credit card networks to detect anomalous activity. In future work we would look to nd a way to combine these indicators for use as an internal fraud detection system.

  3. Detecting Target Data in Network Traffic

    Science.gov (United States)

    2017-03-01

    perimeter around a network and create a single point of entry where security policies can be enforced and auditing can be performed [16]. Security...that we used in this thesis. 2.4.1 bulk_extractor Bulk_extractor is a forensic analysis tool designed for directly extracting artifacts of forensic ...educated guess whether or not the file is there. Shields et al. explains that they need other forensics tools to ensure that all forensic evidence is used

  4. Revisiting Anomaly-based Network Intrusion Detection Systems

    NARCIS (Netherlands)

    Bolzoni, D.

    2009-01-01

    Intrusion detection systems (IDSs) are well-known and widely-deployed security tools to detect cyber-attacks and malicious activities in computer systems and networks. A signature-based IDS works similar to anti-virus software. It employs a signature database of known attacks, and a successful match

  5. Approaches in anomaly-based network intrusion detection systems

    NARCIS (Netherlands)

    Bolzoni, D.; Etalle, S.; Di Pietro, R.; Mancini, L.V.

    2008-01-01

    Anomaly-based network intrusion detection systems (NIDSs) can take into consideration packet headers, the payload, or a combination of both. We argue that payload-based approaches are becoming the most effective methods to detect attacks. Nowadays, attacks aim mainly to exploit vulnerabilities at

  6. Approaches in Anomaly-based Network Intrusion Detection Systems

    NARCIS (Netherlands)

    Bolzoni, D.; Etalle, Sandro

    Anomaly-based network intrusion detection systems (NIDSs) can take into consideration packet headers, the payload, or a combination of both. We argue that payload-based approaches are becoming the most effective methods to detect attacks. Nowadays, attacks aim mainly to exploit vulnerabilities at

  7. Generative adversarial networks for anomaly detection in images

    OpenAIRE

    Batiste Ros, Guillem

    2018-01-01

    Anomaly detection is used to identify abnormal observations that don t follow a normal pattern. Inthis work, we use the power of Generative Adversarial Networks in sampling from image distributionsto perform anomaly detection with images and to identify local anomalous segments within thisimages. Also, we explore potential application of this method to support pathological analysis ofbiological tissues

  8. Artificial-neural-network-based failure detection and isolation

    Science.gov (United States)

    Sadok, Mokhtar; Gharsalli, Imed; Alouani, Ali T.

    1998-03-01

    This paper presents the design of a systematic failure detection and isolation system that uses the concept of failure sensitive variables (FSV) and artificial neural networks (ANN). The proposed approach was applied to tube leak detection in a utility boiler system. Results of the experimental testing are presented in the paper.

  9. Wavelet-based higher-order neural networks for mine detection in thermal IR imagery

    Science.gov (United States)

    Baertlein, Brian A.; Liao, Wen-Jiao

    2000-08-01

    An image processing technique is described for the detection of miens in RI imagery. The proposed technique is based on a third-order neural network, which processes the output of a wavelet packet transform. The technique is inherently invariant to changes in signature position, rotation and scaling. The well-known memory limitations that arise with higher-order neural networks are addressed by (1) the data compression capabilities of wavelet packets, (2) protections of the image data into a space of similar triangles, and (3) quantization of that 'triangle space'. Using these techniques, image chips of size 28 by 28, which would require 0(109) neural net weights, are processed by a network having 0(102) weights. ROC curves are presented for mine detection in real and simulated imagery.

  10. The strategic capability of Asian network airlines to compete with low-cost carriers

    OpenAIRE

    Pearson, James; O'Connell, John F.; Pitfield, David; Ryley, Tim

    2015-01-01

    Never before have network airlines been so exposed and vulnerable to low-cost carriers (LCCs). While LCCs had 26.3% of all world seats in 2013, Southeast Asia had 57.7% and South Asia 58.4% – and these figures will only increase. There are many consequences of LCCs on network airlines, including inadequately meeting the expectations of customers, so increasing dissatisfaction, and not offering sufficient value-for-money. Clearly, it is fundamentally important for Asian network airlines to res...

  11. Patch layout generation by detecting feature networks

    KAUST Repository

    Cao, Yuanhao

    2015-02-01

    The patch layout of 3D surfaces reveals the high-level geometric and topological structures. In this paper, we study the patch layout computation by detecting and enclosing feature loops on surfaces. We present a hybrid framework which combines several key ingredients, including feature detection, feature filtering, feature curve extension, patch subdivision and boundary smoothing. Our framework is able to compute patch layouts through concave features as previous approaches, but also able to generate nice layouts through smoothing regions. We demonstrate the effectiveness of our framework by comparing with the state-of-the-art methods.

  12. Quantifying capability of a local seismic network in terms of locations and focal mechanism solutions of weak earthquakes

    Science.gov (United States)

    Fojtíková, Lucia; Kristeková, Miriam; Málek, Jiří; Sokos, Efthimios; Csicsay, Kristián; Zahradník, Jiří

    2016-01-01

    Extension of permanent seismic networks is usually governed by a number of technical, economic, logistic, and other factors. Planned upgrade of the network can be justified by theoretical assessment of the network capability in terms of reliable estimation of the key earthquake parameters (e.g., location and focal mechanisms). It could be useful not only for scientific purposes but also as a concrete proof during the process of acquisition of the funding needed for upgrade and operation of the network. Moreover, the theoretical assessment can also identify the configuration where no improvement can be achieved with additional stations, establishing a tradeoff between the improvement and additional expenses. This paper presents suggestion of a combination of suitable methods and their application to the Little Carpathians local seismic network (Slovakia, Central Europe) monitoring epicentral zone important from the point of seismic hazard. Three configurations of the network are considered: 13 stations existing before 2011, 3 stations already added in 2011, and 7 new planned stations. Theoretical errors of the relative location are estimated by a new method, specifically developed in this paper. The resolvability of focal mechanisms determined by waveform inversion is analyzed by a recent approach based on 6D moment-tensor error ellipsoids. We consider potential seismic events situated anywhere in the studied region, thus enabling "mapping" of the expected errors. Results clearly demonstrate that the network extension remarkably decreases the errors, mainly in the planned 23-station configuration. The already made three-station extension of the network in 2011 allowed for a few real data examples. Free software made available by the authors enables similar application in any other existing or planned networks.

  13. Artificial neural network techniques to improve the ability of optical coherence tomography to detect optic neuritis.

    Science.gov (United States)

    Garcia-Martin, Elena; Herrero, Raquel; Bambo, Maria P; Ara, Jose R; Martin, Jesus; Polo, Vicente; Larrosa, Jose M; Garcia-Feijoo, Julian; Pablo, Luis E

    2015-01-01

    To analyze the ability of Spectralis optical coherence tomography (OCT) to detect multiple sclerosis (MS) and to distinguish MS eyes with antecedent optic neuritis (ON). To analyze the capability of artificial neural network (ANN) techniques to improve the diagnostic precision. MS patients and controls were enrolled (n = 217). OCT was used to determine the 768 retinal nerve fiber layer thicknesses. Sensitivity and specificity were evaluated to test the ability of OCT to discriminate between MS and healthy eyes, and between MS with and without antecedent ON using ANN. Using ANN technique multilayer perceptrons, OCT could detect MS with a sensitivity of 89.3%, a specificity of 87.6%, and a diagnostic precision of 88.5%. Compared with the OCT-provided parameters, the ANN had a better sensitivity-specificity balance. ANN technique improves the capability of Spectralis OCT to detect MS disease and to distinguish MS eyes with or without antecedent ON.

  14. A Hybrid Spectral Clustering and Deep Neural Network Ensemble Algorithm for Intrusion Detection in Sensor Networks.

    Science.gov (United States)

    Ma, Tao; Wang, Fen; Cheng, Jianjun; Yu, Yang; Chen, Xiaoyun

    2016-10-13

    The development of intrusion detection systems (IDS) that are adapted to allow routers and network defence systems to detect malicious network traffic disguised as network protocols or normal access is a critical challenge. This paper proposes a novel approach called SCDNN, which combines spectral clustering (SC) and deep neural network (DNN) algorithms. First, the dataset is divided into k subsets based on sample similarity using cluster centres, as in SC. Next, the distance between data points in a testing set and the training set is measured based on similarity features and is fed into the deep neural network algorithm for intrusion detection. Six KDD-Cup99 and NSL-KDD datasets and a sensor network dataset were employed to test the performance of the model. These experimental results indicate that the SCDNN classifier not only performs better than backpropagation neural network (BPNN), support vector machine (SVM), random forest (RF) and Bayes tree models in detection accuracy and the types of abnormal attacks found. It also provides an effective tool of study and analysis of intrusion detection in large networks.

  15. A Stochastic Model for the Landing Dispersion of Hazard Detection and Avoidance Capable Flight Systems

    Science.gov (United States)

    Witte, L.

    2014-06-01

    To support landing site assessments for HDA-capable flight systems and to facilitate trade studies between the potential HDA architectures versus the yielded probability of safe landing a stochastic landing dispersion model has been developed.

  16. On Event Detection and Localization in Acyclic Flow Networks

    KAUST Repository

    Suresh, Mahima Agumbe

    2013-05-01

    Acyclic flow networks, present in many infrastructures of national importance (e.g., oil and gas and water distribution systems), have been attracting immense research interest. Existing solutions for detecting and locating attacks against these infrastructures have been proven costly and imprecise, particularly when dealing with large-scale distribution systems. In this article, to the best of our knowledge, for the first time, we investigate how mobile sensor networks can be used for optimal event detection and localization in acyclic flow networks. We propose the idea of using sensors that move along the edges of the network and detect events (i.e., attacks). To localize the events, sensors detect proximity to beacons, which are devices with known placement in the network. We formulate the problem of minimizing the cost of monitoring infrastructure (i.e., minimizing the number of sensors and beacons deployed) in a predetermined zone of interest, while ensuring a degree of coverage by sensors and a required accuracy in locating events using beacons. We propose algorithms for solving the aforementioned problem and demonstrate their effectiveness with results obtained from a realistic flow network simulator.

  17. Effects of multi-state links in network community detection

    International Nuclear Information System (INIS)

    Rocco, Claudio M.; Moronta, José; Ramirez-Marquez, José E.; Barker, Kash

    2017-01-01

    A community is defined as a group of nodes of a network that are densely interconnected with each other but only sparsely connected with the rest of the network. The set of communities (i.e., the network partition) and their inter-community links could be derived using special algorithms account for the topology of the network and, in certain cases, the possible weights associated to the links. In general, the set of weights represents some characteristic as capacity, flow and reliability, among others. The effects of considering weights could be translated to obtain a different partition. In many real situations, particularly when modeling infrastructure systems, networks must be modeled as multi-state networks (e.g., electric power networks). In such networks, each link is characterized by a vector of known random capacities (i.e., the weight on each link could vary according to a known probability distribution). In this paper a simple Monte Carlo approach is proposed to evaluate the effects of multi-state links on community detection as well as on the performance of the network. The approach is illustrated with the topology of an electric power system. - Highlights: • Identify network communities when considering multi-state links. • Identified how effects of considering weights translate to different partition. • Identified importance of Inter-Community Links and changes with respect to community. • Preamble to performing a resilience assessment able to mimic the evolution of the state of each community.

  18. Bayesian network models for error detection in radiotherapy plans

    International Nuclear Information System (INIS)

    Kalet, Alan M; Ford, Eric C; Phillips, Mark H; Gennari, John H

    2015-01-01

    The purpose of this study is to design and develop a probabilistic network for detecting errors in radiotherapy plans for use at the time of initial plan verification. Our group has initiated a multi-pronged approach to reduce these errors. We report on our development of Bayesian models of radiotherapy plans. Bayesian networks consist of joint probability distributions that define the probability of one event, given some set of other known information. Using the networks, we find the probability of obtaining certain radiotherapy parameters, given a set of initial clinical information. A low probability in a propagated network then corresponds to potential errors to be flagged for investigation. To build our networks we first interviewed medical physicists and other domain experts to identify the relevant radiotherapy concepts and their associated interdependencies and to construct a network topology. Next, to populate the network’s conditional probability tables, we used the Hugin Expert software to learn parameter distributions from a subset of de-identified data derived from a radiation oncology based clinical information database system. These data represent 4990 unique prescription cases over a 5 year period. Under test case scenarios with approximately 1.5% introduced error rates, network performance produced areas under the ROC curve of 0.88, 0.98, and 0.89 for the lung, brain and female breast cancer error detection networks, respectively. Comparison of the brain network to human experts performance (AUC of 0.90 ± 0.01) shows the Bayes network model performs better than domain experts under the same test conditions. Our results demonstrate the feasibility and effectiveness of comprehensive probabilistic models as part of decision support systems for improved detection of errors in initial radiotherapy plan verification procedures. (paper)

  19. Detection and classification of power quality disturbances using S-transform and modular neural network

    Energy Technology Data Exchange (ETDEWEB)

    Bhende, C.N.; Mishra, S.; Panigrahi, B.K. [Department of Electrical Engineering, Indian Institute of Technology, New Delhi 110016 (India)

    2008-01-15

    This paper presents an S-transform based modular neural network (NN) classifier for recognition of power quality disturbances. The excellent time - frequency resolution characteristics of the S-transform makes it an attractive candidate for the analysis of power quality (PQ) disturbances under noisy condition and has the ability to detect the disturbance correctly. On the other hand, the performance of wavelet transform (WT) degrades while detecting and localizing the disturbances in the presence of noise. Features extracted by using the S-transform are applied to a modular NN for automatic classification of the PQ disturbances that solves a relatively complex problem by decomposing it into simpler subtasks. Modularity of neural network provides better classification, model complexity reduction and better learning capability, etc. Eleven types of PQ disturbances are considered for the classification. The simulation results show that the combination of the S-transform and a modular NN can effectively detect and classify different power quality disturbances. (author)

  20. Detecting groups of similar components in complex networks

    International Nuclear Information System (INIS)

    Wang Jiao; Lai, C-H

    2008-01-01

    We study how to detect groups in a complex network each of which consists of component nodes sharing a similar connection pattern. Based on the mixture models and the exploratory analysis set up by Newman and Leicht (2007 Proc. Natl. Acad. Sci. USA 104 9564), we develop an algorithm that is applicable to a network with any degree distribution. The partition of a network suggested by this algorithm also applies to its complementary network. In general, groups of similar components are not necessarily identical with the communities in a community network; thus partitioning a network into groups of similar components provides additional information of the network structure. The proposed algorithm can also be used for community detection when the groups and the communities overlap. By introducing a tunable parameter that controls the involved effects of the heterogeneity, we can also investigate conveniently how the group structure can be coupled with the heterogeneity characteristics. In particular, an interesting example shows a group partition can evolve into a community partition in some situations when the involved heterogeneity effects are tuned. The extension of this algorithm to weighted networks is discussed as well.

  1. Cooperative Detection for Primary User in Cognitive Radio Networks

    Directory of Open Access Journals (Sweden)

    Zhu Jia

    2009-01-01

    Full Text Available We propose two novel cooperative detection schemes based on the AF (Amplify and Forward and DF (Decode and Forward protocols to achieve spatial diversity gains for cognitive radio networks, which are referred to as the AF-CDS, (AF-based Cooperative Detection Scheme and DF-CDS (DF-based Cooperative Detection Scheme, respectively. Closed-form expressions of detection probabilities for the noncooperation scheme, AND-CDS (AND-based Cooperative Detection Scheme, AF-CDS and DF-CDS, are derived over Rayleigh fading channels. Also, we analyze the overall agility for the proposed cooperative detection schemes and show that our schemes can further reduce the detection time. In addition, we compare the DF-CDS with the AF-CDS in terms of detection probability and agility gain, depicting the advantage of DF-CDS at low SNR region and high false alarm probability region.

  2. Using new edges for anomaly detection in computer networks

    Science.gov (United States)

    Neil, Joshua Charles

    2015-05-19

    Creation of new edges in a network may be used as an indication of a potential attack on the network. Historical data of a frequency with which nodes in a network create and receive new edges may be analyzed. Baseline models of behavior among the edges in the network may be established based on the analysis of the historical data. A new edge that deviates from a respective baseline model by more than a predetermined threshold during a time window may be detected. The new edge may be flagged as potentially anomalous when the deviation from the respective baseline model is detected. Probabilities for both new and existing edges may be obtained for all edges in a path or other subgraph. The probabilities may then be combined to obtain a score for the path or other subgraph. A threshold may be obtained by calculating an empirical distribution of the scores under historical conditions.

  3. A Robust Optimization Based Energy-Aware Virtual Network Function Placement Proposal for Small Cell 5G Networks with Mobile Edge Computing Capabilities

    Directory of Open Access Journals (Sweden)

    Bego Blanco

    2017-01-01

    Full Text Available In the context of cloud-enabled 5G radio access networks with network function virtualization capabilities, we focus on the virtual network function placement problem for a multitenant cluster of small cells that provide mobile edge computing services. Under an emerging distributed network architecture and hardware infrastructure, we employ cloud-enabled small cells that integrate microservers for virtualization execution, equipped with additional hardware appliances. We develop an energy-aware placement solution using a robust optimization approach based on service demand uncertainty in order to minimize the power consumption in the system constrained by network service latency requirements and infrastructure terms. Then, we discuss the results of the proposed placement mechanism in 5G scenarios that combine several service flavours and robust protection values. Once the impact of the service flavour and robust protection on the global power consumption of the system is analyzed, numerical results indicate that our proposal succeeds in efficiently placing the virtual network functions that compose the network services in the available hardware infrastructure while fulfilling service constraints.

  4. A study on efficient detection of network-based IP spoofing DDoS and malware-infected Systems.

    Science.gov (United States)

    Seo, Jung Woo; Lee, Sang Jin

    2016-01-01

    Large-scale network environments require effective detection and response methods against DDoS attacks. Depending on the advancement of IT infrastructure such as the server or network equipment, DDoS attack traffic arising from a few malware-infected systems capable of crippling the organization's internal network has become a significant threat. This study calculates the frequency of network-based packet attributes and analyzes the anomalies of the attributes in order to detect IP-spoofed DDoS attacks. Also, a method is proposed for the effective detection of malware infection systems triggering IP-spoofed DDoS attacks on an edge network. Detection accuracy and performance of the collected real-time traffic on a core network is analyzed thru the use of the proposed algorithm, and a prototype was developed to evaluate the performance of the algorithm. As a result, DDoS attacks on the internal network were detected in real-time and whether or not IP addresses were spoofed was confirmed. Detecting hosts infected by malware in real-time allowed the execution of intrusion responses before stoppage of the internal network caused by large-scale attack traffic.

  5. Fuzzy Based Advanced Hybrid Intrusion Detection System to Detect Malicious Nodes in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Rupinder Singh

    2017-01-01

    Full Text Available In this paper, an Advanced Hybrid Intrusion Detection System (AHIDS that automatically detects the WSNs attacks is proposed. AHIDS makes use of cluster-based architecture with enhanced LEACH protocol that intends to reduce the level of energy consumption by the sensor nodes. AHIDS uses anomaly detection and misuse detection based on fuzzy rule sets along with the Multilayer Perceptron Neural Network. The Feed Forward Neural Network along with the Backpropagation Neural Network are utilized to integrate the detection results and indicate the different types of attackers (i.e., Sybil attack, wormhole attack, and hello flood attack. For detection of Sybil attack, Advanced Sybil Attack Detection Algorithm is developed while the detection of wormhole attack is done by Wormhole Resistant Hybrid Technique. The detection of hello flood attack is done by using signal strength and distance. An experimental analysis is carried out in a set of nodes; 13.33% of the nodes are determined as misbehaving nodes, which classified attackers along with a detection rate of the true positive rate and false positive rate. Sybil attack is detected at a rate of 99,40%; hello flood attack has a detection rate of 98, 20%; and wormhole attack has a detection rate of 99, 20%.

  6. Analysis of Traffic Engineering capabilities for SDN-based Data Center Networks

    DEFF Research Database (Denmark)

    Pilimon, Artur; Kentis, Angelos Mimidis; Ruepp, Sarah Renée

    2018-01-01

    In the recent years more and more existing services have moved from local execution environments into the cloud. In addition, new cloud-based services are emerging, which are characterized by very stringent delay requirements. This trend puts a stress in the existing monolithic architecture of Data...... techniques focusing on their limitations. Then, it highlights the benefits of incorporating the Software Defined Networking (SDN) paradigm to address these limitations. Furthermore, it illustrates two methodologies and addresses the scalability aspect of DCN-oriented TE, network and service testing...

  7. Detecting and evaluating communities in complex human and biological networks

    Science.gov (United States)

    Morrison, Greg; Mahadevan, L.

    2012-02-01

    We develop a simple method for detecting the community structure in a network can by utilizing a measure of closeness between nodes. This approach readily leads to a method of coarse graining the network, which allows the detection of the natural hierarchy (or hierarchies) of community structure without appealing to an unknown resolution parameter. The closeness measure can also be used to evaluate the robustness of an individual node's assignment to its community (rather than evaluating only the quality of the global structure). Each of these methods in community detection and evaluation are illustrated using a variety of real world networks of either biological or sociological importance and illustrate the power and flexibility of the approach.

  8. RMOD: a tool for regulatory motif detection in signaling network.

    Directory of Open Access Journals (Sweden)

    Jinki Kim

    Full Text Available Regulatory motifs are patterns of activation and inhibition that appear repeatedly in various signaling networks and that show specific regulatory properties. However, the network structures of regulatory motifs are highly diverse and complex, rendering their identification difficult. Here, we present a RMOD, a web-based system for the identification of regulatory motifs and their properties in signaling networks. RMOD finds various network structures of regulatory motifs by compressing the signaling network and detecting the compressed forms of regulatory motifs. To apply it into a large-scale signaling network, it adopts a new subgraph search algorithm using a novel data structure called path-tree, which is a tree structure composed of isomorphic graphs of query regulatory motifs. This algorithm was evaluated using various sizes of signaling networks generated from the integration of various human signaling pathways and it showed that the speed and scalability of this algorithm outperforms those of other algorithms. RMOD includes interactive analysis and auxiliary tools that make it possible to manipulate the whole processes from building signaling network and query regulatory motifs to analyzing regulatory motifs with graphical illustration and summarized descriptions. As a result, RMOD provides an integrated view of the regulatory motifs and mechanism underlying their regulatory motif activities within the signaling network. RMOD is freely accessible online at the following URL: http://pks.kaist.ac.kr/rmod.

  9. DeepMitosis: Mitosis detection via deep detection, verification and segmentation networks.

    Science.gov (United States)

    Li, Chao; Wang, Xinggang; Liu, Wenyu; Latecki, Longin Jan

    2018-04-01

    Mitotic count is a critical predictor of tumor aggressiveness in the breast cancer diagnosis. Nowadays mitosis counting is mainly performed by pathologists manually, which is extremely arduous and time-consuming. In this paper, we propose an accurate method for detecting the mitotic cells from histopathological slides using a novel multi-stage deep learning framework. Our method consists of a deep segmentation network for generating mitosis region when only a weak label is given (i.e., only the centroid pixel of mitosis is annotated), an elaborately designed deep detection network for localizing mitosis by using contextual region information, and a deep verification network for improving detection accuracy by removing false positives. We validate the proposed deep learning method on two widely used Mitosis Detection in Breast Cancer Histological Images (MITOSIS) datasets. Experimental results show that we can achieve the highest F-score on the MITOSIS dataset from ICPR 2012 grand challenge merely using the deep detection network. For the ICPR 2014 MITOSIS dataset that only provides the centroid location of mitosis, we employ the segmentation model to estimate the bounding box annotation for training the deep detection network. We also apply the verification model to eliminate some false positives produced from the detection model. By fusing scores of the detection and verification models, we achieve the state-of-the-art results. Moreover, our method is very fast with GPU computing, which makes it feasible for clinical practice. Copyright © 2018 Elsevier B.V. All rights reserved.

  10. Vehicle Detection in Aerial Images Based on Region Convolutional Neural Networks and Hard Negative Example Mining.

    Science.gov (United States)

    Tang, Tianyu; Zhou, Shilin; Deng, Zhipeng; Zou, Huanxin; Lei, Lin

    2017-02-10

    Detecting vehicles in aerial imagery plays an important role in a wide range of applications. The current vehicle detection methods are mostly based on sliding-window search and handcrafted or shallow-learning-based features, having limited description capability and heavy computational costs. Recently, due to the powerful feature representations, region convolutional neural networks (CNN) based detection methods have achieved state-of-the-art performance in computer vision, especially Faster R-CNN. However, directly using it for vehicle detection in aerial images has many limitations: (1) region proposal network (RPN) in Faster R-CNN has poor performance for accurately locating small-sized vehicles, due to the relatively coarse feature maps; and (2) the classifier after RPN cannot distinguish vehicles and complex backgrounds well. In this study, an improved detection method based on Faster R-CNN is proposed in order to accomplish the two challenges mentioned above. Firstly, to improve the recall, we employ a hyper region proposal network (HRPN) to extract vehicle-like targets with a combination of hierarchical feature maps. Then, we replace the classifier after RPN by a cascade of boosted classifiers to verify the candidate regions, aiming at reducing false detection by negative example mining. We evaluate our method on the Munich vehicle dataset and the collected vehicle dataset, with improvements in accuracy and robustness compared to existing methods.

  11. Fiber Bragg Grating Sensor for Fault Detection in Radial and Network Transmission Lines

    Directory of Open Access Journals (Sweden)

    Mehdi Shadaram

    2010-10-01

    Full Text Available In this paper, a fiber optic based sensor capable of fault detection in both radial and network overhead transmission power line systems is investigated. Bragg wavelength shift is used to measure the fault current and detect fault in power systems. Magnetic fields generated by currents in the overhead transmission lines cause a strain in magnetostrictive material which is then detected by Fiber Bragg Grating (FBG. The Fiber Bragg interrogator senses the reflected FBG signals, and the Bragg wavelength shift is calculated and the signals are processed. A broadband light source in the control room scans the shift in the reflected signal. Any surge in the magnetic field relates to an increased fault current at a certain location. Also, fault location can be precisely defined with an artificial neural network (ANN algorithm. This algorithm can be easily coordinated with other protective devices. It is shown that the faults in the overhead transmission line cause a detectable wavelength shift on the reflected signal of FBG and can be used to detect and classify different kind of faults. The proposed method has been extensively tested by simulation and results confirm that the proposed scheme is able to detect different kinds of fault in both radial and network system.

  12. Anomaly detection in wide area network mesh using two machine learning anomaly detection algorithms

    OpenAIRE

    Zhang, James; Vukotic, Ilija; Gardner, Robert

    2018-01-01

    Anomaly detection is the practice of identifying items or events that do not conform to an expected behavior or do not correlate with other items in a dataset. It has previously been applied to areas such as intrusion detection, system health monitoring, and fraud detection in credit card transactions. In this paper, we describe a new method for detecting anomalous behavior over network performance data, gathered by perfSONAR, using two machine learning algorithms: Boosted Decision Trees (BDT...

  13. Community structures and role detection in music networks

    Science.gov (United States)

    Teitelbaum, T.; Balenzuela, P.; Cano, P.; Buldú, Javier M.

    2008-12-01

    We analyze the existence of community structures in two different social networks using data obtained from similarity and collaborative features between musical artists. Our analysis reveals some characteristic organizational patterns and provides information about the driving forces behind the growth of the networks. In the similarity network, we find a strong correlation between clusters of artists and musical genres. On the other hand, the collaboration network shows two different kinds of communities: rather small structures related to music bands and geographic zones, and much bigger communities built upon collaborative clusters with a high number of participants related through the period the artists were active. Finally, we detect the leading artists inside their corresponding communities and analyze their roles in the network by looking at a few topological properties of the nodes.

  14. STRAY DOG DETECTION IN WIRED CAMERA NETWORK

    Directory of Open Access Journals (Sweden)

    C. Prashanth

    2013-08-01

    Full Text Available Existing surveillance systems impose high level of security on humans but lacks attention on animals. Stray dogs could be used as an alternative to humans to carry explosive material. It is therefore imperative to ensure the detection of stray dogs for necessary corrective action. In this paper, a novel composite approach to detect the presence of stray dogs is proposed. The captured frame from the surveillance camera is initially pre-processed using Gaussian filter to remove noise. The foreground object of interest is extracted utilizing ViBe algorithm. Histogram of Oriented Gradients (HOG algorithm is used as the shape descriptor which derives the shape and size information of the extracted foreground object. Finally, stray dogs are classified from humans using a polynomial Support Vector Machine (SVM of order 3. The proposed composite approach is simulated in MATLAB and OpenCV. Further it is validated with real time video feeds taken from an existing surveillance system. From the results obtained, it is found that a classification accuracy of about 96% is achieved. This encourages the utilization of the proposed composite algorithm in real time surveillance systems.

  15. Online fouling detection in electrical circulation heaters using neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Lalot, S. [M.E.T.I.E.R., Longuenesse Cedex (France); Universite de Valenciennes (France). LME; Lecoeuche, S. [M.E.T.I.E.R., Longuenesse Cedex (France); Universite de Lille (France). Laboratoire 13D

    2003-06-01

    Here is presented a method that is able to detect fouling during the service of a circulation electrical heater. The neural based technique is divided in two major steps: identification and classification. Each step uses a neural network, the connection weights of the first one being the inputs of the second network. Each step is detailed and the main characteristics and abilities of the two neural networks are given. It is shown that the method is able to discriminate fouling from viscosity modification that would lead to the same type of effect on the total heat transfer coefficient. (author)

  16. Applied network security monitoring collection, detection, and analysis

    CERN Document Server

    Sanders, Chris

    2013-01-01

    Applied Network Security Monitoring is the essential guide to becoming an NSM analyst from the ground up. This book takes a fundamental approach to NSM, complete with dozens of real-world examples that teach you the key concepts of NSM. Network security monitoring is based on the principle that prevention eventually fails. In the current threat landscape, no matter how much you try, motivated attackers will eventually find their way into your network. At that point, it is your ability to detect and respond to that intrusion that can be the difference between a small incident and a major di

  17. Module detection in complex networks using integer optimisation

    Directory of Open Access Journals (Sweden)

    Tsoka Sophia

    2010-11-01

    Full Text Available Abstract Background The detection of modules or community structure is widely used to reveal the underlying properties of complex networks in biology, as well as physical and social sciences. Since the adoption of modularity as a measure of network topological properties, several methodologies for the discovery of community structure based on modularity maximisation have been developed. However, satisfactory partitions of large graphs with modest computational resources are particularly challenging due to the NP-hard nature of the related optimisation problem. Furthermore, it has been suggested that optimising the modularity metric can reach a resolution limit whereby the algorithm fails to detect smaller communities than a specific size in large networks. Results We present a novel solution approach to identify community structure in large complex networks and address resolution limitations in module detection. The proposed algorithm employs modularity to express network community structure and it is based on mixed integer optimisation models. The solution procedure is extended through an iterative procedure to diminish effects that tend to agglomerate smaller modules (resolution limitations. Conclusions A comprehensive comparative analysis of methodologies for module detection based on modularity maximisation shows that our approach outperforms previously reported methods. Furthermore, in contrast to previous reports, we propose a strategy to handle resolution limitations in modularity maximisation. Overall, we illustrate ways to improve existing methodologies for community structure identification so as to increase its efficiency and applicability.

  18. Stochastic fluctuations and the detectability limit of network communities.

    Science.gov (United States)

    Floretta, Lucio; Liechti, Jonas; Flammini, Alessandro; De Los Rios, Paolo

    2013-12-01

    We have analyzed the detectability limits of network communities in the framework of the popular Girvan and Newman benchmark. By carefully taking into account the inevitable stochastic fluctuations that affect the construction of each and every instance of the benchmark, we come to the conclusion that the native, putative partition of the network is completely lost even before the in-degree/out-degree ratio becomes equal to that of a structureless Erdös-Rényi network. We develop a simple iterative scheme, analytically well described by an infinite branching process, to provide an estimate of the true detectability limit. Using various algorithms based on modularity optimization, we show that all of them behave (semiquantitatively) in the same way, with the same functional form of the detectability threshold as a function of the network parameters. Because the same behavior has also been found by further modularity-optimization methods and for methods based on different heuristics implementations, we conclude that indeed a correct definition of the detectability limit must take into account the stochastic fluctuations of the network construction.

  19. Adaptive filtering for hidden node detection and tracking in networks.

    Science.gov (United States)

    Hamilton, Franz; Setzer, Beverly; Chavez, Sergio; Tran, Hien; Lloyd, Alun L

    2017-07-01

    The identification of network connectivity from noisy time series is of great interest in the study of network dynamics. This connectivity estimation problem becomes more complicated when we consider the possibility of hidden nodes within the network. These hidden nodes act as unknown drivers on our network and their presence can lead to the identification of false connections, resulting in incorrect network inference. Detecting the parts of the network they are acting on is thus critical. Here, we propose a novel method for hidden node detection based on an adaptive filtering framework with specific application to neuronal networks. We consider the hidden node as a problem of missing variables when model fitting and show that the estimated system noise covariance provided by the adaptive filter can be used to localize the influence of the hidden nodes and distinguish the effects of different hidden nodes. Additionally, we show that the sequential nature of our algorithm allows for tracking changes in the hidden node influence over time.

  20. Neuronal synchrony detection on single-electron neural networks

    International Nuclear Information System (INIS)

    Oya, Takahide; Asai, Tetsuya; Kagaya, Ryo; Hirose, Tetsuya; Amemiya, Yoshihito

    2006-01-01

    Synchrony detection between burst and non-burst spikes is known to be one functional example of depressing synapses. Kanazawa et al. demonstrated synchrony detection with MOS depressing synapse circuits. They found that the performance of a network with depressing synapses that discriminates between burst and random input spikes increases non-monotonically as the static device mismatch is increased. We designed a single-electron depressing synapse and constructed the same network as in Kanazawa's study to develop noise-tolerant single-electron circuits. We examined the temperature characteristics and explored possible architecture that enables single-electron circuits to operate at T > 0 K

  1. Salient regions detection using convolutional neural networks and color volume

    Science.gov (United States)

    Liu, Guang-Hai; Hou, Yingkun

    2018-03-01

    Convolutional neural network is an important technique in machine learning, pattern recognition and image processing. In order to reduce the computational burden and extend the classical LeNet-5 model to the field of saliency detection, we propose a simple and novel computing model based on LeNet-5 network. In the proposed model, hue, saturation and intensity are utilized to extract depth cues, and then we integrate depth cues and color volume to saliency detection following the basic structure of the feature integration theory. Experimental results show that the proposed computing model outperforms some existing state-of-the-art methods on MSRA1000 and ECSSD datasets.

  2. Fast Detection Method in Cooperative Cognitive Radio Networks

    Directory of Open Access Journals (Sweden)

    Zhengyi Li

    2010-01-01

    Full Text Available Cognitive Radio (CR technology improves the utilization of spectrum highly via opportunistic spectrum sharing, which requests fast detection as the spectrum utilization is dynamic. Taking into consideration the characteristic of wireless channels, we propose a fast detection scheme for a cooperative cognitive radio network, which consists of multiple CRs and a central control office. Specifically, each CR makes individual detection decision using the sequential probability ratio test combined with Neyman Pearson detection with respect to a specific observation window length. The proposed method upper bounds the detection delay. In addition, a weighted K out of N fusion rule is also proposed for the central control office to reach fast global decision based on the information collected from CRs, with more weights assigned for CRs with good channel conditions. Simulation results show that the proposed scheme can achieve fast detection while maintaining the detection accuracy.

  3. Android malware detection based on evolutionary super-network

    Science.gov (United States)

    Yan, Haisheng; Peng, Lingling

    2018-04-01

    In the paper, an android malware detection method based on evolutionary super-network is proposed in order to improve the precision of android malware detection. Chi square statistics method is used for selecting characteristics on the basis of analyzing android authority. Boolean weighting is utilized for calculating characteristic weight. Processed characteristic vector is regarded as the system training set and test set; hyper edge alternative strategy is used for training super-network classification model, thereby classifying test set characteristic vectors, and it is compared with traditional classification algorithm. The results show that the detection method proposed in the paper is close to or better than traditional classification algorithm. The proposed method belongs to an effective Android malware detection means.

  4. ANOMALY DETECTION IN NETWORKING USING HYBRID ARTIFICIAL IMMUNE ALGORITHM

    Directory of Open Access Journals (Sweden)

    D. Amutha Guka

    2012-01-01

    Full Text Available Especially in today’s network scenario, when computers are interconnected through internet, security of an information system is very important issue. Because no system can be absolutely secure, the timely and accurate detection of anomalies is necessary. The main aim of this research paper is to improve the anomaly detection by using Hybrid Artificial Immune Algorithm (HAIA which is based on Artificial Immune Systems (AIS and Genetic Algorithm (GA. In this research work, HAIA approach is used to develop Network Anomaly Detection System (NADS. The detector set is generated by using GA and the anomalies are identified using Negative Selection Algorithm (NSA which is based on AIS. The HAIA algorithm is tested with KDD Cup 99 benchmark dataset. The detection rate is used to measure the effectiveness of the NADS. The results and consistency of the HAIA are compared with earlier approaches and the results are presented. The proposed algorithm gives best results when compared to the earlier approaches.

  5. Inter-domain networking innovation on steroids: empowering ixps with SDN capabilities

    KAUST Repository

    Chiesa, Marco

    2016-10-14

    While innovation in inter-domain routing has remained stagnant for over a decade, Internet exchange points (IXPs) are consolidating their role as economically advantageous interconnection points for reducing path latencies and exchanging ever increasing amounts of traffic. As such, IXPs appear as a natural place to foster network innovation and assess the benefits of SDN, a recent technological trend that has already boosted innovation within data center networks. In this article, we give a comprehensive overview of use cases for SDN at IXPs, which leverage the superior vantage point of an IXP to introduce advanced features like load balancing and DDoS mitigation. We discuss the benefits of SDN solutions by analyzing real-world data from one of the largest IXPs. We also leverage insights into IXP operations to shape benefits not only for members but also for operators.

  6. LSTM-Based Hierarchical Denoising Network for Android Malware Detection

    OpenAIRE

    Yan, Jinpei; Qi, Yong; Rao, Qifan

    2018-01-01

    Mobile security is an important issue on Android platform. Most malware detection methods based on machine learning models heavily rely on expert knowledge for manual feature engineering, which are still difficult to fully describe malwares. In this paper, we present LSTM-based hierarchical denoise network (HDN), a novel static Android malware detection method which uses LSTM to directly learn from the raw opcode sequences extracted from decompiled Android files. However, most opcode sequence...

  7. Subsurface Event Detection and Classification Using Wireless Signal Networks

    Directory of Open Access Journals (Sweden)

    Muhannad T. Suleiman

    2012-11-01

    Full Text Available Subsurface environment sensing and monitoring applications such as detection of water intrusion or a landslide, which could significantly change the physical properties of the host soil, can be accomplished using a novel concept, Wireless Signal Networks (WSiNs. The wireless signal networks take advantage of the variations of radio signal strength on the distributed underground sensor nodes of WSiNs to monitor and characterize the sensed area. To characterize subsurface environments for event detection and classification, this paper provides a detailed list and experimental data of soil properties on how radio propagation is affected by soil properties in subsurface communication environments. Experiments demonstrated that calibrated wireless signal strength variations can be used as indicators to sense changes in the subsurface environment. The concept of WSiNs for the subsurface event detection is evaluated with applications such as detection of water intrusion, relative density change, and relative motion using actual underground sensor nodes. To classify geo-events using the measured signal strength as a main indicator of geo-events, we propose a window-based minimum distance classifier based on Bayesian decision theory. The window-based classifier for wireless signal networks has two steps: event detection and event classification. With the event detection, the window-based classifier classifies geo-events on the event occurring regions that are called a classification window. The proposed window-based classification method is evaluated with a water leakage experiment in which the data has been measured in laboratory experiments. In these experiments, the proposed detection and classification method based on wireless signal network can detect and classify subsurface events.

  8. Subsurface event detection and classification using Wireless Signal Networks.

    Science.gov (United States)

    Yoon, Suk-Un; Ghazanfari, Ehsan; Cheng, Liang; Pamukcu, Sibel; Suleiman, Muhannad T

    2012-11-05

    Subsurface environment sensing and monitoring applications such as detection of water intrusion or a landslide, which could significantly change the physical properties of the host soil, can be accomplished using a novel concept, Wireless Signal Networks (WSiNs). The wireless signal networks take advantage of the variations of radio signal strength on the distributed underground sensor nodes of WSiNs to monitor and characterize the sensed area. To characterize subsurface environments for event detection and classification, this paper provides a detailed list and experimental data of soil properties on how radio propagation is affected by soil properties in subsurface communication environments. Experiments demonstrated that calibrated wireless signal strength variations can be used as indicators to sense changes in the subsurface environment. The concept of WSiNs for the subsurface event detection is evaluated with applications such as detection of water intrusion, relative density change, and relative motion using actual underground sensor nodes. To classify geo-events using the measured signal strength as a main indicator of geo-events, we propose a window-based minimum distance classifier based on Bayesian decision theory. The window-based classifier for wireless signal networks has two steps: event detection and event classification. With the event detection, the window-based classifier classifies geo-events on the event occurring regions that are called a classification window. The proposed window-based classification method is evaluated with a water leakage experiment in which the data has been measured in laboratory experiments. In these experiments, the proposed detection and classification method based on wireless signal network can detect and classify subsurface events.

  9. Research on the tourism resource development from the perspective of network capability-Taking Wuxi Huishan Ancient Town as an example

    Science.gov (United States)

    Bao, Yanli; Hua, Hefeng

    2017-03-01

    Network capability is the enterprise's capability to set up, manage, maintain and use a variety of relations between enterprises, and to obtain resources for improving competitiveness. Tourism in China is in a transformation period from sightseeing to leisure and vacation. Scenic spots as well as tourist enterprises can learn from some other enterprises in the process of resource development, and build up its own network relations in order to get resources for their survival and development. Through the effective management of network relations, the performance of resource development will be improved. By analyzing literature on network capability and the case analysis of Wuxi Huishan Ancient Town, the role of network capacity in the tourism resource development is explored and resource development path is built from the perspective of network capability. Finally, the tourism resource development process model based on network capacity is proposed. This model mainly includes setting up network vision, resource identification, resource acquisition, resource utilization and tourism project development. In these steps, network construction, network management and improving network center status are key points.

  10. Case study: development of a SANDF tactical data link network enabling capability

    CSIR Research Space (South Africa)

    Smith, CJ

    2011-11-01

    Full Text Available stream_source_info Smith4_2011.pdf.txt stream_content_type text/plain stream_size 24788 Content-Encoding ISO-8859-1 stream_name Smith4_2011.pdf.txt Content-Type text/plain; charset=ISO-8859-1 Case Study: Development of a...Commander/Operational Personnel Operational Doctrine/Processes C2 and Platform applications Application 1 Application 2 Application 3 Data Data Data Communications Infrastructure DataData DataData Figure 1. TDL Capability Model In relation to Figure 1, Link...

  11. Improving Fault Ride-Through Capability of Variable Speed Wind Turbines in Distribution Networks

    DEFF Research Database (Denmark)

    Mokryani, Geev; Siano, P.; Piccolo, Antonio

    2013-01-01

    In this paper, a fuzzy controller for improving the fault ride-through (FRT) capability of variable speed wind turbines (WTs) equipped with a doubly fed induction generator (DFIG) is presented. DFIGs can be used as reactive power sources to control the voltage at the point of common coupling (PCC......). The controller is designed to compensate for the voltage at the PCC by simultaneously regulating the reactive and active power generated by WTs. The performance of the controller is evaluated in different case studies considering a different number of wind farms in different locations. Simulations carried out...

  12. Detecting atrial fibrillation by deep convolutional neural networks.

    Science.gov (United States)

    Xia, Yong; Wulan, Naren; Wang, Kuanquan; Zhang, Henggui

    2018-02-01

    Atrial fibrillation (AF) is the most common cardiac arrhythmia. The incidence of AF increases with age, causing high risks of stroke and increased morbidity and mortality. Efficient and accurate diagnosis of AF based on the ECG is valuable in clinical settings and remains challenging. In this paper, we proposed a novel method with high reliability and accuracy for AF detection via deep learning. The short-term Fourier transform (STFT) and stationary wavelet transform (SWT) were used to analyze ECG segments to obtain two-dimensional (2-D) matrix input suitable for deep convolutional neural networks. Then, two different deep convolutional neural network models corresponding to STFT output and SWT output were developed. Our new method did not require detection of P or R peaks, nor feature designs for classification, in contrast to existing algorithms. Finally, the performances of the two models were evaluated and compared with those of existing algorithms. Our proposed method demonstrated favorable performances on ECG segments as short as 5 s. The deep convolutional neural network using input generated by STFT, presented a sensitivity of 98.34%, specificity of 98.24% and accuracy of 98.29%. For the deep convolutional neural network using input generated by SWT, a sensitivity of 98.79%, specificity of 97.87% and accuracy of 98.63% was achieved. The proposed method using deep convolutional neural networks shows high sensitivity, specificity and accuracy, and, therefore, is a valuable tool for AF detection. Copyright © 2017 Elsevier Ltd. All rights reserved.

  13. Towards Optimal Event Detection and Localization in Acyclic Flow Networks

    KAUST Repository

    Agumbe Suresh, Mahima

    2012-01-03

    Acyclic flow networks, present in many infrastructures of national importance (e.g., oil & gas and water distribution systems), have been attracting immense research interest. Existing solutions for detecting and locating attacks against these infrastructures, have been proven costly and imprecise, especially when dealing with large scale distribution systems. In this paper, to the best of our knowledge for the first time, we investigate how mobile sensor networks can be used for optimal event detection and localization in acyclic flow networks. Sensor nodes move along the edges of the network and detect events (i.e., attacks) and proximity to beacon nodes with known placement in the network. We formulate the problem of minimizing the cost of monitoring infrastructure (i.e., minimizing the number of sensor and beacon nodes deployed), while ensuring a degree of sensing coverage in a zone of interest and a required accuracy in locating events. We propose algorithms for solving these problems and demonstrate their effectiveness with results obtained from a high fidelity simulator.

  14. Superior Generalization Capability of Hardware-Learing Algorithm Developed for Self-Learning Neuron-MOS Neural Networks

    Science.gov (United States)

    Kondo, Shuhei; Shibata, Tadashi; Ohmi, Tadahiro

    1995-02-01

    We have investigated the learning performance of the hardware backpropagation (HBP) algorithm, a hardware-oriented learning algorithm developed for the self-learning architecture of neural networks constructed using neuron MOS (metal-oxide-semiconductor) transistors. The solution to finding a mirror symmetry axis in a 4×4 binary pixel array was tested by computer simulation based on the HBP algorithm. Despite the inherent restrictions imposed on the hardware-learning algorithm, HBP exhibits equivalent learning performance to that of the original backpropagation (BP) algorithm when all the pertinent parameters are optimized. Very importantly, we have found that HBP has a superior generalization capability over BP; namely, HBP exhibits higher performance in solving problems that the network has not yet learnt.

  15. Including 10-Gigabit-capable Passive Optical Network under End-to-End Generalized Multi-Protocol Label Switching Provisioned Quality of Service

    DEFF Research Database (Denmark)

    Brewka, Lukasz Jerzy; Gavler, Anders; Wessing, Henrik

    2012-01-01

    of the network where quality of service signaling is bridged. This article proposes strategies for generalized multi-protocol label switching control over next emerging passive optical network standard, i.e., the 10-gigabit-capable passive optical network. Node management and resource allocation approaches...... are discussed, and possible issues are raised. The analysis shows that consideration of a 10-gigabit-capable passive optical network as a generalized multi-protocol label switching controlled domain is valid and may advance end-to-end quality of service provisioning for passive optical network based customers.......End-to-end quality of service provisioning is still a challenging task despite many years of research and development in this area. Considering a generalized multi-protocol label switching based core/metro network and resource reservation protocol capable home gateways, it is the access part...

  16. A framework for unsupervised spam detection in social networking sites

    NARCIS (Netherlands)

    Bosma, M.; Meij, E.; Weerkamp, W.

    2012-01-01

    Social networking sites offer users the option to submit user spam reports for a given message, indicating this message is inappropriate. In this paper we present a framework that uses these user spam reports for spam detection. The framework is based on the HITS web link analysis framework and is

  17. Distributed Event Detection in Wireless Sensor Networks for Disaster Management

    NARCIS (Netherlands)

    Bahrepour, M.; Meratnia, Nirvana; Poel, Mannes; Taghikhaki, Zahra; Havinga, Paul J.M.

    2010-01-01

    Recently, wireless sensor networks (WSNs) have become mature enough to go beyond being simple fine-grained continuous monitoring platforms and become one of the enabling technologies for disaster early-warning systems. Event detection functionality of WSNs can be of great help and importance for

  18. Deep convolutional neural networks for detection of rail surface defects

    NARCIS (Netherlands)

    Faghih Roohi, S.; Hajizadeh, S.; Nunez Vicencio, Alfredo; Babuska, R.; De Schutter, B.H.K.; Estevez, Pablo A.; Angelov, Plamen P.; Del Moral Hernandez, Emilio

    2016-01-01

    In this paper, we propose a deep convolutional neural network solution to the analysis of image data for the detection of rail surface defects. The images are obtained from many hours of automated video recordings. This huge amount of data makes it impossible to manually inspect the images and

  19. Image objects detection based on boosting neural network

    NARCIS (Netherlands)

    Liang, N.; Hegt, J.A.; Mladenov, V.M.

    2010-01-01

    This paper discusses the problem of object area detection of video frames. The goal is to design a pixel accurate detector for grass, which could be used for object adaptive video enhancement. A boosting neural network is used for creating such a detector. The resulted detector uses both textural

  20. Application of Cellular Automata to Detection of Malicious Network Packets

    Science.gov (United States)

    Brown, Robert L.

    2014-01-01

    A problem in computer security is identification of attack signatures in network packets. An attack signature is a pattern of bits that characterizes a particular attack. Because there are many kinds of attacks, there are potentially many attack signatures. Furthermore, attackers may seek to avoid detection by altering the attack mechanism so that…

  1. Expert knowledge for automatic detection of bullies in social networks

    NARCIS (Netherlands)

    Dadvar, M.; Trieschnigg, Rudolf Berend; de Jong, Franciska M.G.

    2013-01-01

    Cyberbullying is a serious social problem in online environments and social networks. Current approaches to tackle this problem are still inadequate for detecting bullying incidents or to flag bullies. In this study we used a multi-criteria evaluation system to obtain a better understanding of

  2. A framework for detecting communities of unbalanced sizes in networks

    Science.gov (United States)

    Žalik, Krista Rizman; Žalik, Borut

    2018-01-01

    Community detection in large networks has been a focus of recent research in many of fields, including biology, physics, social sciences, and computer science. Most community detection methods partition the entire network into communities, groups of nodes that have many connections within communities and few connections between them and do not identify different roles that nodes can have in communities. We propose a community detection model that integrates more different measures that can fast identify communities of different sizes and densities. We use node degree centrality, strong similarity with one node from community, maximal similarity of node to community, compactness of communities and separation between communities. Each measure has its own strength and weakness. Thus, combining different measures can benefit from the strengths of each one and eliminate encountered problems of using an individual measure. We present a fast local expansion algorithm for uncovering communities of different sizes and densities and reveals rich information on input networks. Experimental results show that the proposed algorithm is better or as effective as the other community detection algorithms for both real-world and synthetic networks while it requires less time.

  3. Efficient Mining and Detection of Sequential Intrusion Patterns for Network Intrusion Detection Systems

    Science.gov (United States)

    Shyu, Mei-Ling; Huang, Zifang; Luo, Hongli

    In recent years, pervasive computing infrastructures have greatly improved the interaction between human and system. As we put more reliance on these computing infrastructures, we also face threats of network intrusion and/or any new forms of undesirable IT-based activities. Hence, network security has become an extremely important issue, which is closely connected with homeland security, business transactions, and people's daily life. Accurate and efficient intrusion detection technologies are required to safeguard the network systems and the critical information transmitted in the network systems. In this chapter, a novel network intrusion detection framework for mining and detecting sequential intrusion patterns is proposed. The proposed framework consists of a Collateral Representative Subspace Projection Modeling (C-RSPM) component for supervised classification, and an inter-transactional association rule mining method based on Layer Divided Modeling (LDM) for temporal pattern analysis. Experiments on the KDD99 data set and the traffic data set generated by a private LAN testbed show promising results with high detection rates, low processing time, and low false alarm rates in mining and detecting sequential intrusion detections.

  4. A soft robot capable of 2D mobility and self-sensing for obstacle detection and avoidance

    Science.gov (United States)

    Qin, Lei; Tang, Yucheng; Gupta, Ujjaval; Zhu, Jian

    2018-04-01

    Soft robots have shown great potential for surveillance applications due to their interesting attributes including inherent flexibility, extreme adaptability, and excellent ability to move in confined spaces. High mobility combined with the sensing systems that can detect obstacles plays a significant role in performing surveillance tasks. Extensive studies have been conducted on movement mechanisms of traditional hard-bodied robots to increase their mobility. However, there are limited efforts in the literature to explore the mobility of soft robots. In addition, little attempt has been made to study the obstacle-detection capability of a soft mobile robot. In this paper, we develop a soft mobile robot capable of high mobility and self-sensing for obstacle detection and avoidance. This robot, consisting of a dielectric elastomer actuator as the robot body and four electroadhesion actuators as the robot feet, can generate 2D mobility, i.e. translations and turning in a 2D plane, by programming the actuation sequence of the robot body and feet. Furthermore, we develop a self-sensing method which models the robot body as a deformable capacitor. By measuring the real-time capacitance of the robot body, the robot can detect an obstacle when the peak capacitance drops suddenly. This sensing method utilizes the robot body itself instead of external sensors to achieve detection of obstacles, which greatly reduces the weight and complexity of the robot system. The 2D mobility and self-sensing capability ensure the success of obstacle detection and avoidance, which paves the way for the development of lightweight and intelligent soft mobile robots.

  5. Bridge damage detection using spatiotemporal patterns extracted from dense sensor network

    International Nuclear Information System (INIS)

    Liu, Chao; Sarkar, Soumik; Gong, Yongqiang; Laflamme, Simon; Phares, Brent

    2017-01-01

    The alarmingly degrading state of transportation infrastructures combined with their key societal and economic importance calls for automatic condition assessment methods to facilitate smart management of maintenance and repairs. With the advent of ubiquitous sensing and communication capabilities, scalable data-driven approaches is of great interest, as it can utilize large volume of streaming data without requiring detailed physical models that can be inaccurate and computationally expensive to run. Properly designed, a data-driven methodology could enable fast and automatic evaluation of infrastructures, discovery of causal dependencies among various sub-system dynamic responses, and decision making with uncertainties and lack of labeled data. In this work, a spatiotemporal pattern network (STPN) strategy built on symbolic dynamic filtering (SDF) is proposed to explore spatiotemporal behaviors in a bridge network. Data from strain gauges installed on two bridges are generated using finite element simulation for three types of sensor networks from a density perspective (dense, nominal, sparse). Causal relationships among spatially distributed strain data streams are extracted and analyzed for vehicle identification and detection, and for localization of structural degradation in bridges. Multiple case studies show significant capabilities of the proposed approach in: (i) capturing spatiotemporal features to discover causality between bridges (geographically close), (ii) robustness to noise in data for feature extraction, (iii) detecting and localizing damage via comparison of bridge responses to similar vehicle loads, and (iv) implementing real-time health monitoring and decision making work flow for bridge networks. Also, the results demonstrate increased sensitivity in detecting damages and higher reliability in quantifying the damage level with increase in sensor network density. (paper)

  6. An artifical neural network for detection of simulated dental caries

    Energy Technology Data Exchange (ETDEWEB)

    Kositbowornchai, S. [Khon Kaen Univ. (Thailand). Dept. of Oral Diagnosis; Siriteptawee, S.; Plermkamon, S.; Bureerat, S. [Khon Kaen Univ. (Thailand). Dept. of Mechanical Engineering; Chetchotsak, D. [Khon Kaen Univ. (Thailand). Dept. of Industrial Engineering

    2006-08-15

    Objects: A neural network was developed to diagnose artificial dental caries using images from a charged-coupled device (CCD)camera and intra-oral digital radiography. The diagnostic performance of this neural network was evaluated against a gold standard. Materials and methods: The neural network design was the Learning Vector Quantization (LVQ) used to classify a tooth surface as sound or as having dental caries. The depth of the dental caries was indicated on a graphic user interface (GUI) screen developed by Matlab programming. Forty-nine images of both sound and simulated dental caries, derived from a CCD camera and by digital radiography, were used to 'train' an artificial neural network. After the 'training' process, a separate test-set comprising 322 unseen images was evaluated. Tooth sections and microscopic examinations were used to confirm the actual dental caries status.The performance of neural network was evaluated using diagnostic test. Results: The sensitivity (95%CI)/specificity (95%CI) of dental caries detection by the CCD camera and digital radiography were 0.77(0.68-0.85)/0.85(0.75-0.92) and 0.81(0.72-0.88)/0.93(0.84-0.97), respectively. The accuracy of caries depth-detection by the CCD camera and digital radiography was 58 and 40%, respectively. Conclusions: The model neural network used in this study could be a prototype for caries detection but should be improved for classifying caries depth. Our study suggests an artificial neural network can be trained to make the correct interpretations of dental caries. (orig.)

  7. An artifical neural network for detection of simulated dental caries

    International Nuclear Information System (INIS)

    Kositbowornchai, S.; Siriteptawee, S.; Plermkamon, S.; Bureerat, S.; Chetchotsak, D.

    2006-01-01

    Objects: A neural network was developed to diagnose artificial dental caries using images from a charged-coupled device (CCD)camera and intra-oral digital radiography. The diagnostic performance of this neural network was evaluated against a gold standard. Materials and methods: The neural network design was the Learning Vector Quantization (LVQ) used to classify a tooth surface as sound or as having dental caries. The depth of the dental caries was indicated on a graphic user interface (GUI) screen developed by Matlab programming. Forty-nine images of both sound and simulated dental caries, derived from a CCD camera and by digital radiography, were used to 'train' an artificial neural network. After the 'training' process, a separate test-set comprising 322 unseen images was evaluated. Tooth sections and microscopic examinations were used to confirm the actual dental caries status.The performance of neural network was evaluated using diagnostic test. Results: The sensitivity (95%CI)/specificity (95%CI) of dental caries detection by the CCD camera and digital radiography were 0.77(0.68-0.85)/0.85(0.75-0.92) and 0.81(0.72-0.88)/0.93(0.84-0.97), respectively. The accuracy of caries depth-detection by the CCD camera and digital radiography was 58 and 40%, respectively. Conclusions: The model neural network used in this study could be a prototype for caries detection but should be improved for classifying caries depth. Our study suggests an artificial neural network can be trained to make the correct interpretations of dental caries. (orig.)

  8. Marketing and social networks: a criterion for detecting opinion leaders

    Directory of Open Access Journals (Sweden)

    Arnaldo Mario Litterio

    2017-10-01

    Full Text Available Purpose - The purpose of this paper is to use the practical application of tools provided by social network theory for the detection of potential influencers from the point of view of marketing within online communities. It proposes a method to detect significant actors based on centrality metrics. Design/methodology/approach - A matrix is proposed for the classification of the individuals that integrate a social network based on the combination of eigenvector centrality and betweenness centrality. The model is tested on a Facebook fan page for a sporting event. NodeXL is used to extract and analyze information. Semantic analysis and agent-based simulation are used to test the model. Findings - The proposed model is effective in detecting actors with the potential to efficiently spread a message in relation to the rest of the community, which is achieved from their position within the network. Social network analysis (SNA and the proposed model, in particular, are useful to detect subgroups of components with particular characteristics that are not evident from other analysis methods. Originality/value - This paper approaches the application of SNA to online social communities from an empirical and experimental perspective. Its originality lies in combining information from two individual metrics to understand the phenomenon of influence. Online social networks are gaining relevance and the literature that exists in relation to this subject is still fragmented and incipient. This paper contributes to a better understanding of this phenomenon of networks and the development of better tools to manage it through the proposal of a novel method.

  9. INTRUSION DETECTION PREVENTION SYSTEM (IDPS PADA LOCAL AREA NETWORK (LAN

    Directory of Open Access Journals (Sweden)

    Didit Suhartono

    2015-02-01

    Full Text Available Penelitian ini berjudul “Intrusion Detection Prevention System Local Area Network (LAN” yang bertujuan untuk memproteksi jaringan dari usaha- usaha penyusupan yang dilakukan oleh seorang intruder. Metode yang digunakan pada penelitian ini adalah menggunakan metode kerangka pikir sebagai acuan dari tahap- tahap penelitian yang penulis lakukan. IDS difungsikan sebagai pendeteksi adanya serangan sesuai rule yang ada kemudian pesan peringatan disimpan dalam database dan dikirim via sms kepada seorang network administrator, sedangkan Firewall digunakan sebagai packet filtering dengan cara menentukan security policy yang dinilai penting. Hasilnya adalah ketika IDS memberikanpesan peringatan ketika ada serangan, seorang network administrator dapat memblok adanya serangan tersebut dengan cara manual dengan firewall, ataupun firewall akan memblok sendiri serangan tersebut sesuai dengan security policy yang diterapkan oleh network adminisrator sebelumnya

  10. Cosmic ray antiproton/electron discrimination capability of the CAPRICE silicon-tungsten calorimeter using neural networks

    International Nuclear Information System (INIS)

    Bellotti, R.; Boezio, M.; Castellano, M.; De Marzo, C.; Picozza, P.; Prigiobbe, V.; Sparvoli, R.; Tirocchi, M.

    1996-01-01

    A data analysis based on an artificial neural network classifier is proposed to identify cosmic ray antiprotons detected with the CAPRICE silicon-tungsten imaging calorimeter against electron background in the energy range 1.2-4.0 GeV. A set of new physical variables, describing the events inside the calorimeter on the base of their different patterns, are introduced in order to discriminate between hadronic and electromagnetic showers. The ability of the artificial neural network classifier to perform a careful multidimensional analysis gives the possibility to identify antiprotons with an electron rejection 408±85 (stat) at 95.0±0.2 (stat)% of signal detection efficiency. The high accuracy achieved by this method improves substantially the efficiency in the evaluation of the cosmic ray antiproton spectrum. (orig.)

  11. Real-time multiple networked viewer capability of the DIII-D EC data acquisition system

    International Nuclear Information System (INIS)

    Ponce, D.; Gorelov, I.A.; Chiu, H.K.; Baity, F.W.

    2005-01-01

    A data acquisition system (DAS) which permits real-time viewing by multiple locally networked operators is being implemented for the electron cyclotron (EC) heating and current drive system at DIII-D. The DAS is expected to demonstrate performance equivalent to standalone oscilloscopes. Participation by remote viewers, including throughout the greater DIII-D facility, can also be incorporated. The real-time system uses one computer-controlled DAS per gyrotron. The DAS computers send their data to a central data server using individual and dedicated 200 Mbps fully duplexed Ethernet connections. The server has a dedicated 10 krpm hard drive for each gyrotron DAS. Selected channels can then be reprocessed and distributed to viewers over a standard local area network (LAN). They can also be bridged from the LAN to the internet. Calculations indicate that the hardware will support real-time writing of each channel at full resolution to the server hard drives. The data will be re-sampled for distribution to multiple viewers over the LAN in real-time. The hardware for this system is in place. The software is under development. This paper will present the design details and up-to-date performance metrics of the system

  12. Moving Target Detection and Active Tracking with a Multicamera Network

    Directory of Open Access Journals (Sweden)

    Long Zhao

    2014-01-01

    Full Text Available We propose a systematic framework for Intelligence Video Surveillance System (IVSS with a multicamera network. The proposed framework consists of low-cost static and PTZ cameras, target detection and tracking algorithms, and a low-cost PTZ camera feedback control algorithm based on target information. The target detection and tracking is realized by fixed cameras using a moving target detection and tracking algorithm; the PTZ camera is manoeuvred to actively track the target from the tracking results of the static camera. The experiments are carried out using practical surveillance system data, and the experimental results show that the systematic framework and algorithms presented in this paper are efficient.

  13. Mapping of networks to detect priority zoonoses in Jordan

    Directory of Open Access Journals (Sweden)

    Erin M Sorrell

    2015-10-01

    Full Text Available Early detection of emerging disease events is a priority focus area for cooperative bioengagement programs. Communication and coordination among national disease surveillance and response networks are essential for timely detection and control of a public health event. Although systematic information sharing between the human and animal health sectors can help stakeholders detect and respond to zoonotic diseases rapidly, resource constraints and other barriers often prevent efficient cross-sector reporting. The purpose of this research project was to map the laboratory and surveillance networks currently in place for detecting and reporting priority zoonotic diseases in Jordan in order to identify the nodes of communication, coordination, and decision-making where health and veterinary sectors intersect, and to identify priorities and gaps that limit information-sharing for action. We selected three zoonotic diseases as case studies: highly pathogenic avian influenza (HPAI H5N1, rabies, and brucellosis. Through meetings with government agencies and health officials, and desk research, we mapped each system from the index case through response – including both surveillance and laboratory networks, highlighting both areas of strength and those that would benefit from capacity-building resources. Our major findings indicate informal communication exists across sectors; in the event of emergence of one of the priority zoonoses studied there is effective coordination across the Ministry of Health and Ministry of Agriculture. However, routine formal coordination is lacking. Overall, there is a strong desire and commitment for multi-sectoral coordination in detection and response to zoonoses across public health and veterinary sectors. Our analysis indicates that the networks developed in response to HPAI can and should be leveraged to develop a comprehensive laboratory and surveillance One Health network.

  14. Detecting Cyber-Attacks on Wireless Mobile Networks Using Multicriterion Fuzzy Classifier with Genetic Attribute Selection

    Directory of Open Access Journals (Sweden)

    El-Sayed M. El-Alfy

    2015-01-01

    Full Text Available With the proliferation of wireless and mobile network infrastructures and capabilities, a wide range of exploitable vulnerabilities emerges due to the use of multivendor and multidomain cross-network services for signaling and transport of Internet- and wireless-based data. Consequently, the rates and types of cyber-attacks have grown considerably and current security countermeasures for protecting information and communication may be no longer sufficient. In this paper, we investigate a novel methodology based on multicriterion decision making and fuzzy classification that can provide a viable second-line of defense for mitigating cyber-attacks. The proposed approach has the advantage of dealing with various types and sizes of attributes related to network traffic such as basic packet headers, content, and time. To increase the effectiveness and construct optimal models, we augmented the proposed approach with a genetic attribute selection strategy. This allows efficient and simpler models which can be replicated at various network components to cooperatively detect and report malicious behaviors. Using three datasets covering a variety of network attacks, the performance enhancements due to the proposed approach are manifested in terms of detection errors and model construction times.

  15. BLACK HOLE ATTACK IN AODV & FRIEND FEATURES UNIQUE EXTRACTION TO DESIGN DETECTION ENGINE FOR INTRUSION DETECTION SYSTEM IN MOBILE ADHOC NETWORK

    Directory of Open Access Journals (Sweden)

    HUSAIN SHAHNAWAZ

    2012-10-01

    Full Text Available Ad-hoc network is a collection of nodes that are capable to form dynamically a temporary network without the support of any centralized fixed infrastructure. Since there is no central controller to determine the reliable & secure communication paths in Mobile Adhoc Network, each node in the ad hoc network has to rely on each other in order to forward packets, thus highly cooperative nodes are required to ensure that the initiated data transmission process does not fail. In a mobile ad hoc network (MANET where security is a crucial issue and they are forced to rely on the neighbor node, trust plays an important role that could improve the number of successful data transmission. Larger the number of trusted nodes, higher successful data communication process rates could be expected. In this paper, Black Hole attack is applied in the network, statistics are collected to design intrusion detection engine for MANET Intrusion Detection System (IDS. Feature extraction and rule inductions are applied to find out the accuracy of detection engine by using support vector machine. In this paper True Positive generated by the detection engine is very high and this is a novel approach in the area of Mobile Adhoc Intrusion detection system.

  16. VLSI Design of SVM-Based Seizure Detection System With On-Chip Learning Capability.

    Science.gov (United States)

    Feng, Lichen; Li, Zunchao; Wang, Yuanfa

    2018-02-01

    Portable automatic seizure detection system is very convenient for epilepsy patients to carry. In order to make the system on-chip trainable with high efficiency and attain high detection accuracy, this paper presents a very large scale integration (VLSI) design based on the nonlinear support vector machine (SVM). The proposed design mainly consists of a feature extraction (FE) module and an SVM module. The FE module performs the three-level Daubechies discrete wavelet transform to fit the physiological bands of the electroencephalogram (EEG) signal and extracts the time-frequency domain features reflecting the nonstationary signal properties. The SVM module integrates the modified sequential minimal optimization algorithm with the table-driven-based Gaussian kernel to enable efficient on-chip learning. The presented design is verified on an Altera Cyclone II field-programmable gate array and tested using the two publicly available EEG datasets. Experiment results show that the designed VLSI system improves the detection accuracy and training efficiency.

  17. Investigating SWOT's capabilities to detect meso and submesoscale eddies in the western Mediterranean

    Science.gov (United States)

    Gomez-Navarro, Laura; Pascual, Ananda; Fablet, Ronan; Mason, Evan

    2017-04-01

    The primary oceanographic objective of the future Surface Water Ocean Topography (SWOT) altimetric satellite is to characterize the mesoscale and submesoscale ocean circulation. The aim of this study is to assess the capabilities of SWOT to resolve the meso and submesoscale in the western Mediterranean. With ROMS model data as inputs for the SWOT simulator, pseudo-SWOT data were generated. These data were compared with the original ROMS model data and ADT data from present day altimetric satellites to assess the temporal and spatial resolution of SWOT in the western Mediterranean. We then addressed the removal of the satellite's noise in the pseudo-SWOT data using a Laplacian diffusion. We investigated different parameters of the filter by looking at their impact on the spatial spectra and RMSEs calculated from the simulator outputs. To further assess the satellites capabilities, we derived absolute geostrophic velocities and relative vorticity. Our numerical experiments show that the noise patterns affect the spectral content of the pseudo-SWOT fields below 30 km. The Laplacian diffusion improves the recovery of the spectral signature of the altimetric field, especially down to 20 km. With the help of this filter, we manage to observe small scale oceanic features in pseudo-SWOT data, and in its derived variables.

  18. STICAP: A linear circuit analysis program with stiff systems capability. Volume 1: Theory manual. [network analysis

    Science.gov (United States)

    Cooke, C. H.

    1975-01-01

    STICAP (Stiff Circuit Analysis Program) is a FORTRAN 4 computer program written for the CDC-6400-6600 computer series and SCOPE 3.0 operating system. It provides the circuit analyst a tool for automatically computing the transient responses and frequency responses of large linear time invariant networks, both stiff and nonstiff (algorithms and numerical integration techniques are described). The circuit description and user's program input language is engineer-oriented, making simple the task of using the program. Engineering theories underlying STICAP are examined. A user's manual is included which explains user interaction with the program and gives results of typical circuit design applications. Also, the program structure from a systems programmer's viewpoint is depicted and flow charts and other software documentation are given.

  19. The effect of faulty local detectors on a detection network

    International Nuclear Information System (INIS)

    Mirjalily, G.; Emadi, S.

    2002-01-01

    Distributed detection theory has received increasing attention recently. Development of multiple sensors for signal detection results in improved performance and increased reliability. in a detection network, each local sensor decides locally whether a signal is detected or not. The local decisions are sent to the fusion center, where the final decision is made. In this paper, a theoretic approach is considered to data fusion when one of the sensors is faulty. If the fusion center does not have any knowledge of this fault, the performance of the system is different than its normal performance. The changes in the error probabilities depend on the type of the fault and on the threshold value of the fission center test. We derived some expressions of the changes in the values of error probabilities. For some type of faults, the system false alarm probability increases significantly, whereas for some other faults, the system detection probability decreases significantly. To illustrate the results, a numerical example is also given

  20. Maximum-entropy networks pattern detection, network reconstruction and graph combinatorics

    CERN Document Server

    Squartini, Tiziano

    2017-01-01

    This book is an introduction to maximum-entropy models of random graphs with given topological properties and their applications. Its original contribution is the reformulation of many seemingly different problems in the study of both real networks and graph theory within the unified framework of maximum entropy. Particular emphasis is put on the detection of structural patterns in real networks, on the reconstruction of the properties of networks from partial information, and on the enumeration and sampling of graphs with given properties.  After a first introductory chapter explaining the motivation, focus, aim and message of the book, chapter 2 introduces the formal construction of maximum-entropy ensembles of graphs with local topological constraints. Chapter 3 focuses on the problem of pattern detection in real networks and provides a powerful way to disentangle nontrivial higher-order structural features from those that can be traced back to simpler local constraints. Chapter 4 focuses on the problem o...

  1. Consensus-based methodology for detection communities in multilayered networks

    Science.gov (United States)

    Karimi-Majd, Amir-Mohsen; Fathian, Mohammad; Makrehchi, Masoud

    2018-03-01

    Finding groups of network users who are densely related with each other has emerged as an interesting problem in the area of social network analysis. These groups or so-called communities would be hidden behind the behavior of users. Most studies assume that such behavior could be understood by focusing on user interfaces, their behavioral attributes or a combination of these network layers (i.e., interfaces with their attributes). They also assume that all network layers refer to the same behavior. However, in real-life networks, users' behavior in one layer may differ from their behavior in another one. In order to cope with these issues, this article proposes a consensus-based community detection approach (CBC). CBC finds communities among nodes at each layer, in parallel. Then, the results of layers should be aggregated using a consensus clustering method. This means that different behavior could be detected and used in the analysis. As for other significant advantages, the methodology would be able to handle missing values. Three experiments on real-life and computer-generated datasets have been conducted in order to evaluate the performance of CBC. The results indicate superiority and stability of CBC in comparison to other approaches.

  2. Adaptive multi-resolution Modularity for detecting communities in networks

    Science.gov (United States)

    Chen, Shi; Wang, Zhi-Zhong; Bao, Mei-Hua; Tang, Liang; Zhou, Ji; Xiang, Ju; Li, Jian-Ming; Yi, Chen-He

    2018-02-01

    Community structure is a common topological property of complex networks, which attracted much attention from various fields. Optimizing quality functions for community structures is a kind of popular strategy for community detection, such as Modularity optimization. Here, we introduce a general definition of Modularity, by which several classical (multi-resolution) Modularity can be derived, and then propose a kind of adaptive (multi-resolution) Modularity that can combine the advantages of different Modularity. By applying the Modularity to various synthetic and real-world networks, we study the behaviors of the methods, showing the validity and advantages of the multi-resolution Modularity in community detection. The adaptive Modularity, as a kind of multi-resolution method, can naturally solve the first-type limit of Modularity and detect communities at different scales; it can quicken the disconnecting of communities and delay the breakup of communities in heterogeneous networks; and thus it is expected to generate the stable community structures in networks more effectively and have stronger tolerance against the second-type limit of Modularity.

  3. Distributed detection of communities in complex networks using synthetic coordinates

    International Nuclear Information System (INIS)

    Papadakis, H; Fragopoulou, P; Panagiotakis, C

    2014-01-01

    Various applications like finding Web communities, detecting the structure of social networks, and even analyzing a graph’s structure to uncover Internet attacks are just some of the applications for which community detection is important. In this paper, we propose an algorithm that finds the entire community structure of a network, on the basis of local interactions between neighboring nodes and an unsupervised distributed hierarchical clustering algorithm. The novelty of the proposed approach, named SCCD (standing for synthetic coordinate community detection), lies in the fact that the algorithm is based on the use of Vivaldi synthetic network coordinates computed by a distributed algorithm. The current paper not only presents an efficient distributed community finding algorithm, but also demonstrates that synthetic network coordinates could be used to derive efficient solutions to a variety of problems. Experimental results and comparisons with other methods from the literature are presented for a variety of benchmark graphs with known community structure, derived from varying a number of graph parameters and real data set graphs. The experimental results and comparisons to existing methods with similar computation cost on real and synthetic data sets demonstrate the high performance and robustness of the proposed scheme. (paper)

  4. SmartPipes: Smart Wireless Sensor Networks for Leak Detection in Water Pipelines

    Directory of Open Access Journals (Sweden)

    Ali M. Sadeghioon

    2014-02-01

    Full Text Available Asset monitoring, specifically infrastructure monitoring such as water distribution pipelines, is becoming increasingly critical for utility owners who face new challenges due to an aging network. In the UK alone, during the period of 2009–2010, approximately 3281 mega litres (106 of water were wasted due to failure or leaks in water pipelines. Various techniques can be used for the monitoring of water distribution networks. This paper presents the design, development and testing of a smart wireless sensor network for leak detection in water pipelines, based on the measurement of relative indirect pressure changes in plastic pipes. Power consumption of the sensor nodes is minimised to 2.2 mW based on one measurement every 6 h in order to prolong the lifetime of the network and increase the sensor nodes’ compatibility with current levels of power available by energy harvesting methods and long life batteries. A novel pressure sensing method is investigated for its performance and capabilities by both laboratory and field trials. The sensors were capable of measuring pressure changes due to leaks. These pressure profiles can also be used to locate the leaks.

  5. Community detection in complex networks using proximate support vector clustering

    Science.gov (United States)

    Wang, Feifan; Zhang, Baihai; Chai, Senchun; Xia, Yuanqing

    2018-03-01

    Community structure, one of the most attention attracting properties in complex networks, has been a cornerstone in advances of various scientific branches. A number of tools have been involved in recent studies concentrating on the community detection algorithms. In this paper, we propose a support vector clustering method based on a proximity graph, owing to which the introduced algorithm surpasses the traditional support vector approach both in accuracy and complexity. Results of extensive experiments undertaken on computer generated networks and real world data sets illustrate competent performances in comparison with the other counterparts.

  6. Tansig activation function (of MLP network) for cardiac abnormality detection

    Science.gov (United States)

    Adnan, Ja'afar; Daud, Nik Ghazali Nik; Ishak, Mohd Taufiq; Rizman, Zairi Ismael; Rahman, Muhammad Izzuddin Abd

    2018-02-01

    Heart abnormality often occurs regardless of gender, age and races. This problem sometimes does not show any symptoms and it can cause a sudden death to the patient. In general, heart abnormality is the irregular electrical activity of the heart. This paper attempts to develop a program that can detect heart abnormality activity through implementation of Multilayer Perceptron (MLP) network. A certain amount of data of the heartbeat signals from the electrocardiogram (ECG) will be used in this project to train the MLP network by using several training algorithms with Tansig activation function.

  7. Eddy current crack detection capability assessment approach using crack specimens with differing electrical conductivity

    Science.gov (United States)

    Koshti, Ajay M.

    2018-03-01

    Like other NDE methods, eddy current surface crack detectability is determined using probability of detection (POD) demonstration. The POD demonstration involves eddy current testing of surface crack specimens with known crack sizes. Reliably detectable flaw size, denoted by, a90/95 is determined by statistical analysis of POD test data. The surface crack specimens shall be made from a similar material with electrical conductivity close to the part conductivity. A calibration standard with electro-discharged machined (EDM) notches is typically used in eddy current testing for surface crack detection. The calibration standard conductivity shall be within +/- 15% of the part conductivity. This condition is also applicable to the POD demonstration crack set. Here, a case is considered, where conductivity of the crack specimens available for POD testing differs by more than 15% from that of the part to be inspected. Therefore, a direct POD demonstration of reliably detectable flaw size is not applicable. Additional testing is necessary to use the demonstrated POD test data. An approach to estimate the reliably detectable flaw size in eddy current testing for part made from material A using POD crack specimens made from material B with different conductivity is provided. The approach uses additional test data obtained on EDM notch specimens made from materials A and B. EDM notch test data from the two materials is used to create a transfer function between the demonstrated a90/95 size on crack specimens made of material B and the estimated a90/95 size for part made of material A. Two methods are given. For method A, a90/95 crack size for material B is given and POD data is available. Objective of method A is to determine a90/95 crack size for material A using the same relative decision threshold that was used for material B. For method B, target crack size a90/95 for material A is known. Objective is to determine decision threshold for inspecting material A.

  8. An Embedded Wireless Sensor Network with Wireless Power Transmission Capability for the Structural Health Monitoring of Reinforced Concrete Structures.

    Science.gov (United States)

    Gallucci, Luca; Menna, Costantino; Angrisani, Leopoldo; Asprone, Domenico; Moriello, Rosario Schiano Lo; Bonavolontà, Francesco; Fabbrocino, Francesco

    2017-11-07

    Maintenance strategies based on structural health monitoring can provide effective support in the optimization of scheduled repair of existing structures, thus enabling their lifetime to be extended. With specific regard to reinforced concrete (RC) structures, the state of the art seems to still be lacking an efficient and cost-effective technique capable of monitoring material properties continuously over the lifetime of a structure. Current solutions can typically only measure the required mechanical variables in an indirect, but economic, manner, or directly, but expensively. Moreover, most of the proposed solutions can only be implemented by means of manual activation, making the monitoring very inefficient and then poorly supported. This paper proposes a structural health monitoring system based on a wireless sensor network (WSN) that enables the automatic monitoring of a complete structure. The network includes wireless distributed sensors embedded in the structure itself, and follows the monitoring-based maintenance (MBM) approach, with its ABCDE paradigm, namely: accuracy, benefit, compactness, durability, and easiness of operations. The system is structured in a node level and has a network architecture that enables all the node data to converge in a central unit. Human control is completely unnecessary until the periodic evaluation of the collected data. Several tests are conducted in order to characterize the system from a metrological point of view and assess its performance and effectiveness in real RC conditions.

  9. An Embedded Wireless Sensor Network with Wireless Power Transmission Capability for the Structural Health Monitoring of Reinforced Concrete Structures

    Directory of Open Access Journals (Sweden)

    Luca Gallucci

    2017-11-01

    Full Text Available Maintenance strategies based on structural health monitoring can provide effective support in the optimization of scheduled repair of existing structures, thus enabling their lifetime to be extended. With specific regard to reinforced concrete (RC structures, the state of the art seems to still be lacking an efficient and cost-effective technique capable of monitoring material properties continuously over the lifetime of a structure. Current solutions can typically only measure the required mechanical variables in an indirect, but economic, manner, or directly, but expensively. Moreover, most of the proposed solutions can only be implemented by means of manual activation, making the monitoring very inefficient and then poorly supported. This paper proposes a structural health monitoring system based on a wireless sensor network (WSN that enables the automatic monitoring of a complete structure. The network includes wireless distributed sensors embedded in the structure itself, and follows the monitoring-based maintenance (MBM approach, with its ABCDE paradigm, namely: accuracy, benefit, compactness, durability, and easiness of operations. The system is structured in a node level and has a network architecture that enables all the node data to converge in a central unit. Human control is completely unnecessary until the periodic evaluation of the collected data. Several tests are conducted in order to characterize the system from a metrological point of view and assess its performance and effectiveness in real RC conditions.

  10. The capability to detect wimps with a high energy neutrino telescope

    International Nuclear Information System (INIS)

    Blondeau, F.

    1998-05-01

    We studied the potential of the proposed ANTARES undersea neutrino telescope to detect muons coming from from neutralinos annihilating at the center of the Earth. First results show that the full 1 km 3 -scale detector can indicate, after a few years of operation, if there are indeed neutralinos trapped at the core of celestial bodies, as expected are the major form of dark matter in our galaxy. (author)

  11. Summary of detection, location, and characterization capabilities of AE for continuous monitoring of cracks in reactors

    International Nuclear Information System (INIS)

    Hutton, P.H.; Kurtz, R.J.; Friesel, M.A.; Pappas, R.A.; Skorpik, J.R.; Dawson, J.F.

    1984-10-01

    The objective of the program is to develop acoustic emission (AE) methods for continuous monitoring of reactor pressure boundaries to detect and evaluate crack growth. The approach involves three phases: develop relationships to identify crack growth AE signals and to use identified crack growth AE data to estimate flaw severity; evaluate and refine AE/flaw relationships through fatigue testing a heavy section vessel under simulated reactor conditions; and demonstrate continuous AE monitoring on a nuclear power reactor system

  12. X-ray detection capability of a Cs2ZnCl4 single-crystal scintillator

    International Nuclear Information System (INIS)

    Yahaba, Natsuna; Koshimizu, Masanori; Sun, Yan; Asai, Keisuke; Yanagida, Takayuki; Fujimoto, Yutaka; Haruki, Rie; Nishikido, Fumihiko; Kishimoto, Shunji

    2014-01-01

    The X-ray detection capability of a scintillation detector equipped with a Cs 2 ZnCl 4 single crystal was evaluated. The scintillation decay kinetics can be expressed as the sum of two exponential decay components. The fast decay component had a decay time constant of 1.8 ns, and its relative intensity was 95%. The total light output was 630 photons/MeV, and a subnanosecond timing resolution of 0.66 ns was obtained. The detection efficiency of 67.4 keV X-rays was 80% for a detector equipped with a 2.2-mm-thick Cs 2 ZnCl 4 crystal. Thus, excellent timing resolution and high detection efficiency were achieved simultaneously. (author)

  13. Power plant fault detection using artificial neural network

    Science.gov (United States)

    Thanakodi, Suresh; Nazar, Nazatul Shiema Moh; Joini, Nur Fazriana; Hidzir, Hidzrin Dayana Mohd; Awira, Mohammad Zulfikar Khairul

    2018-02-01

    The fault that commonly occurs in power plants is due to various factors that affect the system outage. There are many types of faults in power plants such as single line to ground fault, double line to ground fault, and line to line fault. The primary aim of this paper is to diagnose the fault in 14 buses power plants by using an Artificial Neural Network (ANN). The Multilayered Perceptron Network (MLP) that detection trained utilized the offline training methods such as Gradient Descent Backpropagation (GDBP), Levenberg-Marquardt (LM), and Bayesian Regularization (BR). The best method is used to build the Graphical User Interface (GUI). The modelling of 14 buses power plant, network training, and GUI used the MATLAB software.

  14. Effective surveillance for early classical swine fever virus detection will utilize both virus and antibody detection capabilities.

    Science.gov (United States)

    Panyasing, Yaowalak; Kedkovid, Roongtham; Thanawongnuwech, Roongroje; Kittawornrat, Apisit; Ji, Ju; Giménez-Lirola, Luis; Zimmerman, Jeffrey

    2018-03-01

    Early recognition and rapid elimination of infected animals is key to controlling incursions of classical swine fever virus (CSFV). In this study, the diagnostic characteristics of 10 CSFV assays were evaluated using individual serum (n = 601) and/or oral fluid (n = 1417) samples collected from -14 to 28 days post inoculation (DPI). Serum samples were assayed by virus isolation (VI), 2 commercial antigen-capture enzyme-linked immunosorbent assays (ELISA), virus neutralization (VN), and 3 antibody ELISAs. Both serum and oral fluid samples were tested with 3 commercial real-time reverse transcription-polymerase chain reaction (rRT-PCR) assays. One or more serum samples was positive by VI from DPIs 3 to 21 and by antigen-capture ELISAs from DPIs 6 to 17. VN-positive serum samples were observed at DPIs ≥ 7 and by antibody ELISAs at DPIs ≥ 10. CSFV RNA was detected in serum samples from DPIs 2 to 28 and in oral fluid samples from DPIs 4 to 28. Significant differences in assay performance were detected, but most importantly, no single combination of sample and assay was able to dependably identify CSFV-inoculated pigs throughout the 4-week course of the study. The results show that effective surveillance for CSFV, especially low virulence strains, will require the use of PCR-based assays for the detection of early infections (<14 days) and antibody-based assays, thereafter. Copyright © 2018 Elsevier B.V. All rights reserved.

  15. Event-Triggered Fault Detection of Nonlinear Networked Systems.

    Science.gov (United States)

    Li, Hongyi; Chen, Ziran; Wu, Ligang; Lam, Hak-Keung; Du, Haiping

    2017-04-01

    This paper investigates the problem of fault detection for nonlinear discrete-time networked systems under an event-triggered scheme. A polynomial fuzzy fault detection filter is designed to generate a residual signal and detect faults in the system. A novel polynomial event-triggered scheme is proposed to determine the transmission of the signal. A fault detection filter is designed to guarantee that the residual system is asymptotically stable and satisfies the desired performance. Polynomial approximated membership functions obtained by Taylor series are employed for filtering analysis. Furthermore, sufficient conditions are represented in terms of sum of squares (SOSs) and can be solved by SOS tools in MATLAB environment. A numerical example is provided to demonstrate the effectiveness of the proposed results.

  16. Detection of mobile user location on next generation wireless networks

    DEFF Research Database (Denmark)

    Schou, Saowanee; Olesen, Henning

    2005-01-01

    This paper proposes a novel conceptual mechanism for detecting the location of a mobile user on next generation wireless networks. This mechanism can provide location information of a mobile user at different levels of accuracy, by applying the movement detection mechanism of Mobile IPv6 at both...... macro- and micromobility level. In this scheme, an intradomain mobility management protocol (IDMP) is applied to manage the location of the mobile terminal. The mobile terminal needs two care-of addresses, a global care-of address (GCoA) and a local care-of address (LCoA). The current location...... of a Mobile IPv6 device can be determined by mapping the geographical location information with the two care-of-addresses and the physical address of the access point where the user is connected. Such a mechanism makes location services for mobile entities available on a global IP network. The end-users can...

  17. Cortical network during deception detection by functional neuroimaging

    International Nuclear Information System (INIS)

    Saito, Keiichi

    2008-01-01

    We examined the coherence of cortical network during deception detection. First, we performed combined EEG-MRI experiments during the Guilty Knowledge Test (GKT) using number cards which has been used to model deception and 5 right-handed healthy participants performed the experiment. The superior frontal gyrus, the anterior cingulate cortex and the inferior parietal lobule were activated and the P 300 event-related brain potential (300-450 ms) was detected at only 'Lie' card. Secondary, we measured magnetoencephalography (MEG) data during GKT and the other 5 right-handed healthy subjects participated in the next experiment. The coherence between the superior frontal gyrus and the inferior parietal lobule showed significant differences between 'Lie' card and 'truth' cards during P 300 emerging. This results indicates that the coherence of cortical network is useful for GKT. (author)

  18. Multi-Branch Fully Convolutional Network for Face Detection

    KAUST Repository

    Bai, Yancheng

    2017-07-20

    Face detection is a fundamental problem in computer vision. It is still a challenging task in unconstrained conditions due to significant variations in scale, pose, expressions, and occlusion. In this paper, we propose a multi-branch fully convolutional network (MB-FCN) for face detection, which considers both efficiency and effectiveness in the design process. Our MB-FCN detector can deal with faces at all scale ranges with only a single pass through the backbone network. As such, our MB-FCN model saves computation and thus is more efficient, compared to previous methods that make multiple passes. For each branch, the specific skip connections of the convolutional feature maps at different layers are exploited to represent faces in specific scale ranges. Specifically, small faces can be represented with both shallow fine-grained and deep powerful coarse features. With this representation, superior improvement in performance is registered for the task of detecting small faces. We test our MB-FCN detector on two public face detection benchmarks, including FDDB and WIDER FACE. Extensive experiments show that our detector outperforms state-of-the-art methods on all these datasets in general and by a substantial margin on the most challenging among them (e.g. WIDER FACE Hard subset). Also, MB-FCN runs at 15 FPS on a GPU for images of size 640 x 480 with no assumption on the minimum detectable face size.

  19. An analysis of seismic background noise variation and evaluation of detection capability of Keskin Array (BRTR PS-43) in Turkey

    Science.gov (United States)

    Bakir, M. E.; Ozel, N. M.; Semin, K. U.

    2011-12-01

    Bogazici University, Kandilli Observatory and Earthquake Research Institute (KOERI) is currently operating the Keskin seismic array (BRTR-PS 43) located in town Keskin, providing real-time data to IDC. The instrumentaion of seismic array includes six short period borehole seismometers and one broadband borehole seismometer. The seismic background noise variation of Keskin array are studied in order to estimate the local and regional event detection capability in the frequency range from 1 Hz to 10 Hz. The Power density spectrum and also probability density function of Keskin array data were computed for seasonal and diurnal noise variations between 2008 and 2010. The computation will be extended to cover the period between 2005 and 2008. We attempt to determine the precise frequency characteristics of the background noise, which will help us to assess the station sensitivity. Minimum detectable magnitude versus distance for Keskin seismic array will be analyzed based on the seismic noise analysis. Detailed analysis results of seismic background noise and detection capability will be presented by this research.

  20. Network structure detection and analysis of Shanghai stock market

    Directory of Open Access Journals (Sweden)

    Sen Wu

    2015-04-01

    Full Text Available Purpose: In order to investigate community structure of the component stocks of SSE (Shanghai Stock Exchange 180-index, a stock correlation network is built to find the intra-community and inter-community relationship. Design/methodology/approach: The stock correlation network is built taking the vertices as stocks and edges as correlation coefficients of logarithm returns of stock price. It is built as undirected weighted at first. GN algorithm is selected to detect community structure after transferring the network into un-weighted with different thresholds. Findings: The result of the network community structure analysis shows that the stock market has obvious industrial characteristics. Most of the stocks in the same industry or in the same supply chain are assigned to the same community. The correlation of the internal stock prices’ fluctuation is closer than in different communities. The result of community structure detection also reflects correlations among different industries. Originality/value: Based on the analysis of the community structure in Shanghai stock market, the result reflects some industrial characteristics, which has reference value to relationship among industries or sub-sectors of listed companies.

  1. Abnormality Detection in Mammography using Deep Convolutional Neural Networks

    OpenAIRE

    Xi, Pengcheng; Shu, Chang; Goubran, Rafik

    2018-01-01

    Breast cancer is the most common cancer in women worldwide. The most common screening technology is mammography. To reduce the cost and workload of radiologists, we propose a computer aided detection approach for classifying and localizing calcifications and masses in mammogram images. To improve on conventional approaches, we apply deep convolutional neural networks (CNN) for automatic feature learning and classifier building. In computer-aided mammography, deep CNN classifiers cannot be tra...

  2. Studying Fake News via Network Analysis: Detection and Mitigation

    OpenAIRE

    Shu, Kai; Bernard, H. Russell; Liu, Huan

    2018-01-01

    Social media for news consumption is becoming increasingly popular due to its easy access, fast dissemination, and low cost. However, social media also enable the wide propagation of "fake news", i.e., news with intentionally false information. Fake news on social media poses significant negative societal effects, and also presents unique challenges. To tackle the challenges, many existing works exploit various features, from a network perspective, to detect and mitigate fake news. In essence...

  3. Detection of white matter lesion regions in MRI using SLIC0 and convolutional neural network.

    Science.gov (United States)

    Diniz, Pedro Henrique Bandeira; Valente, Thales Levi Azevedo; Diniz, João Otávio Bandeira; Silva, Aristófanes Corrêa; Gattass, Marcelo; Ventura, Nina; Muniz, Bernardo Carvalho; Gasparetto, Emerson Leandro

    2018-04-19

    White matter lesions are non-static brain lesions that have a prevalence rate up to 98% in the elderly population. Because they may be associated with several brain diseases, it is important that they are detected as soon as possible. Magnetic Resonance Imaging (MRI) provides three-dimensional data with the possibility to detect and emphasize contrast differences in soft tissues, providing rich information about the human soft tissue anatomy. However, the amount of data provided for these images is far too much for manual analysis/interpretation, representing a difficult and time-consuming task for specialists. This work presents a computational methodology capable of detecting regions of white matter lesions of the brain in MRI of FLAIR modality. The techniques highlighted in this methodology are SLIC0 clustering for candidate segmentation and convolutional neural networks for candidate classification. The methodology proposed here consists of four steps: (1) images acquisition, (2) images preprocessing, (3) candidates segmentation and (4) candidates classification. The methodology was applied on 91 magnetic resonance images provided by DASA, and achieved an accuracy of 98.73%, specificity of 98.77% and sensitivity of 78.79% with 0.005 of false positives, without any false positives reduction technique, in detection of white matter lesion regions. It is demonstrated the feasibility of the analysis of brain MRI using SLIC0 and convolutional neural network techniques to achieve success in detection of white matter lesions regions. Copyright © 2018. Published by Elsevier B.V.

  4. The bistatic radar capabilities of the Medicina radiotelescopes in space debris detection and tracking

    Science.gov (United States)

    Montebugnoli, S.; Pupillo, G.; Salerno, E.; Pluchino, S.; di Martino, M.

    2010-03-01

    An accurate measurement of the position and trajectory of the space debris fragments is of primary importance for the characterization of the orbital debris environment. The Medicina Radioastronomical Station is a radio observation facility that is here proposed as receiving part of a ground-based space surveillance system for detecting and tracking space debris at different orbital regions (from Low Earth Orbits up to Geostationary Earth Orbits). The proposed system consists of two bistatic radars formed by the existing Medicina receiving antennas coupled with appropriate transmitters. This paper focuses on the current features and future technical development of the receiving part of the observational setup. Outlines of possible transmitting systems will also be given together with the evaluation of the observation strategies achievable with the proposed facilities.

  5. Learning representations for the early detection of sepsis with deep neural networks.

    Science.gov (United States)

    Kam, Hye Jin; Kim, Ha Young

    2017-10-01

    Sepsis is one of the leading causes of death in intensive care unit patients. Early detection of sepsis is vital because mortality increases as the sepsis stage worsens. This study aimed to develop detection models for the early stage of sepsis using deep learning methodologies, and to compare the feasibility and performance of the new deep learning methodology with those of the regression method with conventional temporal feature extraction. Study group selection adhered to the InSight model. The results of the deep learning-based models and the InSight model were compared. With deep feedforward networks, the area under the ROC curve (AUC) of the models were 0.887 and 0.915 for the InSight and the new feature sets, respectively. For the model with the combined feature set, the AUC was the same as that of the basic feature set (0.915). For the long short-term memory model, only the basic feature set was applied and the AUC improved to 0.929 compared with the existing 0.887 of the InSight model. The contributions of this paper can be summarized in three ways: (i) improved performance without feature extraction using domain knowledge, (ii) verification of feature extraction capability of deep neural networks through comparison with reference features, and (iii) improved performance with feedforward neural networks using long short-term memory, a neural network architecture that can learn sequential patterns. Copyright © 2017 Elsevier Ltd. All rights reserved.

  6. The effect of destination linked feature selection in real-time network intrusion detection

    CSIR Research Space (South Africa)

    Mzila, P

    2013-07-01

    Full Text Available techniques in the network intrusion detection system (NIDS) is the feature selection technique. The ability of NIDS to accurately identify intrusion from the network traffic relies heavily on feature selection, which describes the pattern of the network...

  7. Coherent network detection of gravitational waves: the redundancy veto

    International Nuclear Information System (INIS)

    Wen Linqing; Schutz, Bernard F

    2005-01-01

    A network of gravitational wave detectors is called redundant if, given the direction to a source, the strain induced by a gravitational wave in one or more of the detectors can be fully expressed in terms of the strain induced in others in the network. Because gravitational waves have only two polarizations, any network of three or more differently oriented interferometers with similar observing bands is redundant. The three-armed LISA space interferometer has three outputs that are redundant at low frequencies. The two aligned LIGO interferometers at Hanford WA are redundant, and the LIGO detector at Livingston LA is nearly redundant with either of the Hanford detectors. Redundant networks have a powerful veto against spurious noise, a linear combination of the detector outputs that contains no gravitational wave signal. For LISA, this 'null' output is known as the Sagnac mode, and its use in discriminating between detector noise and a cosmological gravitational wave background is well understood. But the usefulness of the null veto for ground-based detector networks has been ignored until now. We show that it should make it possible to discriminate in a model-independent way between real gravitational waves and accidentally coincident non-Gaussian noise 'events' in redundant networks of two or more broadband detectors. It has been shown that with three detectors, the null output can even be used to locate the direction to the source, and then two other linear combinations of detector outputs give the optimal 'coherent' reconstruction of the two polarization components of the signal. We discuss briefly the implementation of such a detection strategy in realistic networks, where signals are weak, detector calibration is a significant uncertainty, and the various detectors may have different (but overlapping) observing bands

  8. Fully automatic oil spill detection from COSMO-SkyMed imagery using a neural network approach

    Science.gov (United States)

    Avezzano, Ruggero G.; Del Frate, Fabio; Latini, Daniele

    2012-09-01

    The increased amount of available Synthetic Aperture Radar (SAR) images acquired over the ocean represents an extraordinary potential for improving oil spill detection activities. On the other side this involves a growing workload on the operators at analysis centers. In addition, even if the operators go through extensive training to learn manual oil spill detection, they can provide different and subjective responses. Hence, the upgrade and improvements of algorithms for automatic detection that can help in screening the images and prioritizing the alarms are of great benefit. In the framework of an ASI Announcement of Opportunity for the exploitation of COSMO-SkyMed data, a research activity (ASI contract L/020/09/0) aiming at studying the possibility to use neural networks architectures to set up fully automatic processing chains using COSMO-SkyMed imagery has been carried out and results are presented in this paper. The automatic identification of an oil spill is seen as a three step process based on segmentation, feature extraction and classification. We observed that a PCNN (Pulse Coupled Neural Network) was capable of providing a satisfactory performance in the different dark spots extraction, close to what it would be produced by manual editing. For the classification task a Multi-Layer Perceptron (MLP) Neural Network was employed.

  9. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

    OpenAIRE

    Ren, Shaoqing; He, Kaiming; Girshick, Ross; Sun, Jian

    2015-01-01

    State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet and Fast R-CNN have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultan...

  10. CAPABILITIES OF REMOTE SENSING HYPERSPECTRAL IMAGES FOR THE DETECTION OF LEAD CONTAMINATION: A REVIEW

    Directory of Open Access Journals (Sweden)

    A. A. Maliki

    2012-07-01

    Full Text Available Advances in remote sensing technologies are increasingly becoming more useful for resource, ecosystem and agricultural management applications to the extent that these techniques can now also be applied for monitoring of soil contamination and human health risk assessment. While, extensive previous studies have shown that Visible and Near Infrared Spectroscopy (VNIRS in the spectral range 400–2500 nm can be used to quantify various soil constituents simultaneously, the direct determination of metal concentrations by remote sensing and reflectance spectroscopy is not as well examined as other soil parameters. The application of VNIRS, including laboratory hyperpectral measurements, field spectrometer measurements or image spectroscopy, generally achieves a good prediction of metal concentrations when compared to traditional wet chemical methods and has the advantage of being relatively less expensive and faster, allowing chemical assessment of contamination in close to real time. Furthermore, imaging spectroscopy can potentially provide significantly more samples over a larger spatial extent than traditional ground sampling methods. Thus the development of remote sensing techniques (field based and either airborne or satellite hyperspectral imaging can support the monitoring and efficient mapping of metal contamination (in dust and soil for environmental and health impact assessment. This review is concerned with the application of remote sensing and reflectance spectroscopy to the detection of heavy metals and discusses how current methods could be applied for the quantification of Pb contaminated soil surrounding mines and smelters.

  11. Disaster Coverage Predication for the Emerging Tethered Balloon Technology: Capability for Preparedness, Detection, Mitigation, and Response.

    Science.gov (United States)

    Alsamhi, Saeed H; Samar Ansari, Mohd; Rajput, Navin S

    2018-04-01

    A disaster is a consequence of natural hazards and terrorist acts, which have significant potential to disrupt the entire wireless communication infrastructure. Therefore, the essential rescue squads and recovery operations during a catastrophic event will be severely debilitated. To provide efficient communication services, and to reduce casualty mortality and morbidity during the catastrophic events, we proposed the Tethered Balloon technology for disaster preparedness, detection, mitigation, and recovery assessment. The proposed Tethered Balloon is applicable to any type of disaster except for storms. The Tethered Balloon is being actively researched and developed as a simple solution to improve the performance of rescues, facilities, and services of emergency medical communication in the disaster area. The most important requirement for rescue and relief teams during or after the disaster is a high quality of service of delivery communication services to save people's lives. Using our proposed technology, we report that the Tethered Balloon has a large disaster coverage area. Therefore, the rescue and research teams are given higher priority, and their performance significantly improved in the particular coverage area. Tethered Balloon features made it suitable for disaster preparedness, mitigation, and recovery. The performance of rescue and relief teams was effective and efficient before and after the disaster as well as can be continued to coordinate the relief teams until disaster recovery. (Disaster Med Public Health Preparedness. 2018;12:222-231).

  12. Acoustic Emission Detection and Prediction of Fatigue Crack Propagation in Composite Patch Repairs Using Neural Networks

    International Nuclear Information System (INIS)

    Okafor, A. Chukwujekwu; Singh, Navdeep; Singh, Navrag

    2007-01-01

    An aircraft is subjected to severe structural and aerodynamic loads during its service life. These loads can cause damage or weakening of the structure especially for aging military and civilian aircraft, thereby affecting its load carrying capabilities. Hence composite patch repairs are increasingly used to repair damaged aircraft metallic structures to restore its structural efficiency. This paper presents the results of Acoustic Emission (AE) monitoring of crack propagation in 2024-T3 Clad aluminum panels repaired with adhesively bonded octagonal, single sided boron/epoxy composite patch under tension-tension fatigue loading. Crack propagation gages were used to monitor crack initiation. The identified AE sensor features were used to train neural networks for predicting crack length. The results show that AE events are correlated with crack propagation. AE system was able to detect crack propagation even at high noise condition of 10 Hz loading; that crack propagation signals can be differentiated from matrix cracking signals that take place due to fiber breakage in the composite patch. Three back-propagation cascade feed forward networks were trained to predict crack length based on the number of fatigue cycles, AE event number, and both the Fatigue Cycles and AE events, as inputs respectively. Network using both fatigue cycles and AE event number as inputs to predict crack length gave the best results, followed by Network with fatigue cycles as input, while network with just AE events as input had a greater error

  13. Automatic optimisation of gamma dose rate sensor networks: The DETECT Optimisation Tool

    DEFF Research Database (Denmark)

    Helle, K.B.; Müller, T.O.; Astrup, Poul

    2014-01-01

    of the EU FP 7 project DETECT. It evaluates the gamma dose rates that a proposed set of sensors might measure in an emergency and uses this information to optimise the sensor locations. The gamma dose rates are taken from a comprehensive library of simulations of atmospheric radioactive plumes from 64......Fast delivery of comprehensive information on the radiological situation is essential for decision-making in nuclear emergencies. Most national radiological agencies in Europe employ gamma dose rate sensor networks to monitor radioactive pollution of the atmosphere. Sensor locations were often...... source locations. These simulations cover the whole European Union, so the DOT allows evaluation and optimisation of sensor networks for all EU countries, as well as evaluation of fencing sensors around possible sources. Users can choose from seven cost functions to evaluate the capability of a given...

  14. Detection of broken rotor bar faults in induction motor at low load using neural network.

    Science.gov (United States)

    Bessam, B; Menacer, A; Boumehraz, M; Cherif, H

    2016-09-01

    The knowledge of the broken rotor bars characteristic frequencies and amplitudes has a great importance for all related diagnostic methods. The monitoring of motor faults requires a high resolution spectrum to separate different frequency components. The Discrete Fourier Transform (DFT) has been widely used to achieve these requirements. However, at low slip this technique cannot give good results. As a solution for these problems, this paper proposes an efficient technique based on a neural network approach and Hilbert transform (HT) for broken rotor bar diagnosis in induction machines at low load. The Hilbert transform is used to extract the stator current envelope (SCE). Two features are selected from the (SCE) spectrum (the amplitude and frequency of the harmonic). These features will be used as input for neural network. The results obtained are astonishing and it is capable to detect the correct number of broken rotor bars under different load conditions. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  15. Radiation detection and situation management by distributed sensor networks

    International Nuclear Information System (INIS)

    Jan, Frigo; Mielke, Angela; Cai, D. Michael

    2009-01-01

    Detection of radioactive materials in an urban environment usually requires large, portal-monitor-style radiation detectors. However, this may not be a practical solution in many transport scenarios. Alternatively, a distributed sensor network (DSN) could complement portal-style detection of radiological materials through the implementation of arrays of low cost, small heterogeneous sensors with the ability to detect the presence of radioactive materials in a moving vehicle over a specific region. In this paper, we report on the use of a heterogeneous, wireless, distributed sensor network for traffic monitoring in a field demonstration. Through wireless communications, the energy spectra from different radiation detectors are combined to improve the detection confidence. In addition, the DSN exploits other sensor technologies and algorithms to provide additional information about the vehicle, such as its speed, location, class (e.g. car, truck), and license plate number. The sensors are in-situ and data is processed in real-time at each node. Relevant information from each node is sent to a base station computer which is used to assess the movement of radioactive materials

  16. AdaBoost-based algorithm for network intrusion detection.

    Science.gov (United States)

    Hu, Weiming; Hu, Wei; Maybank, Steve

    2008-04-01

    Network intrusion detection aims at distinguishing the attacks on the Internet from normal use of the Internet. It is an indispensable part of the information security system. Due to the variety of network behaviors and the rapid development of attack fashions, it is necessary to develop fast machine-learning-based intrusion detection algorithms with high detection rates and low false-alarm rates. In this correspondence, we propose an intrusion detection algorithm based on the AdaBoost algorithm. In the algorithm, decision stumps are used as weak classifiers. The decision rules are provided for both categorical and continuous features. By combining the weak classifiers for continuous features and the weak classifiers for categorical features into a strong classifier, the relations between these two different types of features are handled naturally, without any forced conversions between continuous and categorical features. Adaptable initial weights and a simple strategy for avoiding overfitting are adopted to improve the performance of the algorithm. Experimental results show that our algorithm has low computational complexity and error rates, as compared with algorithms of higher computational complexity, as tested on the benchmark sample data.

  17. Sleep Deprivation Attack Detection in Wireless Sensor Network

    Science.gov (United States)

    Bhattasali, Tapalina; Chaki, Rituparna; Sanyal, Sugata

    2012-02-01

    Deployment of sensor network in hostile environment makes it mainly vulnerable to battery drainage attacks because it is impossible to recharge or replace the battery power of sensor nodes. Among different types of security threats, low power sensor nodes are immensely affected by the attacks which cause random drainage of the energy level of sensors, leading to death of the nodes. The most dangerous type of attack in this category is sleep deprivation, where target of the intruder is to maximize the power consumption of sensor nodes, so that their lifetime is minimized. Most of the existing works on sleep deprivation attack detection involve a lot of overhead, leading to poor throughput. The need of the day is to design a model for detecting intrusions accurately in an energy efficient manner. This paper proposes a hierarchical framework based on distributed collaborative mechanism for detecting sleep deprivation torture in wireless sensor network efficiently. Proposed model uses anomaly detection technique in two steps to reduce the probability of false intrusion.

  18. Social Network Sensors for Early Detection of Contagious Outbreaks

    Science.gov (United States)

    Christakis, Nicholas A.; Fowler, James H.

    2010-01-01

    Current methods for the detection of contagious outbreaks give contemporaneous information about the course of an epidemic at best. It is known that individuals near the center of a social network are likely to be infected sooner during the course of an outbreak, on average, than those at the periphery. Unfortunately, mapping a whole network to identify central individuals who might be monitored for infection is typically very difficult. We propose an alternative strategy that does not require ascertainment of global network structure, namely, simply monitoring the friends of randomly selected individuals. Such individuals are known to be more central. To evaluate whether such a friend group could indeed provide early detection, we studied a flu outbreak at Harvard College in late 2009. We followed 744 students who were either members of a group of randomly chosen individuals or a group of their friends. Based on clinical diagnoses, the progression of the epidemic in the friend group occurred 13.9 days (95% C.I. 9.9–16.6) in advance of the randomly chosen group (i.e., the population as a whole). The friend group also showed a significant lead time (pepidemic, a full 46 days before the peak in daily incidence in the population as a whole. This sensor method could provide significant additional time to react to epidemics in small or large populations under surveillance. The amount of lead time will depend on features of the outbreak and the network at hand. The method could in principle be generalized to other biological, psychological, informational, or behavioral contagions that spread in networks. PMID:20856792

  19. Phylogenetically informed logic relationships improve detection of biological network organization

    Science.gov (United States)

    2011-01-01

    Background A "phylogenetic profile" refers to the presence or absence of a gene across a set of organisms, and it has been proven valuable for understanding gene functional relationships and network organization. Despite this success, few studies have attempted to search beyond just pairwise relationships among genes. Here we search for logic relationships involving three genes, and explore its potential application in gene network analyses. Results Taking advantage of a phylogenetic matrix constructed from the large orthologs database Roundup, we invented a method to create balanced profiles for individual triplets of genes that guarantee equal weight on the different phylogenetic scenarios of coevolution between genes. When we applied this idea to LAPP, the method to search for logic triplets of genes, the balanced profiles resulted in significant performance improvement and the discovery of hundreds of thousands more putative triplets than unadjusted profiles. We found that logic triplets detected biological network organization and identified key proteins and their functions, ranging from neighbouring proteins in local pathways, to well separated proteins in the whole pathway, and to the interactions among different pathways at the system level. Finally, our case study suggested that the directionality in a logic relationship and the profile of a triplet could disclose the connectivity between the triplet and surrounding networks. Conclusion Balanced profiles are superior to the raw profiles employed by traditional methods of phylogenetic profiling in searching for high order gene sets. Gene triplets can provide valuable information in detection of biological network organization and identification of key genes at different levels of cellular interaction. PMID:22172058

  20. Wireless and real-time structural damage detection: A novel decentralized method for wireless sensor networks

    Science.gov (United States)

    Avci, Onur; Abdeljaber, Osama; Kiranyaz, Serkan; Hussein, Mohammed; Inman, Daniel J.

    2018-06-01

    Being an alternative to conventional wired sensors, wireless sensor networks (WSNs) are extensively used in Structural Health Monitoring (SHM) applications. Most of the Structural Damage Detection (SDD) approaches available in the SHM literature are centralized as they require transferring data from all sensors within the network to a single processing unit to evaluate the structural condition. These methods are found predominantly feasible for wired SHM systems; however, transmission and synchronization of huge data sets in WSNs has been found to be arduous. As such, the application of centralized methods with WSNs has been a challenge for engineers. In this paper, the authors are presenting a novel application of 1D Convolutional Neural Networks (1D CNNs) on WSNs for SDD purposes. The SDD is successfully performed completely wireless and real-time under ambient conditions. As a result of this, a decentralized damage detection method suitable for wireless SHM systems is proposed. The proposed method is based on 1D CNNs and it involves training an individual 1D CNN for each wireless sensor in the network in a format where each CNN is assigned to process the locally-available data only, eliminating the need for data transmission and synchronization. The proposed damage detection method operates directly on the raw ambient vibration condition signals without any filtering or preprocessing. Moreover, the proposed approach requires minimal computational time and power since 1D CNNs merge both feature extraction and classification tasks into a single learning block. This ability is prevailingly cost-effective and evidently practical in WSNs considering the hardware systems have been occasionally reported to suffer from limited power supply in these networks. To display the capability and verify the success of the proposed method, large-scale experiments conducted on a laboratory structure equipped with a state-of-the-art WSN are reported.

  1. Distance Based Method for Outlier Detection of Body Sensor Networks

    Directory of Open Access Journals (Sweden)

    Haibin Zhang

    2016-01-01

    Full Text Available We propose a distance based method for the outlier detection of body sensor networks. Firstly, we use a Kernel Density Estimation (KDE to calculate the probability of the distance to k nearest neighbors for diagnosed data. If the probability is less than a threshold, and the distance of this data to its left and right neighbors is greater than a pre-defined value, the diagnosed data is decided as an outlier. Further, we formalize a sliding window based method to improve the outlier detection performance. Finally, to estimate the KDE by training sensor readings with errors, we introduce a Hidden Markov Model (HMM based method to estimate the most probable ground truth values which have the maximum probability to produce the training data. Simulation results show that the proposed method possesses a good detection accuracy with a low false alarm rate.

  2. A Partially Distributed Intrusion Detection System for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Eung Jun Cho

    2013-11-01

    Full Text Available The increasing use of wireless sensor networks, which normally comprise several very small sensor nodes, makes their security an increasingly important issue. They can be practically and efficiently secured using intrusion detection systems. Conventional security mechanisms are not usually applicable due to the sensor nodes having limitations of computational power, memory capacity, and battery power. Therefore, specific security systems should be designed to function under constraints of energy or memory. A partially distributed intrusion detection system with low memory and power demands is proposed here. It employs a Bloom filter, which allows reduced signature code size. Multiple Bloom filters can be combined to reduce the signature code for each Bloom filter array. The mechanism could then cope with potential denial of service attacks, unlike many previous detection systems with Bloom filters. The mechanism was evaluated and validated through analysis and simulation.

  3. CONEDEP: COnvolutional Neural network based Earthquake DEtection and Phase Picking

    Science.gov (United States)

    Zhou, Y.; Huang, Y.; Yue, H.; Zhou, S.; An, S.; Yun, N.

    2017-12-01

    We developed an automatic local earthquake detection and phase picking algorithm based on Fully Convolutional Neural network (FCN). The FCN algorithm detects and segments certain features (phases) in 3 component seismograms to realize efficient picking. We use STA/LTA algorithm and template matching algorithm to construct the training set from seismograms recorded 1 month before and after the Wenchuan earthquake. Precise P and S phases are identified and labeled to construct the training set. Noise data are produced by combining back-ground noise and artificial synthetic noise to form the equivalent scale of noise set as the signal set. Training is performed on GPUs to achieve efficient convergence. Our algorithm has significantly improved performance in terms of the detection rate and precision in comparison with STA/LTA and template matching algorithms.

  4. Gas Detection Instrument Based on Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    ANSONG FENG

    2013-06-01

    Full Text Available The wireless sensor network is used to simulate poisonous gas generating system in the Fire-Fighting Simulated Training System. In the paper, we use the wireless signal to simulate the poisonous gas source and use received signal strength indicator (RSSI to simulate the distance between the fireman and the gas source. The gas detection instrument samples the temperature and sphygmus of the trainee and uses the wireless signal as poisonous gas signal. When the trainee enters into the poisonous gas area, the gas detection instrument warns with sound and light and sends the type, density value, the location of the poisonous gas and vital signs of the trainee to host. The paper discusses the software and hardware design of the gas detection instrument. The system has been used to the several of Fire-Fighting training systems.

  5. LSTM-Based Hierarchical Denoising Network for Android Malware Detection

    Directory of Open Access Journals (Sweden)

    Jinpei Yan

    2018-01-01

    Full Text Available Mobile security is an important issue on Android platform. Most malware detection methods based on machine learning models heavily rely on expert knowledge for manual feature engineering, which are still difficult to fully describe malwares. In this paper, we present LSTM-based hierarchical denoise network (HDN, a novel static Android malware detection method which uses LSTM to directly learn from the raw opcode sequences extracted from decompiled Android files. However, most opcode sequences are too long for LSTM to train due to the gradient vanishing problem. Hence, HDN uses a hierarchical structure, whose first-level LSTM parallelly computes on opcode subsequences (we called them method blocks to learn the dense representations; then the second-level LSTM can learn and detect malware through method block sequences. Considering that malicious behavior only appears in partial sequence segments, HDN uses method block denoise module (MBDM for data denoising by adaptive gradient scaling strategy based on loss cache. We evaluate and compare HDN with the latest mainstream researches on three datasets. The results show that HDN outperforms these Android malware detection methods,and it is able to capture longer sequence features and has better detection efficiency than N-gram-based malware detection which is similar to our method.

  6. Application of Composite Indices for Improving Joint Detection Capabilities of Instrumented Roof Bolt Drills in Underground Mining and Construction

    Science.gov (United States)

    Liu, Wenpeng; Rostami, Jamal; Elsworth, Derek; Ray, Asok

    2018-03-01

    Roof bolts are the dominant method of ground support in mining and tunneling applications, and the concept of using drilling parameters from the bolter for ground characterization has been studied for a few decades. This refers to the use of drilling data to identify geological features in the ground including joints and voids, as well as rock classification. Rock mass properties, including distribution of joints/voids and strengths of rock layers, are critical factors for proper design of ground support to avoid instability. The goal of this research was to improve the capability and sensitivity of joint detection programs based on the updated pattern recognition algorithms in sensing joints with smaller than 3.175 mm (0.125 in.) aperture while reducing the number of false alarms, and discriminating rock layers with different strengths. A set of concrete blocks with different strengths were used to simulate various rock layers, where the gap between the blocks would represent the joints in laboratory tests. Data obtained from drilling through these blocks were analyzed to improve the reliability and precision of joint detection systems. While drilling parameters can be used to detect the gaps, due to low accuracy of the results, new composite indices have been introduced and used in the analysis to improve the detection rates. This paper briefly discusses ongoing research on joint detection by using drilling parameters collected from a roof bolter in a controlled environment. The performances of the new algorithms for joint detection are also examined by comparing their ability to identify existing joints and reducing false alarms.

  7. Quantitative investigation of a novel small field of view hybrid gamma camera (HGC) capability for sentinel lymph node detection

    Science.gov (United States)

    Lees, John E; Bugby, Sarah L; Jambi, Layal K; Perkins, Alan C

    2016-01-01

    Objective: The hybrid gamma camera (HGC) has been developed to enhance the localization of radiopharmaceutical uptake in targeted tissues during surgical procedures such as sentinel lymph node (SLN) biopsy. To assess the capability of the HGC, a lymph node contrast (LNC) phantom was constructed to simulate medical scenarios of varying radioactivity concentrations and SLN size. Methods: The phantom was constructed using two clear acrylic glass plates. The SLNs were simulated by circular wells of diameters ranging from 10 to 2.5 mm (16 wells in total) in 1 plate. The second plate contains four larger rectangular wells to simulate tissue background activity surrounding the SLNs. The activity used to simulate each SLN ranged between 4 and 0.025 MBq. The activity concentration ratio between the background and the activity injected in the SLNs was 1 : 10. The LNC phantom was placed at different depths of scattering material ranging between 5 and 40 mm. The collimator-to-source distance was 120 mm. Image acquisition times ranged from 60 to 240 s. Results: Contrast-to-noise ratio analysis and full-width-at-half-maximum (FWHM) measurements of the simulated SLNs were carried out for the images obtained. Over the range of activities used, the HGC detected between 87.5 and 100% of the SLNs through 20 mm of scattering material and 75–93.75% of the SLNs through 40 mm of scattering material. The FWHM of the detected SLNs ranged between 11.93 and 14.70 mm. Conclusion: The HGC is capable of detecting low accumulation of activity in small SLNs, indicating its usefulness as an intraoperative imaging system during surgical SLN procedures. Advances in knowledge: This study investigates the capability of a novel small-field-of-view (SFOV) HGC to detect low activity uptake in small SLNs. The phantom and procedure described are inexpensive and could be easily replicated and applied to any SFOV camera, to provide a comparison between systems with clinically relevant

  8. A universal, fault-tolerant, non-linear analytic network for modeling and fault detection

    International Nuclear Information System (INIS)

    Mott, J.E.; King, R.W.; Monson, L.R.; Olson, D.L.; Staffon, J.D.

    1992-01-01

    The similarities and differences of a universal network to normal neural networks are outlined. The description and application of a universal network is discussed by showing how a simple linear system is modeled by normal techniques and by universal network techniques. A full implementation of the universal network as universal process modeling software on a dedicated computer system at EBR-II is described and example results are presented. It is concluded that the universal network provides different feature recognition capabilities than a neural network and that the universal network can provide extremely fast, accurate, and fault-tolerant estimation, validation, and replacement of signals in a real system

  9. A universal, fault-tolerant, non-linear analytic network for modeling and fault detection

    Energy Technology Data Exchange (ETDEWEB)

    Mott, J.E. [Advanced Modeling Techniques Corp., Idaho Falls, ID (United States); King, R.W.; Monson, L.R.; Olson, D.L.; Staffon, J.D. [Argonne National Lab., Idaho Falls, ID (United States)

    1992-03-06

    The similarities and differences of a universal network to normal neural networks are outlined. The description and application of a universal network is discussed by showing how a simple linear system is modeled by normal techniques and by universal network techniques. A full implementation of the universal network as universal process modeling software on a dedicated computer system at EBR-II is described and example results are presented. It is concluded that the universal network provides different feature recognition capabilities than a neural network and that the universal network can provide extremely fast, accurate, and fault-tolerant estimation, validation, and replacement of signals in a real system.

  10. Detection capability of a pulsed Ground Penetrating Radar utilizing an oscilloscope and Radargram Fusion Approach for optimal signal quality

    Science.gov (United States)

    Seyfried, Daniel; Schoebel, Joerg

    2015-07-01

    In scientific research pulsed radars often employ a digital oscilloscope as sampling unit. The sensitivity of an oscilloscope is determined in general by means of the number of digits of its analog-to-digital converter and the selected full scale vertical setting, i.e., the maximal voltage range displayed. Furthermore oversampling or averaging of the input signal may increase the effective number of digits, hence the sensitivity. Especially for Ground Penetrating Radar applications high sensitivity of the radar system is demanded since reflection amplitudes of buried objects are strongly attenuated in ground. Hence, in order to achieve high detection capability this parameter is one of the most crucial ones. In this paper we analyze the detection capability of our pulsed radar system utilizing a Rohde & Schwarz RTO 1024 oscilloscope as sampling unit for Ground Penetrating Radar applications, such as detection of pipes and cables in the ground. Also effects of averaging and low-noise amplification of the received signal prior to sampling are investigated by means of an appropriate laboratory setup. To underline our findings we then present real-world radar measurements performed on our GPR test site, where we have buried pipes and cables of different types and materials in different depths. The results illustrate the requirement for proper choice of the settings of the oscilloscope for optimal data recording. However, as we show, displaying both strong signal contributions due to e.g., antenna cross-talk and direct ground bounce reflection as well as weak reflections from objects buried deeper in ground requires opposing trends for the oscilloscope's settings. We therefore present our Radargram Fusion Approach. By means of this approach multiple radargrams recorded in parallel, each with an individual optimized setting for a certain type of contribution, can be fused in an appropriate way in order to finally achieve a single radargram which displays all

  11. Detection of strong attractors in social media networks.

    Science.gov (United States)

    Qasem, Ziyaad; Jansen, Marc; Hecking, Tobias; Hoppe, H Ulrich

    2016-01-01

    Detection of influential actors in social media such as Twitter or Facebook plays an important role for improving the quality and efficiency of work and services in many fields such as education and marketing. The work described here aims to introduce a new approach that characterizes the influence of actors by the strength of attracting new active members into a networked community. We present a model of influence of an actor that is based on the attractiveness of the actor in terms of the number of other new actors with which he or she has established relations over time. We have used this concept and measure of influence to determine optimal seeds in a simulation of influence maximization using two empirically collected social networks for the underlying graphs. Our empirical results on the datasets demonstrate that our measure stands out as a useful measure to define the attractors comparing to the other influence measures.

  12. ID card number detection algorithm based on convolutional neural network

    Science.gov (United States)

    Zhu, Jian; Ma, Hanjie; Feng, Jie; Dai, Leiyan

    2018-04-01

    In this paper, a new detection algorithm based on Convolutional Neural Network is presented in order to realize the fast and convenient ID information extraction in multiple scenarios. The algorithm uses the mobile device equipped with Android operating system to locate and extract the ID number; Use the special color distribution of the ID card, select the appropriate channel component; Use the image threshold segmentation, noise processing and morphological processing to take the binary processing for image; At the same time, the image rotation and projection method are used for horizontal correction when image was tilting; Finally, the single character is extracted by the projection method, and recognized by using Convolutional Neural Network. Through test shows that, A single ID number image from the extraction to the identification time is about 80ms, the accuracy rate is about 99%, It can be applied to the actual production and living environment.

  13. Boundary Region Detection for Continuous Objects in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Yaqiang Zhang

    2018-01-01

    Full Text Available Industrial Internet of Things has been widely used to facilitate disaster monitoring applications, such as liquid leakage and toxic gas detection. Since disasters are usually harmful to the environment, detecting accurate boundary regions for continuous objects in an energy-efficient and timely fashion is a long-standing research challenge. This article proposes a novel mechanism for continuous object boundary region detection in a fog computing environment, where sensing holes may exist in the deployed network region. Leveraging sensory data that have been gathered, interpolation algorithms have been applied to estimate sensory data at certain geographical locations, in order to estimate a more accurate boundary line. To examine whether estimated sensory data reflect that fact, mobile sensors are adopted to traverse these locations for gathering their sensory data, and the boundary region is calibrated accordingly. Experimental evaluation shows that this technique can generate a precise object boundary region with certain time constraints, and the network lifetime can be prolonged significantly.

  14. Cyber-Physical Trade-Offs in Distributed Detection Networks

    International Nuclear Information System (INIS)

    Rao, Nageswara S.; Yao, David K.Y.; Chin, J.C.; Ma, Chris Y.T.; Madan, Rabinder

    2010-01-01

    We consider a network of sensors that measure the scalar intensity due to the background or a source combined with background, inside a two-dimensional monitoring area. The sensor measurements may be random due to the underlying nature of the source and background or due to sensor errors or both. The detection problem is to infer the presence of a source of unknown intensity and location based on sensor measurements. In the conventional approach, detection decisions are made at the individual sensors, which are then combined at the fusion center, for example using the majority rule. With increased communication and computation costs, we show that a more complex fusion algorithm based on measurements achieves better detection performance under smooth and non-smooth source intensity functions, Lipschitz conditions on probability ratios and a minimum packing number for the state-space. We show that these conditions for trade-offs between the cyber costs and physical detection performance are applicable for two detection problems: (i) point radiation sources amidst background radiation, and (ii) sources and background with Gaussian distributions.

  15. Cellular neural networks for motion estimation and obstacle detection

    Directory of Open Access Journals (Sweden)

    D. Feiden

    2003-01-01

    Full Text Available Obstacle detection is an important part of Video Processing because it is indispensable for a collision prevention of autonomously navigating moving objects. For example, vehicles driving without human guidance need a robust prediction of potential obstacles, like other vehicles or pedestrians. Most of the common approaches of obstacle detection so far use analytical and statistical methods like motion estimation or generation of maps. In the first part of this contribution a statistical algorithm for obstacle detection in monocular video sequences is presented. The proposed procedure is based on a motion estimation and a planar world model which is appropriate to traffic scenes. The different processing steps of the statistical procedure are a feature extraction, a subsequent displacement vector estimation and a robust estimation of the motion parameters. Since the proposed procedure is composed of several processing steps, the error propagation of the successive steps often leads to inaccurate results. In the second part of this contribution it is demonstrated, that the above mentioned problems can be efficiently overcome by using Cellular Neural Networks (CNN. It will be shown, that a direct obstacle detection algorithm can be easily performed, based only on CNN processing of the input images. Beside the enormous computing power of programmable CNN based devices, the proposed method is also very robust in comparison to the statistical method, because is shows much less sensibility to noisy inputs. Using the proposed approach of obstacle detection in planar worlds, a real time processing of large input images has been made possible.

  16. Bidirectional QoS support for novelty detection applications based on hierarchical wireless sensor network model

    Science.gov (United States)

    Edwards, Mark; Hu, Fei; Kumar, Sunil

    2004-10-01

    The research on the Novelty Detection System (NDS) (called as VENUS) at the authors' universities has generated exciting results. For example, we can detect an abnormal behavior (such as cars thefts from the parking lot) from a series of video frames based on the cognitively motivated theory of habituation. In this paper, we would like to describe the implementation strategies of lower layer protocols for using large-scale Wireless Sensor Networks (WSN) to NDS with Quality-of-Service (QoS) support. Wireless data collection framework, consisting of small and low-power sensor nodes, provides an alternative mechanism to observe the physical world, by using various types of sensing capabilities that include images (and even videos using Panoptos), sound and basic physical measurements such as temperature. We do not want to lose any 'data query command' packets (in the downstream direction: sink-to-sensors) or have any bit-errors in them since they are so important to the whole sensor network. In the upstream direction (sensors-to-sink), we may tolerate the loss of some sensing data packets. But the 'interested' sensing flow should be assigned a higher priority in terms of multi-hop path choice, network bandwidth allocation, and sensing data packet generation frequency (we hope to generate more sensing data packet for that novel event in the specified network area). The focus of this paper is to investigate MAC-level Quality of Service (QoS) issue in Wireless Sensor Networks (WSN) for Novelty Detection applications. Although QoS has been widely studied in other types of networks including wired Internet, general ad hoc networks and mobile cellular networks, we argue that QoS in WSN has its own characteristics. In wired Internet, the main QoS parameters include delay, jitter and bandwidth. In mobile cellular networks, two most common QoS metrics are: handoff call dropping probability and new call blocking probability. Since the main task of WSN is to detect and report

  17. Seismic network based detection, classification and location of volcanic tremors

    Science.gov (United States)

    Nikolai, S.; Soubestre, J.; Seydoux, L.; de Rosny, J.; Droznin, D.; Droznina, S.; Senyukov, S.; Gordeev, E.

    2017-12-01

    Volcanic tremors constitute an important attribute of volcanic unrest in many volcanoes, and their detection and characterization is a challenging issue of volcano monitoring. The main goal of the present work is to develop a network-based method to automatically classify volcanic tremors, to locate their sources and to estimate the associated wave speed. The method is applied to four and a half years of seismic data continuously recorded by 19 permanent seismic stations in the vicinity of the Klyuchevskoy volcanic group (KVG) in Kamchatka (Russia), where five volcanoes were erupting during the considered time period. The method is based on the analysis of eigenvalues and eigenvectors of the daily array covariance matrix. As a first step, following Seydoux et al. (2016), most coherent signals corresponding to dominating tremor sources are detected based on the width of the covariance matrix eigenvalues distribution. With this approach, the volcanic tremors of the two volcanoes known as most active during the considered period, Klyuchevskoy and Tolbachik, are efficiently detected. As a next step, we consider the array covariance matrix's first eigenvectors computed every day. The main hypothesis of our analysis is that these eigenvectors represent the principal component of the daily seismic wavefield and, for days with tremor activity, characterize the dominant tremor sources. Those first eigenvectors can therefore be used as network-based fingerprints of tremor sources. A clustering process is developed to analyze this collection of first eigenvectors, using correlation coefficient as a measure of their similarity. Then, we locate tremor sources based on cross-correlations amplitudes. We characterize seven tremor sources associated with different periods of activity of four volcanoes: Tolbachik, Klyuchevskoy, Shiveluch, and Kizimen. The developed method does not require a priori knowledge, is fully automatic and the database of network-based tremor fingerprints

  18. Specificity and Strain-Typing Capabilities of Nanorod Array-Surface Enhanced Raman Spectroscopy for Mycoplasma pneumoniae Detection.

    Directory of Open Access Journals (Sweden)

    Kelley C Henderson

    Full Text Available Mycoplasma pneumoniae is a cell wall-less bacterial pathogen of the human respiratory tract that accounts for > 20% of all community-acquired pneumonia (CAP. At present the most effective means for detection and strain-typing is quantitative polymerase chain reaction (qPCR, which can exhibit excellent sensitivity and specificity but requires separate tests for detection and genotyping, lacks standardization between available tests and between labs, and has limited practicality for widespread, point-of-care use. We have developed and previously described a silver nanorod array-surface enhanced Raman Spectroscopy (NA-SERS biosensing platform capable of detecting M. pneumoniae with statistically significant specificity and sensitivity in simulated and true clinical throat swab samples, and the ability to distinguish between reference strains of the two main genotypes of M. pneumoniae. Furthermore, we have established a qualitative lower endpoint of detection for NA-SERS of < 1 genome equivalent (cell/μl and a quantitative multivariate detection limit of 5.3 ± 1 cells/μl. Here we demonstrate using partial least squares- discriminatory analysis (PLS-DA of sample spectra that NA-SERS correctly identified M. pneumoniae clinical isolates from globally diverse origins and distinguished these from a panel of 12 other human commensal and pathogenic mycoplasma species with 100% cross-validated statistical accuracy. Furthermore, PLS-DA correctly classified by strain type all 30 clinical isolates with 96% cross-validated accuracy for type 1 strains, 98% cross-validated accuracy for type 2 strains, and 90% cross-validated accuracy for type 2V strains.

  19. Real-time method for establishing a detection map for a network of sensors

    Science.gov (United States)

    Nguyen, Hung D; Koch, Mark W; Giron, Casey; Rondeau, Daniel M; Russell, John L

    2012-09-11

    A method for establishing a detection map of a dynamically configurable sensor network. This method determines an appropriate set of locations for a plurality of sensor units of a sensor network and establishes a detection map for the network of sensors while the network is being set up; the detection map includes the effects of the local terrain and individual sensor performance. Sensor performance is characterized during the placement of the sensor units, which enables dynamic adjustment or reconfiguration of the placement of individual elements of the sensor network during network set-up to accommodate variations in local terrain and individual sensor performance. The reconfiguration of the network during initial set-up to accommodate deviations from idealized individual sensor detection zones improves the effectiveness of the sensor network in detecting activities at a detection perimeter and can provide the desired sensor coverage of an area while minimizing unintentional gaps in coverage.

  20. A complex network based model for detecting isolated communities in water distribution networks

    Science.gov (United States)

    Sheng, Nan; Jia, Youwei; Xu, Zhao; Ho, Siu-Lau; Wai Kan, Chi

    2013-12-01

    Water distribution network (WDN) is a typical real-world complex network of major infrastructure that plays an important role in human's daily life. In this paper, we explore the formation of isolated communities in WDN based on complex network theory. A graph-algebraic model is proposed to effectively detect the potential communities due to pipeline failures. This model can properly illustrate the connectivity and evolution of WDN during different stages of contingency events, and identify the emerging isolated communities through spectral analysis on Laplacian matrix. A case study on a practical urban WDN in China is conducted, and the consistency between the simulation results and the historical data are reported to showcase the feasibility and effectiveness of the proposed model.

  1. Cellular Neural Network-Based Methods for Distributed Network Intrusion Detection

    Directory of Open Access Journals (Sweden)

    Kang Xie

    2015-01-01

    Full Text Available According to the problems of current distributed architecture intrusion detection systems (DIDS, a new online distributed intrusion detection model based on cellular neural network (CNN was proposed, in which discrete-time CNN (DTCNN was used as weak classifier in each local node and state-controlled CNN (SCCNN was used as global detection method, respectively. We further proposed a new method for design template parameters of SCCNN via solving Linear Matrix Inequality. Experimental results based on KDD CUP 99 dataset show its feasibility and effectiveness. Emerging evidence has indicated that this new approach is affordable to parallelism and analog very large scale integration (VLSI implementation which allows the distributed intrusion detection to be performed better.

  2. Performance Evaluation of Optimization Models for Calibration and Leakage Detection of Water Distribution Network Using Laboratorial Model

    Directory of Open Access Journals (Sweden)

    Ali Nasirian

    2014-05-01

    Full Text Available In this paper the accuracy of leakage detection using Ant Colony Optimization (ACO has been investigated. The method has been evaluated on two networks consist of a hypothetical and a laboratorial networks. The results have proved the capability of the method and have confirmed the good convergence and speed. Experimental evaluations have shown serious effects of the number and value of leakage on the results. It is proved that a good fitness cannot guarantee the accuracy of the results. To cope with this problem two validation methods based on a number of obtained results have been developed.

  3. Community detection, link prediction, and layer interdependence in multilayer networks

    Science.gov (United States)

    De Bacco, Caterina; Power, Eleanor A.; Larremore, Daniel B.; Moore, Cristopher

    2017-04-01

    Complex systems are often characterized by distinct types of interactions between the same entities. These can be described as a multilayer network where each layer represents one type of interaction. These layers may be interdependent in complicated ways, revealing different kinds of structure in the network. In this work we present a generative model, and an efficient expectation-maximization algorithm, which allows us to perform inference tasks such as community detection and link prediction in this setting. Our model assumes overlapping communities that are common between the layers, while allowing these communities to affect each layer in a different way, including arbitrary mixtures of assortative, disassortative, or directed structure. It also gives us a mathematically principled way to define the interdependence between layers, by measuring how much information about one layer helps us predict links in another layer. In particular, this allows us to bundle layers together to compress redundant information and identify small groups of layers which suffice to predict the remaining layers accurately. We illustrate these findings by analyzing synthetic data and two real multilayer networks, one representing social support relationships among villagers in South India and the other representing shared genetic substring material between genes of the malaria parasite.

  4. Neonatal Seizure Detection Using Deep Convolutional Neural Networks.

    Science.gov (United States)

    Ansari, Amir H; Cherian, Perumpillichira J; Caicedo, Alexander; Naulaers, Gunnar; De Vos, Maarten; Van Huffel, Sabine

    2018-04-02

    Identifying a core set of features is one of the most important steps in the development of an automated seizure detector. In most of the published studies describing features and seizure classifiers, the features were hand-engineered, which may not be optimal. The main goal of the present paper is using deep convolutional neural networks (CNNs) and random forest to automatically optimize feature selection and classification. The input of the proposed classifier is raw multi-channel EEG and the output is the class label: seizure/nonseizure. By training this network, the required features are optimized, while fitting a nonlinear classifier on the features. After training the network with EEG recordings of 26 neonates, five end layers performing the classification were replaced with a random forest classifier in order to improve the performance. This resulted in a false alarm rate of 0.9 per hour and seizure detection rate of 77% using a test set of EEG recordings of 22 neonates that also included dubious seizures. The newly proposed CNN classifier outperformed three data-driven feature-based approaches and performed similar to a previously developed heuristic method.

  5. File Detection On Network Traffic Using Approximate Matching

    Directory of Open Access Journals (Sweden)

    Frank Breitinger

    2014-09-01

    Full Text Available In recent years, Internet technologies changed enormously and allow faster Internet connections, higher data rates and mobile usage. Hence, it is possible to send huge amounts of data / files easily which is often used by insiders or attackers to steal intellectual property. As a consequence, data leakage prevention systems (DLPS have been developed which analyze network traffic and alert in case of a data leak. Although the overall concepts of the detection techniques are known, the systems are mostly closed and commercial.Within this paper we present a new technique for network trac analysis based on approximate matching (a.k.a fuzzy hashing which is very common in digital forensics to correlate similar files. This paper demonstrates how to optimize and apply them on single network packets. Our contribution is a straightforward concept which does not need a comprehensive conguration: hash the file and store the digest in the database. Within our experiments we obtained false positive rates between 10-4 and 10-5 and an algorithm throughput of over 650 Mbit/s.

  6. Protecting Clock Synchronization: Adversary Detection through Network Monitoring

    Directory of Open Access Journals (Sweden)

    Elena Lisova

    2016-01-01

    Full Text Available Nowadays, industrial networks are often used for safety-critical applications with real-time requirements. Such applications usually have a time-triggered nature with message scheduling as a core property. Scheduling requires nodes to share the same notion of time, that is, to be synchronized. Therefore, clock synchronization is a fundamental asset in real-time networks. However, since typical standards for clock synchronization, for example, IEEE 1588, do not provide the required level of security, it raises the question of clock synchronization protection. In this paper, we identify a way to break synchronization based on the IEEE 1588 standard, by conducting a man-in-the-middle (MIM attack followed by a delay attack. A MIM attack can be accomplished through, for example, Address Resolution Protocol (ARP poisoning. Using the AVISPA tool, we evaluate the potential to perform a delay attack using ARP poisoning and analyze its consequences showing both that the attack can, indeed, break clock synchronization and that some design choices, such as a relaxed synchronization condition mode, delay bounding, and using knowledge of environmental conditions, can make the network more robust/resilient against these kinds of attacks. Lastly, a Configuration Agent is proposed to monitor and detect anomalies introduced by an adversary performing attacks targeting clock synchronization.

  7. Convolution neural-network-based detection of lung structures

    Science.gov (United States)

    Hasegawa, Akira; Lo, Shih-Chung B.; Freedman, Matthew T.; Mun, Seong K.

    1994-05-01

    Chest radiography is one of the most primary and widely used techniques in diagnostic imaging. Nowadays with the advent of digital radiology, the digital medical image processing techniques for digital chest radiographs have attracted considerable attention, and several studies on the computer-aided diagnosis (CADx) as well as on the conventional image processing techniques for chest radiographs have been reported. In the automatic diagnostic process for chest radiographs, it is important to outline the areas of the lungs, the heart, and the diaphragm. This is because the original chest radiograph is composed of important anatomic structures and, without knowing exact positions of the organs, the automatic diagnosis may result in unexpected detections. The automatic extraction of an anatomical structure from digital chest radiographs can be a useful tool for (1) the evaluation of heart size, (2) automatic detection of interstitial lung diseases, (3) automatic detection of lung nodules, and (4) data compression, etc. Based on the clearly defined boundaries of heart area, rib spaces, rib positions, and rib cage extracted, one should be able to use this information to facilitate the tasks of the CADx on chest radiographs. In this paper, we present an automatic scheme for the detection of lung field from chest radiographs by using a shift-invariant convolution neural network. A novel algorithm for smoothing boundaries of lungs is also presented.

  8. Convolutional neural network features based change detection in satellite images

    Science.gov (United States)

    Mohammed El Amin, Arabi; Liu, Qingjie; Wang, Yunhong

    2016-07-01

    With the popular use of high resolution remote sensing (HRRS) satellite images, a huge research efforts have been placed on change detection (CD) problem. An effective feature selection method can significantly boost the final result. While hand-designed features have proven difficulties to design features that effectively capture high and mid-level representations, the recent developments in machine learning (Deep Learning) omit this problem by learning hierarchical representation in an unsupervised manner directly from data without human intervention. In this letter, we propose approaching the change detection problem from a feature learning perspective. A novel deep Convolutional Neural Networks (CNN) features based HR satellite images change detection method is proposed. The main guideline is to produce a change detection map directly from two images using a pretrained CNN. This method can omit the limited performance of hand-crafted features. Firstly, CNN features are extracted through different convolutional layers. Then, a concatenation step is evaluated after an normalization step, resulting in a unique higher dimensional feature map. Finally, a change map was computed using pixel-wise Euclidean distance. Our method has been validated on real bitemporal HRRS satellite images according to qualitative and quantitative analyses. The results obtained confirm the interest of the proposed method.

  9. Vision-Based Fall Detection with Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Adrián Núñez-Marcos

    2017-01-01

    Full Text Available One of the biggest challenges in modern societies is the improvement of healthy aging and the support to older persons in their daily activities. In particular, given its social and economic impact, the automatic detection of falls has attracted considerable attention in the computer vision and pattern recognition communities. Although the approaches based on wearable sensors have provided high detection rates, some of the potential users are reluctant to wear them and thus their use is not yet normalized. As a consequence, alternative approaches such as vision-based methods have emerged. We firmly believe that the irruption of the Smart Environments and the Internet of Things paradigms, together with the increasing number of cameras in our daily environment, forms an optimal context for vision-based systems. Consequently, here we propose a vision-based solution using Convolutional Neural Networks to decide if a sequence of frames contains a person falling. To model the video motion and make the system scenario independent, we use optical flow images as input to the networks followed by a novel three-step training phase. Furthermore, our method is evaluated in three public datasets achieving the state-of-the-art results in all three of them.

  10. A Novel Congestion Detection Scheme in TCP Over OBS Networks

    KAUST Repository

    Shihada, Basem

    2009-02-01

    This paper introduces a novel congestion detection scheme for high-bandwidth TCP flows over optical burst switching (OBS) networks, called statistical additive increase multiplicative decrease (SAIMD). SAIMD maintains and analyzes a number of previous round-trip time (RTTs) at the TCP senders in order to identify the confidence with which a packet loss event is due to network congestion. The confidence is derived by positioning short-term RTT in the spectrum of long-term historical RTTs. The derived confidence corresponding to the packet loss is then taken in the developed policy for TCP congestion window adjustment. We will show through extensive simulation that the proposed scheme can effectively solve the false congestion detection problem and significantly outperform the conventional TCP counterparts without losing fairness. The advantages gained in our scheme are at the expense of introducing more overhead in the SAIMD TCP senders. Based on the proposed congestion control algorithm, a throughput model is formulated, and is further verified by simulation results.

  11. Aggregated channels network for real-time pedestrian detection

    Science.gov (United States)

    Ghorban, Farzin; Marín, Javier; Su, Yu; Colombo, Alessandro; Kummert, Anton

    2018-04-01

    Convolutional neural networks (CNNs) have demonstrated their superiority in numerous computer vision tasks, yet their computational cost results prohibitive for many real-time applications such as pedestrian detection which is usually performed on low-consumption hardware. In order to alleviate this drawback, most strategies focus on using a two-stage cascade approach. Essentially, in the first stage a fast method generates a significant but reduced amount of high quality proposals that later, in the second stage, are evaluated by the CNN. In this work, we propose a novel detection pipeline that further benefits from the two-stage cascade strategy. More concretely, the enriched and subsequently compressed features used in the first stage are reused as the CNN input. As a consequence, a simpler network architecture, adapted for such small input sizes, allows to achieve real-time performance and obtain results close to the state-of-the-art while running significantly faster without the use of GPU. In particular, considering that the proposed pipeline runs in frame rate, the achieved performance is highly competitive. We furthermore demonstrate that the proposed pipeline on itself can serve as an effective proposal generator.

  12. OH/H2O Detection Capability Evaluation on Chang'e-5 Lunar Mineralogical Spectrometer (LMS)

    Science.gov (United States)

    Liu, Bin; Ren, Xin; Liu, Jianjun; Li, Chunlai; Mu, Lingli; Deng, Liyan

    2016-10-01

    The Chang'e-5 (CE-5) lunar sample return mission is scheduled to launch in 2017 to bring back lunar regolith and drill samples. The Chang'e-5 Lunar Mineralogical Spectrometer (LMS), as one of the three sets of scientific payload installed on the lander, is used to collect in-situ spectrum and analyze the mineralogical composition of the samplingsite. It can also help to select the sampling site, and to compare the measured laboratory spectrum of returned sample with in-situ data. LMS employs acousto-optic tunable filters (AOTFs) and is composed of a VIS/NIR module (0.48μm-1.45μm) and an IR module (1.4μm -3.2μm). It has spectral resolution ranging from 3 to 25 nm, with a field of view (FOV) of 4.24°×4.24°. Unlike Chang'e-3 VIS/NIR Imaging Spectrometer (VNIS), the spectral coverage of LMS is extended from 2.4μm to 3.2μm, which has capability to identify H2O/OH absorption features around 2.7μm. An aluminum plate and an Infragold plate are fixed in the dust cover, being used as calibration targets in the VIS/NIR and IR spectral range respectively when the dust cover is open. Before launch, a ground verification test of LMS needs to be conducted in order to: 1) test and verify the detection capability of LMS through evaluation on the quality of image and spectral data collected for the simulated lunar samples; and 2) evaluate the accuracy of data processing methods by the simulation of instrument working on the moon. The ground verification test will be conducted both in the lab and field. The spectra of simulated lunar regolith/mineral samples will be collected simultaneously by the LMS and two calibrated spectrometers: a FTIR spectrometer (Model 102F) and an ASD FieldSpec 4 Hi-Res spectrometer. In this study, the results of the LMS ground verification test will be reported, and OH/H2O Detection Capability will be evaluated especially.

  13. Premature ventricular contraction detection combining deep neural networks and rules inference.

    Science.gov (United States)

    Zhou, Fei-Yan; Jin, Lin-Peng; Dong, Jun

    2017-06-01

    Premature ventricular contraction (PVC), which is a common form of cardiac arrhythmia caused by ectopic heartbeat, can lead to life-threatening cardiac conditions. Computer-aided PVC detection is of considerable importance in medical centers or outpatient ECG rooms. In this paper, we proposed a new approach that combined deep neural networks and rules inference for PVC detection. The detection performance and generalization were studied using publicly available databases: the MIT-BIH arrhythmia database (MIT-BIH-AR) and the Chinese Cardiovascular Disease Database (CCDD). The PVC detection accuracy on the MIT-BIH-AR database was 99.41%, with a sensitivity and specificity of 97.59% and 99.54%, respectively, which were better than the results from other existing methods. To test the generalization capability, the detection performance was also evaluated on the CCDD. The effectiveness of the proposed method was confirmed by the accuracy (98.03%), sensitivity (96.42%) and specificity (98.06%) with the dataset over 140,000 ECG recordings of the CCDD. Copyright © 2017 Elsevier B.V. All rights reserved.

  14. Using Hybrid Algorithm to Improve Intrusion Detection in Multi Layer Feed Forward Neural Networks

    Science.gov (United States)

    Ray, Loye Lynn

    2014-01-01

    The need for detecting malicious behavior on a computer networks continued to be important to maintaining a safe and secure environment. The purpose of this study was to determine the relationship of multilayer feed forward neural network architecture to the ability of detecting abnormal behavior in networks. This involved building, training, and…

  15. Specializing network analysis to detect anomalous insider actions

    Science.gov (United States)

    Chen, You; Nyemba, Steve; Zhang, Wen; Malin, Bradley

    2012-01-01

    Collaborative information systems (CIS) enable users to coordinate efficiently over shared tasks in complex distributed environments. For flexibility, they provide users with broad access privileges, which, as a side-effect, leave such systems vulnerable to various attacks. Some of the more damaging malicious activities stem from internal misuse, where users are authorized to access system resources. A promising class of insider threat detection models for CIS focuses on mining access patterns from audit logs, however, current models are limited in that they assume organizations have significant resources to generate label cases for training classifiers or assume the user has committed a large number of actions that deviate from “normal” behavior. In lieu of the previous assumptions, we introduce an approach that detects when specific actions of an insider deviate from expectation in the context of collaborative behavior. Specifically, in this paper, we introduce a specialized network anomaly detection model, or SNAD, to detect such events. This approach assesses the extent to which a user influences the similarity of the group of users that access a particular record in the CIS. From a theoretical perspective, we show that the proposed model is appropriate for detecting insider actions in dynamic collaborative systems. From an empirical perspective, we perform an extensive evaluation of SNAD with the access logs of two distinct environments: the patient record access logs a large electronic health record system (6,015 users, 130,457 patients and 1,327,500 accesses) and the editing logs of Wikipedia (2,394,385 revisors, 55,200 articles and 6,482,780 revisions). We compare our model with several competing methods and demonstrate SNAD is significantly more effective: on average it achieves 20–30% greater area under an ROC curve. PMID:23399988

  16. Modern Adaptive Analytics Approach to Lowering Seismic Network Detection Thresholds

    Science.gov (United States)

    Johnson, C. E.

    2017-12-01

    Modern seismic networks present a number of challenges, but perhaps most notably are those related to 1) extreme variation in station density, 2) temporal variation in station availability, and 3) the need to achieve detectability for much smaller events of strategic importance. The first of these has been reasonably addressed in the development of modern seismic associators, such as GLASS 3.0 by the USGS/NEIC, though some work still remains to be done in this area. However, the latter two challenges demand special attention. Station availability is impacted by weather, equipment failure or the adding or removing of stations, and while thresholds have been pushed to increasingly smaller magnitudes, new algorithms are needed to achieve even lower thresholds. Station availability can be addressed by a modern, adaptive architecture that maintains specified performance envelopes using adaptive analytics coupled with complexity theory. Finally, detection thresholds can be lowered using a novel approach that tightly couples waveform analytics with the event detection and association processes based on a principled repicking algorithm that uses particle realignment for enhanced phase discrimination.

  17. Autonomic intrusion detection: Adaptively detecting anomalies over unlabeled audit data streams in computer networks

    KAUST Repository

    Wang, Wei; Guyet, Thomas; Quiniou, René ; Cordier, Marie-Odile; Masseglia, Florent; Zhang, Xiangliang

    2014-01-01

    In this work, we propose a novel framework of autonomic intrusion detection that fulfills online and adaptive intrusion detection over unlabeled HTTP traffic streams in computer networks. The framework holds potential for self-managing: self-labeling, self-updating and self-adapting. Our framework employs the Affinity Propagation (AP) algorithm to learn a subject’s behaviors through dynamical clustering of the streaming data. It automatically labels the data and adapts to normal behavior changes while identifies anomalies. Two large real HTTP traffic streams collected in our institute as well as a set of benchmark KDD’99 data are used to validate the framework and the method. The test results show that the autonomic model achieves better results in terms of effectiveness and efficiency compared to adaptive Sequential Karhunen–Loeve method and static AP as well as three other static anomaly detection methods, namely, k-NN, PCA and SVM.

  18. Autonomic intrusion detection: Adaptively detecting anomalies over unlabeled audit data streams in computer networks

    KAUST Repository

    Wang, Wei

    2014-06-22

    In this work, we propose a novel framework of autonomic intrusion detection that fulfills online and adaptive intrusion detection over unlabeled HTTP traffic streams in computer networks. The framework holds potential for self-managing: self-labeling, self-updating and self-adapting. Our framework employs the Affinity Propagation (AP) algorithm to learn a subject’s behaviors through dynamical clustering of the streaming data. It automatically labels the data and adapts to normal behavior changes while identifies anomalies. Two large real HTTP traffic streams collected in our institute as well as a set of benchmark KDD’99 data are used to validate the framework and the method. The test results show that the autonomic model achieves better results in terms of effectiveness and efficiency compared to adaptive Sequential Karhunen–Loeve method and static AP as well as three other static anomaly detection methods, namely, k-NN, PCA and SVM.

  19. Faulty node detection in wireless sensor networks using a recurrent neural network

    Science.gov (United States)

    Atiga, Jamila; Mbarki, Nour Elhouda; Ejbali, Ridha; Zaied, Mourad

    2018-04-01

    The wireless sensor networks (WSN) consist of a set of sensors that are more and more used in surveillance applications on a large scale in different areas: military, Environment, Health ... etc. Despite the minimization and the reduction of the manufacturing costs of the sensors, they can operate in places difficult to access without the possibility of reloading of battery, they generally have limited resources in terms of power of emission, of processing capacity, data storage and energy. These sensors can be used in a hostile environment, such as, for example, on a field of battle, in the presence of fires, floods, earthquakes. In these environments the sensors can fail, even in a normal operation. It is therefore necessary to develop algorithms tolerant and detection of defects of the nodes for the network of sensor without wires, therefore, the faults of the sensor can reduce the quality of the surveillance if they are not detected. The values that are measured by the sensors are used to estimate the state of the monitored area. We used the Non-linear Auto- Regressive with eXogeneous (NARX), the recursive architecture of the neural network, to predict the state of a node of a sensor from the previous values described by the functions of time series. The experimental results have verified that the prediction of the State is enhanced by our proposed model.

  20. A neural network detection system for lower-hybrid cavities in electron plasma density measured by the FREJA satellite

    International Nuclear Information System (INIS)

    Waldemark, J.; Karlsson, Jan

    1995-03-01

    This paper presents a lower-hybrid cavity detection system, CDS, for measurements of electron plasma density on the FREJA satellite wave experiment. The system can reduce the amount of data to be analysed by as much as 96% and still retain more than 85% of the desired information. The CDS is a combination of a hybrid neural network, HNN and expert rules. The HNN is a Self Organizing Map, SOM, combined with a feed forward back propagation neural net, BP. The CDS can be controlled by the user to operate with various degrees of sensitivity. Maximum detection capability is as high as 95% with data reduction lowered to 85%. 10 refs

  1. Deep Recurrent Neural Networks for seizure detection and early seizure detection systems

    Energy Technology Data Exchange (ETDEWEB)

    Talathi, S. S. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2017-06-05

    Epilepsy is common neurological diseases, affecting about 0.6-0.8 % of world population. Epileptic patients suffer from chronic unprovoked seizures, which can result in broad spectrum of debilitating medical and social consequences. Since seizures, in general, occur infrequently and are unpredictable, automated seizure detection systems are recommended to screen for seizures during long-term electroencephalogram (EEG) recordings. In addition, systems for early seizure detection can lead to the development of new types of intervention systems that are designed to control or shorten the duration of seizure events. In this article, we investigate the utility of recurrent neural networks (RNNs) in designing seizure detection and early seizure detection systems. We propose a deep learning framework via the use of Gated Recurrent Unit (GRU) RNNs for seizure detection. We use publicly available data in order to evaluate our method and demonstrate very promising evaluation results with overall accuracy close to 100 %. We also systematically investigate the application of our method for early seizure warning systems. Our method can detect about 98% of seizure events within the first 5 seconds of the overall epileptic seizure duration.

  2. Community detection for networks with unipartite and bipartite structure

    Science.gov (United States)

    Chang, Chang; Tang, Chao

    2014-09-01

    Finding community structures in networks is important in network science, technology, and applications. To date, most algorithms that aim to find community structures only focus either on unipartite or bipartite networks. A unipartite network consists of one set of nodes and a bipartite network consists of two nonoverlapping sets of nodes with only links joining the nodes in different sets. However, a third type of network exists, defined here as the mixture network. Just like a bipartite network, a mixture network also consists of two sets of nodes, but some nodes may simultaneously belong to two sets, which breaks the nonoverlapping restriction of a bipartite network. The mixture network can be considered as a general case, with unipartite and bipartite networks viewed as its limiting cases. A mixture network can represent not only all the unipartite and bipartite networks, but also a wide range of real-world networks that cannot be properly represented as either unipartite or bipartite networks in fields such as biology and social science. Based on this observation, we first propose a probabilistic model that can find modules in unipartite, bipartite, and mixture networks in a unified framework based on the link community model for a unipartite undirected network [B Ball et al (2011 Phys. Rev. E 84 036103)]. We test our algorithm on synthetic networks (both overlapping and nonoverlapping communities) and apply it to two real-world networks: a southern women bipartite network and a human transcriptional regulatory mixture network. The results suggest that our model performs well for all three types of networks, is competitive with other algorithms for unipartite or bipartite networks, and is applicable to real-world networks.

  3. Detecting malicious chaotic signals in wireless sensor network

    Science.gov (United States)

    Upadhyay, Ranjit Kumar; Kumari, Sangeeta

    2018-02-01

    In this paper, an e-epidemic Susceptible-Infected-Vaccinated (SIV) model has been proposed to analyze the effect of node immunization and worms attacking dynamics in wireless sensor network. A modified nonlinear incidence rate with cyrtoid type functional response has been considered using sleep and active mode approach. Detailed stability analysis and the sufficient criteria for the persistence of the model system have been established. We also established different types of bifurcation analysis for different equilibria at different critical points of the control parameters. We performed a detailed Hopf bifurcation analysis and determine the direction and stability of the bifurcating periodic solutions using center manifold theorem. Numerical simulations are carried out to confirm the theoretical results. The impact of the control parameters on the dynamics of the model system has been investigated and malicious chaotic signals are detected. Finally, we have analyzed the effect of time delay on the dynamics of the model system.

  4. CRITICAL INFORMATION INFRASTRUCTURE SECURITY - NETWORK INTRUSION DETECTION SYSTEMS

    Directory of Open Access Journals (Sweden)

    Cristea DUMITRU

    2011-12-01

    Full Text Available Critical Information Infrastructure security will always be difficult to ensure, just because of the features that make it irreplaceable tor other critical infrastructures normal operation. It is decentralized, interconnected interdependent, controlled by multiple actors (mainly private and incorporating diverse types of technologies. It is almost axiomatic that the disruption of the Critical Information Infrastructure affects systems located much farther away, and the cyber problems have direct consequences on the real world. Indeed the Internet can be used as a multiplier in order to amplify the effects of an attack on some critical infrastructures. Security challenges increase with the technological progress. One of the last lines of defense which comes to complete the overall security scheme of the Critical Information Infrastructure is represented by the Network Intrusion Detection Systems.

  5. Hybrid emergency radiation detection: a wireless sensor network application for consequence management of a radiological release

    Science.gov (United States)

    Kyker, Ronald D.; Berry, Nina; Stark, Doug; Nachtigal, Noel; Kershaw, Chris

    2004-08-01

    The Hybrid Emergency Radiation Detection (HERD) system is a rapidly deployable ad-hoc wireless sensor network for monitoring the radiation hazard associated with a radiation release. The system is designed for low power, small size, low cost, and rapid deployment in order to provide early notification and minimize exposure. The many design tradeoffs, decisions, and challenges in the implementation of this wireless sensor network design will be presented and compared to the commercial systems available. Our research in a scaleable modular architectural highlights the need and implementation of a system level approach that provides flexibility and adaptability for a variety of applications. This approach seeks to minimize power, provide mission specific specialization, and provide the capability to upgrade the system with the most recent technology advancements by encapsulation and modularity. The implementation of a low power, widely available Real Time Operating System (RTOS) for multitasking with an improvement in code maintenance, portability, and reuse will be presented. Finally future design enhancements technology trends affecting wireless sensor networks will be presented.

  6. Detection of Intermediate-Period Transiting Planets with a Network of Small Telescopes: transitsearch.org

    Science.gov (United States)

    Seagroves, Scott; Harker, Justin; Laughlin, Gregory; Lacy, Justin; Castellano, Tim

    2003-12-01

    We describe a project (transitsearch.org) currently attempting to discover transiting intermediate-period planets orbiting bright parent stars, and we simulate that project's performance. The discovery of such a transit would be an important astronomical advance, bridging the critical gap in understanding between HD 209458b and Jupiter. However, the task is made difficult by intrinsically low transit probabilities and small transit duty cycles. This project's efficient and economical strategy is to photometrically monitor stars that are known (from radial velocity surveys) to bear planets, using a network of widely spaced observers with small telescopes. These observers, each individually capable of precision (1%) differential photometry, monitor candidates during the time windows in which the radial velocity solution predicts a transit if the orbital inclination is close to 90°. We use Monte Carlo techniques to simulate the performance of this network, performing simulations with different configurations of observers in order to optimize coordination of an actual campaign. Our results indicate that transitsearch.org can reliably rule out or detect planetary transits within the current catalog of known planet-bearing stars. A distributed network of skilled amateur astronomers and small college observatories is a cost-effective method for discovering the small number of transiting planets with periods in the range 10 days

  7. Multisensor Network System for Wildfire Detection Using Infrared Image Processing

    Directory of Open Access Journals (Sweden)

    I. Bosch

    2013-01-01

    Full Text Available This paper presents the next step in the evolution of multi-sensor wireless network systems in the early automatic detection of forest fires. This network allows remote monitoring of each of the locations as well as communication between each of the sensors and with the control stations. The result is an increased coverage area, with quicker and safer responses. To determine the presence of a forest wildfire, the system employs decision fusion in thermal imaging, which can exploit various expected characteristics of a real fire, including short-term persistence and long-term increases over time. Results from testing in the laboratory and in a real environment are presented to authenticate and verify the accuracy of the operation of the proposed system. The system performance is gauged by the number of alarms and the time to the first alarm (corresponding to a real fire, for different probability of false alarm (PFA. The necessity of including decision fusion is thereby demonstrated.

  8. Duplicate Address Detection Table in IPv6 Mobile Networks

    Science.gov (United States)

    Alisherov, Farkhod; Kim, Taihoon

    In IP networks, each computer or communication equipment needs an IP address. To supply enough IP addresses, the new Internet protocol IPv6 is used in next generatoion mobile communication. Although IPv6 improves the existing IPv4 Internet protocol, Duplicate Address Detection (DAD) mechanism may consume resources and suffer from long delay. DAD is used to ensure whether the IP address is unique or not. When a mobile node performs an inter-domain handoff, it will first generate a new IP and perform a DAD procedure. The DAD procedure not only wastes time but also increases the signaling load on Internet. In this paper, the author proposes a new DAD mechanism to speed up the DAD procedure. A DAD table is created in access or mobility routers in IP networks and record all IP addresses of the area. When a new IP address needs to perform DAD, it can just search in the DAD table to confirm the uniqueness of the address.

  9. Automated embolic signal detection using Deep Convolutional Neural Network.

    Science.gov (United States)

    Sombune, Praotasna; Phienphanich, Phongphan; Phuechpanpaisal, Sutanya; Muengtaweepongsa, Sombat; Ruamthanthong, Anuchit; Tantibundhit, Charturong

    2017-07-01

    This work investigated the potential of Deep Neural Network in detection of cerebral embolic signal (ES) from transcranial Doppler ultrasound (TCD). The resulting system is aimed to couple with TCD devices in diagnosing a risk of stroke in real-time with high accuracy. The Adaptive Gain Control (AGC) approach developed in our previous study is employed to capture suspected ESs in real-time. By using spectrograms of the same TCD signal dataset as that of our previous work as inputs and the same experimental setup, Deep Convolutional Neural Network (CNN), which can learn features while training, was investigated for its ability to bypass the traditional handcrafted feature extraction and selection process. Extracted feature vectors from the suspected ESs are later determined whether they are of an ES, artifact (AF) or normal (NR) interval. The effectiveness of the developed system was evaluated over 19 subjects going under procedures generating emboli. The CNN-based system could achieve in average of 83.0% sensitivity, 80.1% specificity, and 81.4% accuracy, with considerably much less time consumption in development. The certainly growing set of training samples and computational resources will contribute to high performance. Besides having potential use in various clinical ES monitoring settings, continuation of this promising study will benefit developments of wearable applications by leveraging learnable features to serve demographic differentials.

  10. Robust Deep Network with Maximum Correntropy Criterion for Seizure Detection

    Directory of Open Access Journals (Sweden)

    Yu Qi

    2014-01-01

    Full Text Available Effective seizure detection from long-term EEG is highly important for seizure diagnosis. Existing methods usually design the feature and classifier individually, while little work has been done for the simultaneous optimization of the two parts. This work proposes a deep network to jointly learn a feature and a classifier so that they could help each other to make the whole system optimal. To deal with the challenge of the impulsive noises and outliers caused by EMG artifacts in EEG signals, we formulate a robust stacked autoencoder (R-SAE as a part of the network to learn an effective feature. In R-SAE, the maximum correntropy criterion (MCC is proposed to reduce the effect of noise/outliers. Unlike the mean square error (MSE, the output of the new kernel MCC increases more slowly than that of MSE when the input goes away from the center. Thus, the effect of those noises/outliers positioned far away from the center can be suppressed. The proposed method is evaluated on six patients of 33.6 hours of scalp EEG data. Our method achieves a sensitivity of 100% and a specificity of 99%, which is promising for clinical applications.

  11. Intrusion detection techniques for plant-wide network in a nuclear power plant

    International Nuclear Information System (INIS)

    Rajasekhar, P.; Shrikhande, S.V.; Biswas, B.B.; Patil, R.K.

    2012-01-01

    Nuclear power plants have a lot of critical data to be sent to the operator workstations. A plant wide integrated communication network, with high throughput, determinism and redundancy, is required between the workstations and the field. Switched Ethernet network is a promising prospect for such an integrated communication network. But for such an integrated system, intrusion is a major issue. Hence the network should have an intrusion detection system to make the network data secure and enhance the network availability. Intrusion detection is the process of monitoring the events occurring in a network and analyzing them for signs of possible incidents, which are violations or imminent threats of violation of network security policies, acceptable user policies, or standard security practices. This paper states the various intrusion detection techniques and approaches which are applicable for analysis of a plant wide network. (author)

  12. A convolutional neural network for intracranial hemorrhage detection in non-contrast CT

    Science.gov (United States)

    Patel, Ajay; Manniesing, Rashindra

    2018-02-01

    The assessment of the presence of intracranial hemorrhage is a crucial step in the work-up of patients requiring emergency care. Fast and accurate detection of intracranial hemorrhage can aid treating physicians by not only expediting and guiding diagnosis, but also supporting choices for secondary imaging, treatment and intervention. However, the automatic detection of intracranial hemorrhage is complicated by the variation in appearance on non-contrast CT images as a result of differences in etiology and location. We propose a method using a convolutional neural network (CNN) for the automatic detection of intracranial hemorrhage. The method is trained on a dataset comprised of cerebral CT studies for which the presence of hemorrhage has been labeled for each axial slice. A separate test dataset of 20 images is used for quantitative evaluation and shows a sensitivity of 0.87, specificity of 0.97 and accuracy of 0.95. The average processing time for a single three-dimensional (3D) CT volume was 2.7 seconds. The proposed method is capable of fast and automated detection of intracranial hemorrhages in non-contrast CT without being limited to a specific subtype of pathology.

  13. Automated detection and cataloging of global explosive volcanism using the International Monitoring System infrasound network

    Science.gov (United States)

    Matoza, Robin S.; Green, David N.; Le Pichon, Alexis; Shearer, Peter M.; Fee, David; Mialle, Pierrick; Ceranna, Lars

    2017-04-01

    We experiment with a new method to search systematically through multiyear data from the International Monitoring System (IMS) infrasound network to identify explosive volcanic eruption signals originating anywhere on Earth. Detecting, quantifying, and cataloging the global occurrence of explosive volcanism helps toward several goals in Earth sciences and has direct applications in volcanic hazard mitigation. We combine infrasound signal association across multiple stations with source location using a brute-force, grid-search, cross-bearings approach. The algorithm corrects for a background prior rate of coherent unwanted infrasound signals (clutter) in a global grid, without needing to screen array processing detection lists from individual stations prior to association. We develop the algorithm using case studies of explosive eruptions: 2008 Kasatochi, Alaska; 2009 Sarychev Peak, Kurile Islands; and 2010 Eyjafjallajökull, Iceland. We apply the method to global IMS infrasound data from 2005-2010 to construct a preliminary acoustic catalog that emphasizes sustained explosive volcanic activity (long-duration signals or sequences of impulsive transients lasting hours to days). This work represents a step toward the goal of integrating IMS infrasound data products into global volcanic eruption early warning and notification systems. Additionally, a better understanding of volcanic signal detection and location with the IMS helps improve operational event detection, discrimination, and association capabilities.

  14. Robust fault detection of wind energy conversion systems based on dynamic neural networks.

    Science.gov (United States)

    Talebi, Nasser; Sadrnia, Mohammad Ali; Darabi, Ahmad

    2014-01-01

    Occurrence of faults in wind energy conversion systems (WECSs) is inevitable. In order to detect the occurred faults at the appropriate time, avoid heavy economic losses, ensure safe system operation, prevent damage to adjacent relevant systems, and facilitate timely repair of failed components; a fault detection system (FDS) is required. Recurrent neural networks (RNNs) have gained a noticeable position in FDSs and they have been widely used for modeling of complex dynamical systems. One method for designing an FDS is to prepare a dynamic neural model emulating the normal system behavior. By comparing the outputs of the real system and neural model, incidence of the faults can be identified. In this paper, by utilizing a comprehensive dynamic model which contains both mechanical and electrical components of the WECS, an FDS is suggested using dynamic RNNs. The presented FDS detects faults of the generator's angular velocity sensor, pitch angle sensors, and pitch actuators. Robustness of the FDS is achieved by employing an adaptive threshold. Simulation results show that the proposed scheme is capable to detect the faults shortly and it has very low false and missed alarms rate.

  15. Scalable High-Performance Parallel Design for Network Intrusion Detection Systems on Many-Core Processors

    OpenAIRE

    Jiang, Hayang; Xie, Gaogang; Salamatian, Kavé; Mathy, Laurent

    2013-01-01

    Network Intrusion Detection Systems (NIDSes) face significant challenges coming from the relentless network link speed growth and increasing complexity of threats. Both hardware accelerated and parallel software-based NIDS solutions, based on commodity multi-core and GPU processors, have been proposed to overcome these challenges. Network Intrusion Detection Systems (NIDSes) face significant challenges coming from the relentless network link speed growth and increasing complexity of threats. ...

  16. Building dynamic capabilities in large global advertising agency networks: managing the shift from mass communication to digital interactivity

    DEFF Research Database (Denmark)

    Suheimat, Wisam; Prætorius, Thim; Brambini-Pedersen, Jan Vang

    2018-01-01

    Interactive digital technologies result in significant managerial challenges for the largest global advertising agency networks. This paper, based on original data from in-depth case research in three of the largest global advertising networks, investigates how advertising agency networks manage...

  17. Improving Intrusion Detection System Based on Snort Rules for Network Probe Attacks Detection with Association Rules Technique of Data Mining

    Directory of Open Access Journals (Sweden)

    Nattawat Khamphakdee

    2015-07-01

    Full Text Available The intrusion detection system (IDS is an important network security tool for securing computer and network systems. It is able to detect and monitor network traffic data. Snort IDS is an open-source network security tool. It can search and match rules with network traffic data in order to detect attacks, and generate an alert. However, the Snort IDS  can detect only known attacks. Therefore, we have proposed a procedure for improving Snort IDS rules, based on the association rules data mining technique for detection of network probe attacks.  We employed the MIT-DARPA 1999 data set for the experimental evaluation. Since behavior pattern traffic data are both normal and abnormal, the abnormal behavior data is detected by way of the Snort IDS. The experimental results showed that the proposed Snort IDS rules, based on data mining detection of network probe attacks, proved more efficient than the original Snort IDS rules, as well as icmp.rules and icmp-info.rules of Snort IDS.  The suitable parameters for the proposed Snort IDS rules are defined as follows: Min_sup set to 10%, and Min_conf set to 100%, and through the application of eight variable attributes. As more suitable parameters are applied, higher accuracy is achieved.

  18. Neural Network in Fixed Time for Collision Detection between Two Convex Polyhedra

    OpenAIRE

    M. Khouil; N. Saber; M. Mestari

    2014-01-01

    In this paper, a different architecture of a collision detection neural network (DCNN) is developed. This network, which has been particularly reviewed, has enabled us to solve with a new approach the problem of collision detection between two convex polyhedra in a fixed time (O (1) time). We used two types of neurons, linear and threshold logic, which simplified the actual implementation of all the networks proposed. The study of the collision detection is divided into two sections, the coll...

  19. Sensor Anomaly Detection in Wireless Sensor Networks for Healthcare

    Science.gov (United States)

    Haque, Shah Ahsanul; Rahman, Mustafizur; Aziz, Syed Mahfuzul

    2015-01-01

    Wireless Sensor Networks (WSN) are vulnerable to various sensor faults and faulty measurements. This vulnerability hinders efficient and timely response in various WSN applications, such as healthcare. For example, faulty measurements can create false alarms which may require unnecessary intervention from healthcare personnel. Therefore, an approach to differentiate between real medical conditions and false alarms will improve remote patient monitoring systems and quality of healthcare service afforded by WSN. In this paper, a novel approach is proposed to detect sensor anomaly by analyzing collected physiological data from medical sensors. The objective of this method is to effectively distinguish false alarms from true alarms. It predicts a sensor value from historic values and compares it with the actual sensed value for a particular instance. The difference is compared against a threshold value, which is dynamically adjusted, to ascertain whether the sensor value is anomalous. The proposed approach has been applied to real healthcare datasets and compared with existing approaches. Experimental results demonstrate the effectiveness of the proposed system, providing high Detection Rate (DR) and low False Positive Rate (FPR). PMID:25884786

  20. Pneumothorax detection in chest radiographs using convolutional neural networks

    Science.gov (United States)

    Blumenfeld, Aviel; Konen, Eli; Greenspan, Hayit

    2018-02-01

    This study presents a computer assisted diagnosis system for the detection of pneumothorax (PTX) in chest radiographs based on a convolutional neural network (CNN) for pixel classification. Using a pixel classification approach allows utilization of the texture information in the local environment of each pixel while training a CNN model on millions of training patches extracted from a relatively small dataset. The proposed system uses a pre-processing step of lung field segmentation to overcome the large variability in the input images coming from a variety of imaging sources and protocols. Using a CNN classification, suspected pixel candidates are extracted within each lung segment. A postprocessing step follows to remove non-physiological suspected regions and noisy connected components. The overall percentage of suspected PTX area was used as a robust global decision for the presence of PTX in each lung. The system was trained on a set of 117 chest x-ray images with ground truth segmentations of the PTX regions. The system was tested on a set of 86 images and reached diagnosis accuracy of AUC=0.95. Overall preliminary results are promising and indicate the growing ability of CAD based systems to detect findings in medical imaging on a clinical level accuracy.

  1. Detecting Boundary Nodes and Coverage Holes in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Li-Hui Zhao

    2016-01-01

    Full Text Available The emergence of coverage holes in wireless sensor networks (WSNs means that some special events have broken out and the function of WSNs will be seriously influenced. Therefore, the issues of coverage holes have attracted considerable attention. In this paper, we focus on the identification of boundary nodes and coverage holes, which is crucially important to preventing the enlargement of coverage holes and ensuring the transmission of data. We define the problem of coverage holes and propose two novel algorithms to identify the coverage holes in WSNs. The first algorithm, Distributed Sector Cover Scanning (DSCS, can be used to identify the nodes on hole borders and the outer boundary of WSNs. The second scheme, Directional Walk (DW, can locate the coverage holes based on the boundary nodes identified with DSCS. We implement the algorithms in various scenarios and fully evaluate their performance. The simulation results show that the boundary nodes can be accurately detected by DSCS and the holes enclosed by the detected boundary nodes can be identified by DW. The comparisons confirm that the proposed algorithms outperform the existing ones.

  2. Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network

    Directory of Open Access Journals (Sweden)

    Yuexiang Li

    2018-02-01

    Full Text Available Skin lesions are a severe disease globally. Early detection of melanoma in dermoscopy images significantly increases the survival rate. However, the accurate recognition of melanoma is extremely challenging due to the following reasons: low contrast between lesions and skin, visual similarity between melanoma and non-melanoma lesions, etc. Hence, reliable automatic detection of skin tumors is very useful to increase the accuracy and efficiency of pathologists. In this paper, we proposed two deep learning methods to address three main tasks emerging in the area of skin lesion image processing, i.e., lesion segmentation (task 1, lesion dermoscopic feature extraction (task 2 and lesion classification (task 3. A deep learning framework consisting of two fully convolutional residual networks (FCRN is proposed to simultaneously produce the segmentation result and the coarse classification result. A lesion index calculation unit (LICU is developed to refine the coarse classification results by calculating the distance heat-map. A straight-forward CNN is proposed for the dermoscopic feature extraction task. The proposed deep learning frameworks were evaluated on the ISIC 2017 dataset. Experimental results show the promising accuracies of our frameworks, i.e., 0.753 for task 1, 0.848 for task 2 and 0.912 for task 3 were achieved.

  3. Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network.

    Science.gov (United States)

    Li, Yuexiang; Shen, Linlin

    2018-02-11

    Skin lesions are a severe disease globally. Early detection of melanoma in dermoscopy images significantly increases the survival rate. However, the accurate recognition of melanoma is extremely challenging due to the following reasons: low contrast between lesions and skin, visual similarity between melanoma and non-melanoma lesions, etc. Hence, reliable automatic detection of skin tumors is very useful to increase the accuracy and efficiency of pathologists. In this paper, we proposed two deep learning methods to address three main tasks emerging in the area of skin lesion image processing, i.e., lesion segmentation (task 1), lesion dermoscopic feature extraction (task 2) and lesion classification (task 3). A deep learning framework consisting of two fully convolutional residual networks (FCRN) is proposed to simultaneously produce the segmentation result and the coarse classification result. A lesion index calculation unit (LICU) is developed to refine the coarse classification results by calculating the distance heat-map. A straight-forward CNN is proposed for the dermoscopic feature extraction task. The proposed deep learning frameworks were evaluated on the ISIC 2017 dataset. Experimental results show the promising accuracies of our frameworks, i.e., 0.753 for task 1, 0.848 for task 2 and 0.912 for task 3 were achieved.

  4. Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network

    Science.gov (United States)

    2018-01-01

    Skin lesions are a severe disease globally. Early detection of melanoma in dermoscopy images significantly increases the survival rate. However, the accurate recognition of melanoma is extremely challenging due to the following reasons: low contrast between lesions and skin, visual similarity between melanoma and non-melanoma lesions, etc. Hence, reliable automatic detection of skin tumors is very useful to increase the accuracy and efficiency of pathologists. In this paper, we proposed two deep learning methods to address three main tasks emerging in the area of skin lesion image processing, i.e., lesion segmentation (task 1), lesion dermoscopic feature extraction (task 2) and lesion classification (task 3). A deep learning framework consisting of two fully convolutional residual networks (FCRN) is proposed to simultaneously produce the segmentation result and the coarse classification result. A lesion index calculation unit (LICU) is developed to refine the coarse classification results by calculating the distance heat-map. A straight-forward CNN is proposed for the dermoscopic feature extraction task. The proposed deep learning frameworks were evaluated on the ISIC 2017 dataset. Experimental results show the promising accuracies of our frameworks, i.e., 0.753 for task 1, 0.848 for task 2 and 0.912 for task 3 were achieved. PMID:29439500

  5. Aerial Images and Convolutional Neural Network for Cotton Bloom Detection.

    Science.gov (United States)

    Xu, Rui; Li, Changying; Paterson, Andrew H; Jiang, Yu; Sun, Shangpeng; Robertson, Jon S

    2017-01-01

    Monitoring flower development can provide useful information for production management, estimating yield and selecting specific genotypes of crops. The main goal of this study was to develop a methodology to detect and count cotton flowers, or blooms, using color images acquired by an unmanned aerial system. The aerial images were collected from two test fields in 4 days. A convolutional neural network (CNN) was designed and trained to detect cotton blooms in raw images, and their 3D locations were calculated using the dense point cloud constructed from the aerial images with the structure from motion method. The quality of the dense point cloud was analyzed and plots with poor quality were excluded from data analysis. A constrained clustering algorithm was developed to register the same bloom detected from different images based on the 3D location of the bloom. The accuracy and incompleteness of the dense point cloud were analyzed because they affected the accuracy of the 3D location of the blooms and thus the accuracy of the bloom registration result. The constrained clustering algorithm was validated using simulated data, showing good efficiency and accuracy. The bloom count from the proposed method was comparable with the number counted manually with an error of -4 to 3 blooms for the field with a single plant per plot. However, more plots were underestimated in the field with multiple plants per plot due to hidden blooms that were not captured by the aerial images. The proposed methodology provides a high-throughput method to continuously monitor the flowering progress of cotton.

  6. PERFORMANCE COMPARISON FOR INTRUSION DETECTION SYSTEM USING NEURAL NETWORK WITH KDD DATASET

    Directory of Open Access Journals (Sweden)

    S. Devaraju

    2014-04-01

    Full Text Available Intrusion Detection Systems are challenging task for finding the user as normal user or attack user in any organizational information systems or IT Industry. The Intrusion Detection System is an effective method to deal with the kinds of problem in networks. Different classifiers are used to detect the different kinds of attacks in networks. In this paper, the performance of intrusion detection is compared with various neural network classifiers. In the proposed research the four types of classifiers used are Feed Forward Neural Network (FFNN, Generalized Regression Neural Network (GRNN, Probabilistic Neural Network (PNN and Radial Basis Neural Network (RBNN. The performance of the full featured KDD Cup 1999 dataset is compared with that of the reduced featured KDD Cup 1999 dataset. The MATLAB software is used to train and test the dataset and the efficiency and False Alarm Rate is measured. It is proved that the reduced dataset is performing better than the full featured dataset.

  7. Deep Fully Convolutional Networks for the Detection of Informal Settlements in VHR Images

    NARCIS (Netherlands)

    Persello, Claudio; Stein, Alfred

    2017-01-01

    This letter investigates fully convolutional networks (FCNs) for the detection of informal settlements in very high resolution (VHR) satellite images. Informal settlements or slums are proliferating in developing countries and their detection and classification provides vital information for

  8. Pattern detection in stream networks: Quantifying spatialvariability in fish distribution

    Science.gov (United States)

    Torgersen, Christian E.; Gresswell, Robert E.; Bateman, Douglas S.

    2004-01-01

    Biological and physical properties of rivers and streams are inherently difficult to sample and visualize at the resolution and extent necessary to detect fine-scale distributional patterns over large areas. Satellite imagery and broad-scale fish survey methods are effective for quantifying spatial variability in biological and physical variables over a range of scales in marine environments but are often too coarse in resolution to address conservation needs in inland fisheries management. We present methods for sampling and analyzing multiscale, spatially continuous patterns of stream fishes and physical habitat in small- to medium-size watersheds (500–1000 hectares). Geospatial tools, including geographic information system (GIS) software such as ArcInfo dynamic segmentation and ArcScene 3D analyst modules, were used to display complex biological and physical datasets. These tools also provided spatial referencing information (e.g. Cartesian and route-measure coordinates) necessary for conducting geostatistical analyses of spatial patterns (empirical semivariograms and wavelet analysis) in linear stream networks. Graphical depiction of fish distribution along a one-dimensional longitudinal profile and throughout the stream network (superimposed on a 10-metre digital elevation model) provided the spatial context necessary for describing and interpreting the relationship between landscape pattern and the distribution of coastal cutthroat trout (Oncorhynchus clarki clarki) in western Oregon, U.S.A. The distribution of coastal cutthroat trout was highly autocorrelated and exhibited a spherical semivariogram with a defined nugget, sill, and range. Wavelet analysis of the main-stem longitudinal profile revealed periodicity in trout distribution at three nested spatial scales corresponding ostensibly to landscape disturbances and the spacing of tributary junctions.

  9. Weak signal transmission in complex networks and its application in detecting connectivity.

    Science.gov (United States)

    Liang, Xiaoming; Liu, Zonghua; Li, Baowen

    2009-10-01

    We present a network model of coupled oscillators to study how a weak signal is transmitted in complex networks. Through both theoretical analysis and numerical simulations, we find that the response of other nodes to the weak signal decays exponentially with their topological distance to the signal source and the coupling strength between two neighboring nodes can be figured out by the responses. This finding can be conveniently used to detect the topology of unknown network, such as the degree distribution, clustering coefficient and community structure, etc., by repeatedly choosing different nodes as the signal source. Through four typical networks, i.e., the regular one dimensional, small world, random, and scale-free networks, we show that the features of network can be approximately given by investigating many fewer nodes than the network size, thus our approach to detect the topology of unknown network may be efficient in practical situations with large network size.

  10. Fluid pipeline system leak detection based on neural network and pattern recognition

    International Nuclear Information System (INIS)

    Tang Xiujia

    1998-01-01

    The mechanism of the stress wave propagation along the pipeline system of NPP, caused by turbulent ejection from pipeline leakage, is researched. A series of characteristic index are described in time domain or frequency domain, and compress numerical algorithm is developed for original data compression. A back propagation neural networks (BPNN) with the input matrix composed by stress wave characteristics in time domain or frequency domain is first proposed to classify various situations of the pipeline, in order to detect the leakage in the fluid flow pipelines. The capability of the new method had been demonstrated by experiments and finally used to design a handy instrument for the pipeline leakage detection. Usually a pipeline system has many inner branches and often in adjusting dynamic condition, it is difficult for traditional pipeline diagnosis facilities to identify the difference between inner pipeline operation and pipeline fault. The author first proposed pipeline wave propagation identification by pattern recognition to diagnose pipeline leak. A series of pattern primitives such as peaks, valleys, horizon lines, capstan peaks, dominant relations, slave relations, etc., are used to extract features of the negative pressure wave form. The context-free grammar of symbolic representation of the negative wave form is used, and a negative wave form parsing system with application to structural pattern recognition based on the representation is first proposed to detect and localize leaks of the fluid pipelines

  11. IMPACT OF η{sub Earth} ON THE CAPABILITIES OF AFFORDABLE SPACE MISSIONS TO DETECT BIOSIGNATURES ON EXTRASOLAR PLANETS

    Energy Technology Data Exchange (ETDEWEB)

    Léger, Alain [IAS, Univ. Paris-Sud, Orsay (France); Defrère, Denis [Steward Observatory, Department of Astronomy, University of Arizona, 933 N. Cherry Ave, Tucson, AZ 85721 (United States); Malbet, Fabien [UJF-Grenoble 1/CNRS-INSU, Institut de Planétologie et d’Astrophysique de Grenoble (IPAG), UMR 5274, BP 53, F-38041 Grenoble cedex 9 (France); Labadie, Lucas [I. Physikalisches Institut der Universität zu Köln, Zülpicher Str. 77, D-50937 Cologne (Germany); Absil, Olivier, E-mail: Alain.Leger@ias.u-psud.fr [Département d’Astrophysique, Géophysique and Océanographie, Université de Liège, 17 Allée du Six Août, B-4000 Liège (Belgium)

    2015-08-01

    We present an analytic model to estimate the capabilities of space missions dedicated to the search for biosignatures in the atmosphere of rocky planets located in the habitable zone of nearby stars. Relations between performance and mission parameters, such as mirror diameter, distance to targets, and radius of planets, are obtained. Two types of instruments are considered: coronagraphs observing in the visible, and nulling interferometers in the thermal infrared. Missions considered are: single-pupil coronagraphs with a 2.4 m primary mirror, and formation-flying interferometers with 4 × 0.75 m collecting mirrors. The numbers of accessible planets are calculated as a function of η{sub Earth}. When Kepler gives its final estimation for η{sub Earth}, the model will permit a precise assessment of the potential of each instrument. Based on current estimations, η{sub Earth} = 10% around FGK stars and 50% around M stars, the coronagraph could study in spectroscopy only ∼1.5 relevant planets, and the interferometer ∼14.0. These numbers are obtained under the major hypothesis that the exozodiacal light around the target stars is low enough for each instrument. In both cases, a prior detection of planets is assumed and a target list established. For the long-term future, building both types of spectroscopic instruments, and using them on the same targets, will be the optimal solution because they provide complementary information. But as a first affordable space mission, the interferometer looks the more promising in terms of biosignature harvest.

  12. Anomaly Detection in SCADA Systems - A Network Based Approach

    NARCIS (Netherlands)

    Barbosa, R.R.R.

    2014-01-01

    Supervisory Control and Data Acquisition (SCADA) networks are commonly deployed to aid the operation of large industrial facilities, such as water treatment facilities. Historically, these networks were composed by special-purpose embedded devices communicating through proprietary protocols.

  13. Anomaly detection in SCADA systems: a network based approach

    NARCIS (Netherlands)

    Barbosa, R.R.R.

    2014-01-01

    Supervisory Control and Data Acquisition (SCADA) networks are commonly deployed to aid the operation of large industrial facilities, such as water treatment facilities. Historically, these networks were composed by special-purpose embedded devices communicating through proprietary protocols.

  14. Local area networks in radiation detection systems: advantages and pitfalls

    Energy Technology Data Exchange (ETDEWEB)

    Blaauw, M [Interfaculty Reactor Inst., Delft Univ. of Technology (Netherlands); Lindstrom, R M [Inorganic Analytical Research Div., National Inst. of Standards and Technology, Gaithersburg, MD (United States)

    1993-06-01

    Both at the Interfaculty Reactor Institute (IRI) and at the National Institute of Standards and Technology (NIST), local area networks are being used to acquire and process data from multiple [gamma]-ray spectrometers. The IRI system was only recently set up. A comparison is made between the NIST network, the old IRI network and the new IRI network, resulting in recommendations for new systems to be set up. (orig.)

  15. Will available bit rate (ABR) services give us the capability to offer virtual LANs over wide-area ATM networks?

    Science.gov (United States)

    Ferrandiz, Ana; Scallan, Gavin

    1995-10-01

    The available bit rate (ABR) service allows connections to exceed their negotiated data rates during the life of the connections when excess capacity is available in the network. These connections are subject to flow control from the network in the event of network congestion. The ability to dynamically adjust the data rate of the connection can provide improved utilization of the network and be a valuable service to end users. ABR type service is therefore appropriate for the transmission of bursty LAN traffic over a wide area network in a manner that is more efficient and cost effective than allocating bandwdith at the peak cell rate. This paper describes the ABR service and discusses if it is realistic to operate a LAN like service over a wide area using ABR.

  16. Reliability–based economic model predictive control for generalised flow–based networks including actuators’ health–aware capabilities

    Directory of Open Access Journals (Sweden)

    Grosso Juan M.

    2016-09-01

    Full Text Available This paper proposes a reliability-based economic model predictive control (MPC strategy for the management of generalised flow-based networks, integrating some ideas on network service reliability, dynamic safety stock planning, and degradation of equipment health. The proposed strategy is based on a single-layer economic optimisation problem with dynamic constraints, which includes two enhancements with respect to existing approaches. The first enhancement considers chance-constraint programming to compute an optimal inventory replenishment policy based on a desired risk acceptability level, leading to dynamical allocation of safety stocks in flow-based networks to satisfy non-stationary flow demands. The second enhancement computes a smart distribution of the control effort and maximises actuators’ availability by estimating their degradation and reliability. The proposed approach is illustrated with an application of water transport networks using the Barcelona network as the case study considered.

  17. Proposed Network Intrusion Detection System ‎In Cloud Environment Based on Back ‎Propagation Neural Network

    Directory of Open Access Journals (Sweden)

    Shawq Malik Mehibs

    2017-12-01

    Full Text Available Cloud computing is distributed architecture, providing computing facilities and storage resource as a service over the internet. This low-cost service fulfills the basic requirements of users. Because of the open nature and services introduced by cloud computing intruders impersonate legitimate users and misuse cloud resource and services. To detect intruders and suspicious activities in and around the cloud computing environment, intrusion detection system used to discover the illegitimate users and suspicious action by monitors different user activities on the network .this work proposed based back propagation artificial neural network to construct t network intrusion detection in the cloud environment. The proposed module evaluated with kdd99 dataset the experimental results shows promising approach to detect attack with high detection rate and low false alarm rate

  18. Low-Cost Ground Sensor Network for Intrusion Detection

    Science.gov (United States)

    2017-09-01

    their suitability to our research. 1. Wireless Sensor Networks The backend network infrastructure forms the communication links for the network...were not ideal as they were perpetually turned on. Our research considered the backend communication infrastructure and its power requirements when...7 3. Border Patrol— Mobile Situation Awareness Tool (MSAT

  19. Spatial anomaly detection in sensor networks using neighborhood information

    NARCIS (Netherlands)

    Bosman, H.H.W.J.; Iacca, G.; Tejada, A.; Wörtche, H.J.; Liotta, A.

    2016-01-01

    The field of wireless sensor networks (WSNs), embedded systems with sensing and networking capabil- ity, has now matured after a decade-long research effort and technological advances in electronics and networked systems. An important remaining challenge now is to extract meaningful information from

  20. Detecting Statistically Significant Communities of Triangle Motifs in Undirected Networks

    Science.gov (United States)

    2016-04-26

    Systems, Statistics & Management Science, University of Alabama, USA. 1 DISTRIBUTION A: Distribution approved for public release. Contents 1 Summary 5...13 5 Application to Real Networks 18 5.1 2012 FBS Football Schedule Network... football schedule network. . . . . . . . . . . . . . . . . . . . . . 21 14 Stem plot of degree-ordered vertices versus the degree for college football

  1. An investigation of scalable anomaly detection techniques for a large network of Wi-Fi hotspots

    CSIR Research Space (South Africa)

    Machaka, P

    2015-01-01

    Full Text Available . The Neural Networks, Bayesian Networks and Artificial Immune Systems were used for this experiment. Using a set of data extracted from a live network of Wi-Fi hotspots managed by an ISP; we integrated algorithms into a data collection system to detect...

  2. Leakage detection and estimation algorithm for loss reduction in water piping networks

    CSIR Research Space (South Africa)

    Adedeji, KB

    2017-10-01

    Full Text Available the development of efficient algorithms for detecting leakage in water piping networks. Water distribution networks (WDNs) are disperse in nature with numerous number of nodes and branches. Consequently, identifying the segment(s) of the network and the exact...

  3. Vector network analyzer ferromagnetic resonance spectrometer with field differential detection

    Science.gov (United States)

    Tamaru, S.; Tsunegi, S.; Kubota, H.; Yuasa, S.

    2018-05-01

    This work presents a vector network analyzer ferromagnetic resonance (VNA-FMR) spectrometer with field differential detection. This technique differentiates the S-parameter by applying a small binary modulation field in addition to the DC bias field to the sample. By setting the modulation frequency sufficiently high, slow sensitivity fluctuations of the VNA, i.e., low-frequency components of the trace noise, which limit the signal-to-noise ratio of the conventional VNA-FMR spectrometer, can be effectively removed, resulting in a very clean FMR signal. This paper presents the details of the hardware implementation and measurement sequence as well as the data processing and analysis algorithms tailored for the FMR spectrum obtained with this technique. Because the VNA measures a complex S-parameter, it is possible to estimate the Gilbert damping parameter from the slope of the phase variation of the S-parameter with respect to the bias field. We show that this algorithm is more robust against noise than the conventional algorithm based on the linewidth.

  4. A model-guided symbolic execution approach for network protocol implementations and vulnerability detection.

    Science.gov (United States)

    Wen, Shameng; Meng, Qingkun; Feng, Chao; Tang, Chaojing

    2017-01-01

    Formal techniques have been devoted to analyzing whether network protocol specifications violate security policies; however, these methods cannot detect vulnerabilities in the implementations of the network protocols themselves. Symbolic execution can be used to analyze the paths of the network protocol implementations, but for stateful network protocols, it is difficult to reach the deep states of the protocol. This paper proposes a novel model-guided approach to detect vulnerabilities in network protocol implementations. Our method first abstracts a finite state machine (FSM) model, then utilizes the model to guide the symbolic execution. This approach achieves high coverage of both the code and the protocol states. The proposed method is implemented and applied to test numerous real-world network protocol implementations. The experimental results indicate that the proposed method is more effective than traditional fuzzing methods such as SPIKE at detecting vulnerabilities in the deep states of network protocol implementations.

  5. A model-guided symbolic execution approach for network protocol implementations and vulnerability detection.

    Directory of Open Access Journals (Sweden)

    Shameng Wen

    Full Text Available Formal techniques have been devoted to analyzing whether network protocol specifications violate security policies; however, these methods cannot detect vulnerabilities in the implementations of the network protocols themselves. Symbolic execution can be used to analyze the paths of the network protocol implementations, but for stateful network protocols, it is difficult to reach the deep states of the protocol. This paper proposes a novel model-guided approach to detect vulnerabilities in network protocol implementations. Our method first abstracts a finite state machine (FSM model, then utilizes the model to guide the symbolic execution. This approach achieves high coverage of both the code and the protocol states. The proposed method is implemented and applied to test numerous real-world network protocol implementations. The experimental results indicate that the proposed method is more effective than traditional fuzzing methods such as SPIKE at detecting vulnerabilities in the deep states of network protocol implementations.

  6. Protocol vulnerability detection based on network traffic analysis and binary reverse engineering.

    Science.gov (United States)

    Wen, Shameng; Meng, Qingkun; Feng, Chao; Tang, Chaojing

    2017-01-01

    Network protocol vulnerability detection plays an important role in many domains, including protocol security analysis, application security, and network intrusion detection. In this study, by analyzing the general fuzzing method of network protocols, we propose a novel approach that combines network traffic analysis with the binary reverse engineering method. For network traffic analysis, the block-based protocol description language is introduced to construct test scripts, while the binary reverse engineering method employs the genetic algorithm with a fitness function designed to focus on code coverage. This combination leads to a substantial improvement in fuzz testing for network protocols. We build a prototype system and use it to test several real-world network protocol implementations. The experimental results show that the proposed approach detects vulnerabilities more efficiently and effectively than general fuzzing methods such as SPIKE.

  7. Protocol vulnerability detection based on network traffic analysis and binary reverse engineering.

    Directory of Open Access Journals (Sweden)

    Shameng Wen

    Full Text Available Network protocol vulnerability detection plays an important role in many domains, including protocol security analysis, application security, and network intrusion detection. In this study, by analyzing the general fuzzing method of network protocols, we propose a novel approach that combines network traffic analysis with the binary reverse engineering method. For network traffic analysis, the block-based protocol description language is introduced to construct test scripts, while the binary reverse engineering method employs the genetic algorithm with a fitness function designed to focus on code coverage. This combination leads to a substantial improvement in fuzz testing for network protocols. We build a prototype system and use it to test several real-world network protocol implementations. The experimental results show that the proposed approach detects vulnerabilities more efficiently and effectively than general fuzzing methods such as SPIKE.

  8. Combining Host-based and network-based intrusion detection system

    African Journals Online (AJOL)

    These attacks were simulated using hping. The proposed system is implemented in Java. The results show that the proposed system is able to detect attacks both from within (host-based) and outside sources (network-based). Key Words: Intrusion Detection System (IDS), Host-based, Network-based, Signature, Security log.

  9. Towards software-based signature detection for intrusion prevention on the network card

    NARCIS (Netherlands)

    Bos, H.; Huang, Kaiming

    2006-01-01

    CardGuard is a signature detection system for intrusion detection and prevention that scans the entire payload of packets for suspicious patterns and is implemented in software on a network card equiped with an Intel IXP1200 network processor. One card can be used to protect either a single host, or

  10. Fault detection for hydraulic pump based on chaotic parallel RBF network

    Directory of Open Access Journals (Sweden)

    Ma Ning

    2011-01-01

    Full Text Available Abstract In this article, a parallel radial basis function network in conjunction with chaos theory (CPRBF network is presented, and applied to practical fault detection for hydraulic pump, which is a critical component in aircraft. The CPRBF network consists of a number of radial basis function (RBF subnets connected in parallel. The number of input nodes for each RBF subnet is determined by different embedding dimension based on chaotic phase-space reconstruction. The output of CPRBF is a weighted sum of all RBF subnets. It was first trained using the dataset from normal state without fault, and then a residual error generator was designed to detect failures based on the trained CPRBF network. Then, failure detection can be achieved by the analysis of the residual error. Finally, two case studies are introduced to compare the proposed CPRBF network with traditional RBF networks, in terms of prediction and detection accuracy.

  11. Capability Paternalism

    NARCIS (Netherlands)

    Claassen, R.J.G.|info:eu-repo/dai/nl/269266224

    A capability approach prescribes paternalist government actions to the extent that it requires the promotion of specific functionings, instead of the corresponding capabilities. Capability theorists have argued that their theories do not have much of these paternalist implications, since promoting

  12. Distributed Detection with Collisions in a Random, Single-Hop Wireless Sensor Network

    Science.gov (United States)

    2013-05-26

    public release; distribution is unlimited. Distributed detection with collisions in a random, single-hop wireless sensor network The views, opinions...1274 2 ABSTRACT Distributed detection with collisions in a random, single-hop wireless sensor network Report Title We consider the problem of... WIRELESS SENSOR NETWORK Gene T. Whipps?† Emre Ertin† Randolph L. Moses† ?U.S. Army Research Laboratory, Adelphi, MD 20783 †The Ohio State University

  13. bNEAT: a Bayesian network method for detecting epistatic interactions in genome-wide association studies

    Directory of Open Access Journals (Sweden)

    Chen Xue-wen

    2011-07-01

    Full Text Available Abstract Background Detecting epistatic interactions plays a significant role in improving pathogenesis, prevention, diagnosis and treatment of complex human diseases. A recent study in automatic detection of epistatic interactions shows that Markov Blanket-based methods are capable of finding genetic variants strongly associated with common diseases and reducing false positives when the number of instances is large. Unfortunately, a typical dataset from genome-wide association studies consists of very limited number of examples, where current methods including Markov Blanket-based method may perform poorly. Results To address small sample problems, we propose a Bayesian network-based approach (bNEAT to detect epistatic interactions. The proposed method also employs a Branch-and-Bound technique for learning. We apply the proposed method to simulated datasets based on four disease models and a real dataset. Experimental results show that our method outperforms Markov Blanket-based methods and other commonly-used methods, especially when the number of samples is small. Conclusions Our results show bNEAT can obtain a strong power regardless of the number of samples and is especially suitable for detecting epistatic interactions with slight or no marginal effects. The merits of the proposed approach lie in two aspects: a suitable score for Bayesian network structure learning that can reflect higher-order epistatic interactions and a heuristic Bayesian network structure learning method.

  14. An information technology enabled sustainability test-bed (ITEST) for occupancy detection through an environmental sensing network

    Energy Technology Data Exchange (ETDEWEB)

    Dong, Bing; Lam, Khee Poh; Zhang, Rui; Chiou, Yun-Shang [Center for Building Performance and Diagnostics, Carnegie Mellon University, Pittsburgh, PA 15213 (United States); Andrews, Burton; Hoeynck, Michael; Benitez, Diego [Research and Technology Center, Robert BOSCH LLC, Pittsburgh, PA 15212 (United States)

    2010-07-15

    This paper describes a large-scale wireless and wired environmental sensor network test-bed and its application to occupancy detection in an open-plan office building. Detection of occupant presence has been used extensively in built environments for applications such as demand-controlled ventilation and security; however, the ability to discern the actual number of people in a room is beyond the scope of current sensing techniques. To address this problem, a complex sensor network is deployed in the Robert L. Preger Intelligent Workplace comprising a wireless ambient-sensing system, a wired carbon dioxide sensing system, and a wired indoor air quality sensing system. A wired camera network is implemented as well for establishing true occupancy levels to be used as ground truth information for deriving algorithmic relationships with the environment conditions. To our knowledge, this extensive and diverse ambient-sensing infrastructure of the ITEST setup as well as the continuous data-collection capability is unprecedented. Final results indicate that there are significant correlations between measured environmental conditions and occupancy status. An average of 73% accuracy on the occupancy number detection was achieved by Hidden Markov Models during testing periods. This paper serves as an exploration to the research of ITEST for occupancy detection in offices. In addition, its utility extends to a wide variety of other building technology research areas such as human-centered environmental control, security, energy efficient and sustainable green buildings. (author)

  15. Assessment of Performance Measures for Security of the Maritime Transportation Network, Port Security Metrics : Proposed Measurement of Deterrence Capability

    Science.gov (United States)

    2007-01-03

    This report is the thirs in a series describing the development of performance measures pertaining to the security of the maritime transportation network (port security metrics). THe development of measures to guide improvements in maritime security ...

  16. An Integrated Intrusion Detection Model of Cluster-Based Wireless Sensor Network.

    Science.gov (United States)

    Sun, Xuemei; Yan, Bo; Zhang, Xinzhong; Rong, Chuitian

    2015-01-01

    Considering wireless sensor network characteristics, this paper combines anomaly and mis-use detection and proposes an integrated detection model of cluster-based wireless sensor network, aiming at enhancing detection rate and reducing false rate. Adaboost algorithm with hierarchical structures is used for anomaly detection of sensor nodes, cluster-head nodes and Sink nodes. Cultural-Algorithm and Artificial-Fish-Swarm-Algorithm optimized Back Propagation is applied to mis-use detection of Sink node. Plenty of simulation demonstrates that this integrated model has a strong performance of intrusion detection.

  17. Similarity between community structures of different online social networks and its impact on underlying community detection

    Science.gov (United States)

    Fan, W.; Yeung, K. H.

    2015-03-01

    As social networking services are popular, many people may register in more than one online social network. In this paper we study a set of users who have accounts of three online social networks: namely Foursquare, Facebook and Twitter. Community structure of this set of users may be reflected in these three online social networks. Therefore, high correlation between these reflections and the underlying community structure may be observed. In this work, community structures are detected in all three online social networks. Also, we investigate the similarity level of community structures across different networks. It is found that they show strong correlation with each other. The similarity between different networks may be helpful to find a community structure close to the underlying one. To verify this, we propose a method to increase the weights of some connections in networks. With this method, new networks are generated to assist community detection. By doing this, value of modularity can be improved and the new community structure match network's natural structure better. In this paper we also show that the detected community structures of online social networks are correlated with users' locations which are identified on Foursquare. This information may also be useful for underlying community detection.

  18. Local community detection as pattern restoration by attractor dynamics of recurrent neural networks.

    Science.gov (United States)

    Okamoto, Hiroshi

    2016-08-01

    Densely connected parts in networks are referred to as "communities". Community structure is a hallmark of a variety of real-world networks. Individual communities in networks form functional modules of complex systems described by networks. Therefore, finding communities in networks is essential to approaching and understanding complex systems described by networks. In fact, network science has made a great deal of effort to develop effective and efficient methods for detecting communities in networks. Here we put forward a type of community detection, which has been little examined so far but will be practically useful. Suppose that we are given a set of source nodes that includes some (but not all) of "true" members of a particular community; suppose also that the set includes some nodes that are not the members of this community (i.e., "false" members of the community). We propose to detect the community from this "imperfect" and "inaccurate" set of source nodes using attractor dynamics of recurrent neural networks. Community detection by the proposed method can be viewed as restoration of the original pattern from a deteriorated pattern, which is analogous to cue-triggered recall of short-term memory in the brain. We demonstrate the effectiveness of the proposed method using synthetic networks and real social networks for which correct communities are known. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  19. Asymmetric intimacy and algorithm for detecting communities in bipartite networks

    Science.gov (United States)

    Wang, Xingyuan; Qin, Xiaomeng

    2016-11-01

    In this paper, an algorithm to choose a good partition in bipartite networks has been proposed. Bipartite networks have more theoretical significance and broader prospect of application. In view of distinctive structure of bipartite networks, in our method, two parameters are defined to show the relationships between the same type nodes and heterogeneous nodes respectively. Moreover, our algorithm employs a new method of finding and expanding the core communities in bipartite networks. Two kinds of nodes are handled separately and merged, and then the sub-communities are obtained. After that, objective communities will be found according to the merging rule. The proposed algorithm has been simulated in real-world networks and artificial networks, and the result verifies the accuracy and reliability of the parameters on intimacy for our algorithm. Eventually, comparisons with similar algorithms depict that the proposed algorithm has better performance.

  20. A Survey on Distributed Filtering and Fault Detection for Sensor Networks

    Directory of Open Access Journals (Sweden)

    Hongli Dong

    2014-01-01

    Full Text Available In recent years, theoretical and practical research on large-scale networked systems has gained an increasing attention from multiple disciplines including engineering, computer science, and mathematics. Lying in the core part of the area are the distributed estimation and fault detection problems that have recently been attracting growing research interests. In particular, an urgent need has arisen to understand the effects of distributed information structures on filtering and fault detection in sensor networks. In this paper, a bibliographical review is provided on distributed filtering and fault detection problems over sensor networks. The algorithms employed to study the distributed filtering and detection problems are categorised and then discussed. In addition, some recent advances on distributed detection problems for faulty sensors and fault events are also summarized in great detail. Finally, we conclude the paper by outlining future research challenges for distributed filtering and fault detection for sensor networks.

  1. Detection of rainfall-induced landslides on regional seismic networks

    Science.gov (United States)

    Manconi, Andrea; Coviello, Velio; Gariano, Stefano Luigi; Picozzi, Matteo

    2017-04-01

    Seismic techniques are increasingly adopted to detect signals induced by mass movements and to quantitatively evaluate geo-hydrological hazards at different spatial and temporal scales. By analyzing landslide-induced seismicity, it is possible obtaining significant information on the source of the mass wasting, as well as on its dynamics. However, currently only few studies have performed a systematic back analysis on comprehensive catalogues of events to evaluate the performance of proposed algorithms. In this work, we analyze a catalogue of 1058 landslides induced by rainfall in Italy. Among these phenomena, there are 234 rock falls, 55 debris flows, 54 mud flows, and 715 unspecified shallow landslides. This is a subset of a larger catalogue collected by the Italian research institute for geo-hydrological protection (CNR IRPI) during the period 2000-2014 (Brunetti et al., 2015). For each record, the following information are available: the type of landslide; the geographical location of the landslide (coordinates, site, municipality, province, and 3 classes of geographic accuracy); the temporal information on the landslide occurrence (day, month, year, time, date, and 3 classes of temporal accuracy); the rainfall conditions (rainfall duration and cumulated event rainfall) that have resulted in the landslide. We consider here only rainfall-induced landslides for which exact date and time were known from chronicle information. The analysis of coeval seismic data acquired by regional seismic networks show clear signals in at least 3 stations for 64 events (6% of the total dataset). Among them, 20 are associated to local earthquakes and 2 to teleseisms; 10 are anomalous signals characterized by irregular and impulsive waveforms in both time and frequency domains; 33 signals are likely associated to the landslide occurrence, as they have a cigar-shaped waveform characterized by emerging onsets, duration of several tens of seconds, and low frequencies (1-10 Hz). For

  2. Partial Information Community Detection in a Multilayer Network

    Science.gov (United States)

    2016-06-01

    26 3 Methodology 33 3.1 Topology of the Noordin Top Terrorist Network . . . . . . . . . . . . 33 3.2 Partial Information... Topology of Synthetic Network. . . . . . . . . . . . . . . . . . . 69 4.4 Four Discovery Algorithms Discovering Red Vertices in a Synthetic Network 72 4.5...without their expertise and analysis. I have been lucky enough to have learned from the wonderful faculty of Applied Mathe - matics Department at the Naval

  3. Using Hierarchical Temporal Memory for Detecting Anomalous Network Activity

    Science.gov (United States)

    2008-03-01

    warfare, computer network operations, psychological operations, military deception, and operations security, in concert with specified supporting and...you up short—you were subconsciously predicting something else and were surprised by the mismatch” [3]. Notable neurobiologist Horace Barlow of the...malicious network activity is flagged as abnormal . That is, test data should present the N-HTM network with spatial-temporal patterns that do not match 46

  4. Detecting Rumors Through Modeling Information Propagation Networks in a Social Media Environment.

    Science.gov (United States)

    Liu, Yang; Xu, Songhua; Tourassi, Georgia

    2015-01-01

    In the midst of today's pervasive influence of social media content and activities, information credibility has increasingly become a major issue. Accordingly, identifying false information, e.g. rumors circulated in social media environments, attracts expanding research attention and growing interests. Many previous studies have exploited user-independent features for rumor detection. These prior investigations uniformly treat all users relevant to the propagation of a social media message as instances of a generic entity. Such a modeling approach usually adopts a homogeneous network to represent all users, the practice of which ignores the variety across an entire user population in a social media environment. Recognizing this limitation of modeling methodologies, this study explores user-specific features in a social media environment for rumor detection. The new approach hypothesizes that whether a user tends to spread a rumor is dependent upon specific attributes of the user in addition to content characteristics of the message itself. Under this hypothesis, information propagation patterns of rumors versus those of credible messages in a social media environment are systematically differentiable. To explore and exploit this hypothesis, we develop a new information propagation model based on a heterogeneous user representation for rumor recognition. The new approach is capable of differentiating rumors from credible messages through observing distinctions in their respective propagation patterns in social media. Experimental results show that the new information propagation model based on heterogeneous user representation can effectively distinguish rumors from credible social media content.

  5. Acoustic leak detection at complicated geometrical structures using fuzzy logic and neural networks

    International Nuclear Information System (INIS)

    Hessel, G.; Schmitt, W.; Weiss, F.P.

    1993-10-01

    An acoustic method based on pattern recognition is being developed. During the learning phase, the localization classifier is trained with sound patterns that are generated with simulated leaks at all locations endangered by leak. The patterns are extracted from the signals of an appropriate sensor array. After training unknown leak positions can be recognized through comparison with the training patterns. The experimental part is performed at an acoustic 1:3 model of the reactor vessel and head and at an original VVER-440 reactor in the former NPP Greifswald. The leaks were simulated at the vessel head using mobile sound sources driven either by compressed air, a piezoelectric transmitter or by a thin metal blade excited through a jet of compressed air. The sound patterns of the simulated leaks are simultaneously detected with an AE-sensor array and with high frequency microphones measuring structure-borne sound and airborne sound, respectively. Pattern classifiers based on Fuzzy Pattern Classification (FPC) and Artificial Neural Networks (ANN) are currently tested for validation of the acoustic emission-sensor array (FPC), leak localization via structure-borne sound (FPC) and the leak localization using microphones (ANN). The initial results show the used classifiers principally to be capable of detecting and locating leaks, but they also show that further investigations are necessary to develop a reliable method applicable at NPPs. (orig./HP)

  6. Detecting Network Communities: An Application to Phylogenetic Analysis

    Science.gov (United States)

    Andrade, Roberto F. S.; Rocha-Neto, Ivan C.; Santos, Leonardo B. L.; de Santana, Charles N.; Diniz, Marcelo V. C.; Lobão, Thierry Petit; Goés-Neto, Aristóteles; Pinho, Suani T. R.; El-Hani, Charbel N.

    2011-01-01

    This paper proposes a new method to identify communities in generally weighted complex networks and apply it to phylogenetic analysis. In this case, weights correspond to the similarity indexes among protein sequences, which can be used for network construction so that the network structure can be analyzed to recover phylogenetically useful information from its properties. The analyses discussed here are mainly based on the modular character of protein similarity networks, explored through the Newman-Girvan algorithm, with the help of the neighborhood matrix . The most relevant networks are found when the network topology changes abruptly revealing distinct modules related to the sets of organisms to which the proteins belong. Sound biological information can be retrieved by the computational routines used in the network approach, without using biological assumptions other than those incorporated by BLAST. Usually, all the main bacterial phyla and, in some cases, also some bacterial classes corresponded totally (100%) or to a great extent (>70%) to the modules. We checked for internal consistency in the obtained results, and we scored close to 84% of matches for community pertinence when comparisons between the results were performed. To illustrate how to use the network-based method, we employed data for enzymes involved in the chitin metabolic pathway that are present in more than 100 organisms from an original data set containing 1,695 organisms, downloaded from GenBank on May 19, 2007. A preliminary comparison between the outcomes of the network-based method and the results of methods based on Bayesian, distance, likelihood, and parsimony criteria suggests that the former is as reliable as these commonly used methods. We conclude that the network-based method can be used as a powerful tool for retrieving modularity information from weighted networks, which is useful for phylogenetic analysis. PMID:21573202

  7. Evaluation of Techniques to Detect Significant Network Performance Problems using End-to-End Active Network Measurements

    Energy Technology Data Exchange (ETDEWEB)

    Cottrell, R.Les; Logg, Connie; Chhaparia, Mahesh; /SLAC; Grigoriev, Maxim; /Fermilab; Haro, Felipe; /Chile U., Catolica; Nazir, Fawad; /NUST, Rawalpindi; Sandford, Mark

    2006-01-25

    End-to-End fault and performance problems detection in wide area production networks is becoming increasingly hard as the complexity of the paths, the diversity of the performance, and dependency on the network increase. Several monitoring infrastructures are built to monitor different network metrics and collect monitoring information from thousands of hosts around the globe. Typically there are hundreds to thousands of time-series plots of network metrics which need to be looked at to identify network performance problems or anomalous variations in the traffic. Furthermore, most commercial products rely on a comparison with user configured static thresholds and often require access to SNMP-MIB information, to which a typical end-user does not usually have access. In our paper we propose new techniques to detect network performance problems proactively in close to realtime and we do not rely on static thresholds and SNMP-MIB information. We describe and compare the use of several different algorithms that we have implemented to detect persistent network problems using anomalous variations analysis in real end-to-end Internet performance measurements. We also provide methods and/or guidance for how to set the user settable parameters. The measurements are based on active probes running on 40 production network paths with bottlenecks varying from 0.5Mbits/s to 1000Mbit/s. For well behaved data (no missed measurements and no very large outliers) with small seasonal changes most algorithms identify similar events. We compare the algorithms' robustness with respect to false positives and missed events especially when there are large seasonal effects in the data. Our proposed techniques cover a wide variety of network paths and traffic patterns. We also discuss the applicability of the algorithms in terms of their intuitiveness, their speed of execution as implemented, and areas of applicability. Our encouraging results compare and evaluate the accuracy of our

  8. Automatic Fire Detection: A Survey from Wireless Sensor Network Perspective

    NARCIS (Netherlands)

    Bahrepour, M.; Meratnia, Nirvana; Havinga, Paul J.M.

    2008-01-01

    Automatic fire detection is important for early detection and promptly extinguishing fire. There are ample studies investigating the best sensor combinations and appropriate techniques for early fire detection. In the previous studies fire detection has either been considered as an application of a

  9. Detection of protein complex from protein-protein interaction network using Markov clustering

    International Nuclear Information System (INIS)

    Ochieng, P J; Kusuma, W A; Haryanto, T

    2017-01-01

    Detection of complexes, or groups of functionally related proteins, is an important challenge while analysing biological networks. However, existing algorithms to identify protein complexes are insufficient when applied to dense networks of experimentally derived interaction data. Therefore, we introduced a graph clustering method based on Markov clustering algorithm to identify protein complex within highly interconnected protein-protein interaction networks. Protein-protein interaction network was first constructed to develop geometrical network, the network was then partitioned using Markov clustering to detect protein complexes. The interest of the proposed method was illustrated by its application to Human Proteins associated to type II diabetes mellitus. Flow simulation of MCL algorithm was initially performed and topological properties of the resultant network were analysed for detection of the protein complex. The results indicated the proposed method successfully detect an overall of 34 complexes with 11 complexes consisting of overlapping modules and 20 non-overlapping modules. The major complex consisted of 102 proteins and 521 interactions with cluster modularity and density of 0.745 and 0.101 respectively. The comparison analysis revealed MCL out perform AP, MCODE and SCPS algorithms with high clustering coefficient (0.751) network density and modularity index (0.630). This demonstrated MCL was the most reliable and efficient graph clustering algorithm for detection of protein complexes from PPI networks. (paper)

  10. An Overlapping Communities Detection Algorithm via Maxing Modularity in Opportunistic Networks

    Directory of Open Access Journals (Sweden)

    Gao Zhi-Peng

    2016-01-01

    Full Text Available Community detection in opportunistic networks has been a significant and hot issue, which is used to understand characteristics of networks through analyzing structure of it. Community is used to represent a group of nodes in a network where nodes inside the community have more internal connections than external connections. However, most of the existing community detection algorithms focus on binary networks or disjoint community detection. In this paper, we propose a novel algorithm via maxing modularity of communities (MMCto find overlapping community structure in opportunistic networks. It utilizes contact history of nodes to calculate the relation intensity between nodes. It finds nodes with high relation intensity as the initial community and extend the community with nodes of higher belong degree. The algorithm achieves a rapid and efficient overlapping community detection method by maxing the modularity of community continuously. The experiments prove that MMC is effective for uncovering overlapping communities and it achieves better performance than COPRA and Conductance.

  11. On the reliability of Quake-Catcher Network earthquake detections

    Science.gov (United States)

    Yildirim, Battalgazi; Cochran, Elizabeth S.; Chung, Angela I.; Christensen, Carl M.; Lawrence, Jesse F.

    2015-01-01

    Over the past two decades, there have been several initiatives to create volunteer‐based seismic networks. The Personal Seismic Network, proposed around 1990, used a short‐period seismograph to record earthquake waveforms using existing phone lines (Cranswick and Banfill, 1990; Cranswicket al., 1993). NetQuakes (Luetgert et al., 2010) deploys triaxial Micro‐Electromechanical Systems (MEMS) sensors in private homes, businesses, and public buildings where there is an Internet connection. Other seismic networks using a dense array of low‐cost MEMS sensors are the Community Seismic Network (Clayton et al., 2012; Kohler et al., 2013) and the Home Seismometer Network (Horiuchi et al., 2009). One main advantage of combining low‐cost MEMS sensors and existing Internet connection in public and private buildings over the traditional networks is the reduction in installation and maintenance costs (Koide et al., 2006). In doing so, it is possible to create a dense seismic network for a fraction of the cost of traditional seismic networks (D’Alessandro and D’Anna, 2013; D’Alessandro, 2014; D’Alessandro et al., 2014).

  12. Replica Node Detection Using Enhanced Single Hop Detection with Clonal Selection Algorithm in Mobile Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    L. S. Sindhuja

    2016-01-01

    Full Text Available Security of Mobile Wireless Sensor Networks is a vital challenge as the sensor nodes are deployed in unattended environment and they are prone to various attacks. One among them is the node replication attack. In this, the physically insecure nodes are acquired by the adversary to clone them by having the same identity of the captured node, and the adversary deploys an unpredictable number of replicas throughout the network. Hence replica node detection is an important challenge in Mobile Wireless Sensor Networks. Various replica node detection techniques have been proposed to detect these replica nodes. These methods incur control overheads and the detection accuracy is low when the replica is selected as a witness node. This paper proposes to solve these issues by enhancing the Single Hop Detection (SHD method using the Clonal Selection algorithm to detect the clones by selecting the appropriate witness nodes. The advantages of the proposed method include (i increase in the detection ratio, (ii decrease in the control overhead, and (iii increase in throughput. The performance of the proposed work is measured using detection ratio, false detection ratio, packet delivery ratio, average delay, control overheads, and throughput. The implementation is done using ns-2 to exhibit the actuality of the proposed work.

  13. An Efficient Hierarchy Algorithm for Community Detection in Complex Networks

    Directory of Open Access Journals (Sweden)

    Lili Zhang

    2014-01-01

    Full Text Available Community structure is one of the most fundamental and important topology characteristics of complex networks. The research on community structure has wide applications and is very important for analyzing the topology structure, understanding the functions, finding the hidden properties, and forecasting the time-varying of the networks. This paper analyzes some related algorithms and proposes a new algorithm—CN agglomerative algorithm based on graph theory and the local connectedness of network to find communities in network. We show this algorithm is distributed and polynomial; meanwhile the simulations show it is accurate and fine-grained. Furthermore, we modify this algorithm to get one modified CN algorithm and apply it to dynamic complex networks, and the simulations also verify that the modified CN algorithm has high accuracy too.

  14. Detecting Network Vulnerabilities Through Graph TheoreticalMethods

    Energy Technology Data Exchange (ETDEWEB)

    Cesarz, Patrick; Pomann, Gina-Maria; Torre, Luis de la; Villarosa, Greta; Flournoy, Tamara; Pinar, Ali; Meza Juan

    2007-09-30

    Identifying vulnerabilities in power networks is an important problem, as even a small number of vulnerable connections can cause billions of dollars in damage to a network. In this paper, we investigate a graph theoretical formulation for identifying vulnerabilities of a network. We first try to find the most critical components in a network by finding an optimal solution for each possible cutsize constraint for the relaxed version of the inhibiting bisection problem, which aims to find loosely coupled subgraphs with significant demand/supply mismatch. Then we investigate finding critical components by finding a flow assignment that minimizes the maximum among flow assignments on all edges. We also report experiments on IEEE 30, IEEE 118, and WSCC 179 benchmark power networks.

  15. A two-stage flow-based intrusion detection model for next-generation networks.

    Science.gov (United States)

    Umer, Muhammad Fahad; Sher, Muhammad; Bi, Yaxin

    2018-01-01

    The next-generation network provides state-of-the-art access-independent services over converged mobile and fixed networks. Security in the converged network environment is a major challenge. Traditional packet and protocol-based intrusion detection techniques cannot be used in next-generation networks due to slow throughput, low accuracy and their inability to inspect encrypted payload. An alternative solution for protection of next-generation networks is to use network flow records for detection of malicious activity in the network traffic. The network flow records are independent of access networks and user applications. In this paper, we propose a two-stage flow-based intrusion detection system for next-generation networks. The first stage uses an enhanced unsupervised one-class support vector machine which separates malicious flows from normal network traffic. The second stage uses a self-organizing map which automatically groups malicious flows into different alert clusters. We validated the proposed approach on two flow-based datasets and obtained promising results.

  16. A density-based clustering model for community detection in complex networks

    Science.gov (United States)

    Zhao, Xiang; Li, Yantao; Qu, Zehui

    2018-04-01

    Network clustering (or graph partitioning) is an important technique for uncovering the underlying community structures in complex networks, which has been widely applied in various fields including astronomy, bioinformatics, sociology, and bibliometric. In this paper, we propose a density-based clustering model for community detection in complex networks (DCCN). The key idea is to find group centers with a higher density than their neighbors and a relatively large integrated-distance from nodes with higher density. The experimental results indicate that our approach is efficient and effective for community detection of complex networks.

  17. Building a Capabilities Network to Improve Disaster Preparation Efforts in the European Command (EUCOM) Area of Responsibility

    Science.gov (United States)

    2012-12-01

    She planned and executed a wedding, worked full time and withstood many lonely days and nights as I traversed my way through a master’s degree. For...We believe God has equipped the church - the most diverse social network on the planet - to be at the center of these stories, leveraging time

  18. Quantifying capability of a local seismic network in terms of locations and focal mechanism solutions of weak earthquakes

    Czech Academy of Sciences Publication Activity Database

    Fojtíková, Lucia; Kristeková, M.; Málek, Jiří; Sokos, E.; Csicsay, K.; Zahradník, J.

    2016-01-01

    Roč. 20, č. 1 (2016), 93-106 ISSN 1383-4649 R&D Projects: GA ČR GAP210/12/2336 Institutional support: RVO:67985891 Keywords : Focal-mechanism uncertainty * Little Carpathians * Relative location uncertainty * Seismic network * Uncertainty mapping * Waveform inversion * Weak earthquake s Subject RIV: DC - Siesmology, Volcanology, Earth Structure Impact factor: 1.089, year: 2016

  19. Robust Vehicle Detection in Aerial Images Based on Cascaded Convolutional Neural Networks.

    Science.gov (United States)

    Zhong, Jiandan; Lei, Tao; Yao, Guangle

    2017-11-24

    Vehicle detection in aerial images is an important and challenging task. Traditionally, many target detection models based on sliding-window fashion were developed and achieved acceptable performance, but these models are time-consuming in the detection phase. Recently, with the great success of convolutional neural networks (CNNs) in computer vision, many state-of-the-art detectors have been designed based on deep CNNs. However, these CNN-based detectors are inefficient when applied in aerial image data due to the fact that the existing CNN-based models struggle with small-size object detection and precise localization. To improve the detection accuracy without decreasing speed, we propose a CNN-based detection model combining two independent convolutional neural networks, where the first network is applied to generate a set of vehicle-like regions from multi-feature maps of different hierarchies and scales. Because the multi-feature maps combine the advantage of the deep and shallow convolutional layer, the first network performs well on locating the small targets in aerial image data. Then, the generated candidate regions are fed into the second network for feature extraction and decision making. Comprehensive experiments are conducted on the Vehicle Detection in Aerial Imagery (VEDAI) dataset and Munich vehicle dataset. The proposed cascaded detection model yields high performance, not only in detection accuracy but also in detection speed.

  20. A Computationally Intelligent Approach to the Detection of Wormhole Attacks in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Mohammad Nurul Afsar Shaon

    2017-05-01

    Full Text Available A wormhole attack is one of the most critical and challenging security threats for wireless sensor networks because of its nature and ability to perform concealed malicious activities. This paper proposes an innovative wormhole detection scheme to detect wormhole attacks using computational intelligence and an artificial neural network (ANN. Most wormhole detection schemes reported in the literature assume the sensors are uniformly distributed in a network, and, furthermore, they use statistical and topological information and special hardware for their detection. However, these schemes may perform poorly in non-uniformly distributed networks, and, moreover, they may fail to defend against “out of band” and “in band” wormhole attacks. The aim of the proposed research is to develop a detection scheme that is able to detect all kinds of wormhole attacks in both uniformly and non-uniformly distributed sensor networks. Furthermore, the proposed research does not require any special hardware and causes no significant network overhead throughout the network. Most importantly, the probable location of the malicious nodes can be identified by the proposed ANN based detection scheme. We evaluate the efficacy of the proposed detection scheme in terms of detection accuracy, false positive rate, and false negative rate. The performance of the proposed algorithm is also compared with other machine learning techniques (i.e. SVM and regularized nonlinear logistic regression (LR based detection models. The simulation results show that proposed ANN based algorithm outperforms the SVM or LR based detection schemes in terms of detection accuracy, false positive rate, and false negative rates.

  1. Detecting Hidden Hierarchy of Non Hierarchical Terrorist Networks

    DEFF Research Database (Denmark)

    Memon, Nasrullah

    measures (and combinations of them) to identify key players (important nodes) in terrorist networks. Our recently introduced techniques and algorithms (which are also implemented in the investigative data mining toolkit known as iMiner) will be particularly useful for law enforcement agencies that need...... to analyze terrorist networks and prioritize their targets. Applying recently introduced mathematical methods for constructing the hidden hierarchy of "nonhierarchical" terrorist networks; we present case studies of the terrorist attacks occurred / planned in the past, in order to identify hidden hierarchy...

  2. A REVIEW PAPER ON PROFILE CLONE DETECTION IN SOCIAL NETWORKS

    OpenAIRE

    Akshay Sharma*, Dr. Sanjeev Dhawan, Dr. Kulvinder Singh

    2016-01-01

    An online social network is used day by day. Social networking is one of the trendiest Internet behaviors, with billions of users from around the humanity. The times use up on public networking sites like facebook, twitter or LinkedIn is frequently increasing at a notable rate. At the similar time, peoples fill their online profile with an overload of information that aims at providing a complete and faultless representation of them. Attackers may duplicate a user’s online existence in the sa...

  3. DETECTION AND LOCALIZATION OF MULTIPLE SPOOFING ATTACKERS FOR MOBILE WIRELESS NETWORKS

    Directory of Open Access Journals (Sweden)

    R. Maivizhi

    2015-06-01

    Full Text Available The openness nature of wireless networks allows adversaries to easily launch variety of spoofing attacks and causes havoc in network performance. Recent approaches used Received Signal Strength (RSS traces, which only detect spoofing attacks in mobile wireless networks. However, it is not always desirable to use these methods as RSS values fluctuate significantly over time due to distance, noise and interference. In this paper, we discusses a novel approach, Mobile spOofing attack DEtection and Localization in WIireless Networks (MODELWIN system, which exploits location information about nodes to detect identity-based spoofing attacks in mobile wireless networks. Also, this approach determines the number of attackers who used the same node identity to masquerade as legitimate device. Moreover, multiple adversaries can be localized accurately. By eliminating attackers the proposed system enhances network performance. We have evaluated our technique through simulation using an 802.11 (WiFi network and an 802.15.4 (Zigbee networks. The results prove that MODELWIN can detect spoofing attacks with a very high detection rate and localize adversaries accurately.

  4. Detecting the overlapping and hierarchical community structure in complex networks

    International Nuclear Information System (INIS)

    Lancichinetti, Andrea; Fortunato, Santo; Kertesz, Janos

    2009-01-01

    Many networks in nature, society and technology are characterized by a mesoscopic level of organization, with groups of nodes forming tightly connected units, called communities or modules, that are only weakly linked to each other. Uncovering this community structure is one of the most important problems in the field of complex networks. Networks often show a hierarchical organization, with communities embedded within other communities; moreover, nodes can be shared between different communities. Here, we present the first algorithm that finds both overlapping communities and the hierarchical structure. The method is based on the local optimization of a fitness function. Community structure is revealed by peaks in the fitness histogram. The resolution can be tuned by a parameter enabling different hierarchical levels of organization to be investigated. Tests on real and artificial networks give excellent results.

  5. Functional brain networks underlying detection and integration of disconfirmatory evidence.

    Science.gov (United States)

    Lavigne, Katie M; Metzak, Paul D; Woodward, Todd S

    2015-05-15

    Processing evidence that disconfirms a prior interpretation is a fundamental aspect of belief revision, and has clear social and clinical relevance. This complex cognitive process requires (at minimum) an alerting stage and an integration stage, and in the current functional magnetic resonance imaging (fMRI) study, we used multivariate analysis methodology on two datasets in an attempt to separate these sequentially-activated cognitive stages and link them to distinct functional brain networks. Thirty-nine healthy participants completed one of two versions of an evidence integration experiment involving rating two consecutive animal images, both of which consisted of two intact images of animal faces morphed together at different ratios (e.g., 70/30 bird/dolphin followed by 10/90 bird/dolphin). The two versions of the experiment differed primarily in terms of stimulus presentation and timing, which facilitated functional interpretation of brain networks based on differences in the hemodynamic response shapes between versions. The data were analyzed using constrained principal component analysis for fMRI (fMRI-CPCA), which allows distinct, simultaneously active task-based networks to be separated, and these were interpreted using both temporal (task-based hemodynamic response shapes) and spatial (dominant brain regions) information. Three networks showed increased activity during integration of disconfirmatory relative to confirmatory evidence: (1) a network involved in alerting to the requirement to revise an interpretation, identified as the salience network (dorsal anterior cingulate cortex and bilateral insula); (2) a sensorimotor response-related network (pre- and post-central gyri, supplementary motor area, and thalamus); and (3) an integration network involving rostral prefrontal, orbitofrontal and posterior parietal cortex. These three networks were staggered in their peak activity (alerting, responding, then integrating), but at certain time points (e

  6. Robust Meter Network for Water Distribution Pipe Burst Detection

    OpenAIRE

    Donghwi Jung; Joong Hoon Kim

    2017-01-01

    A meter network is a set of meters installed throughout a water distribution system to measure system variables, such as the pipe flow rate and pressure. In the current hyper-connected world, meter networks are being exposed to meter failure conditions, such as malfunction of the meter’s physical system and communication system failure. Therefore, a meter network’s robustness should be secured for reliable provision of informative meter data. This paper introduces a multi-objective optimal me...

  7. Content-Based Covert Group Detection in Social Networks

    Science.gov (United States)

    2017-09-06

    The students took courses in natural language processing, data mining in various multi-media data sets, text retrieval, text summarization and... mining in social media including: we performed work, on (a) diffusion in social networks, (b) influence maximization in signed social networks, (c...Learning, Information Retrieval, Data Mining and Database. There are 8,293 messages. Our method outperformed state of the art methods based on content

  8. Using Active Networking to Detect and Troubleshoot Issues in Tactical Data Networks

    Science.gov (United States)

    2014-06-01

    networking (SDN) paradigm, which has gained popularity in recent years, has its roots in the idea of programmable networks [6]. By extending the...278–289, Aug. 2011. 67 [13] M. Hicks, P. Kakkar, J. T. Moore, C. A. Gunter, and S. Nettles , “Plan: A programming language for active networks,” ACM

  9. Optimal Seamline Detection for Orthoimage Mosaicking by Combining Deep Convolutional Neural Network and Graph Cuts

    Directory of Open Access Journals (Sweden)

    Li Li

    2017-07-01

    Full Text Available When mosaicking orthoimages, especially in urban areas with various obvious ground objects like buildings, roads, cars or trees, the detection of optimal seamlines is one of the key technologies for creating seamless and pleasant image mosaics. In this paper, we propose a new approach to detect optimal seamlines for orthoimage mosaicking with the use of deep convolutional neural network (CNN and graph cuts. Deep CNNs have been widely used in many fields of computer vision and photogrammetry in recent years, and graph cuts is one of the most widely used energy optimization frameworks. We first propose a deep CNN for land cover semantic segmentation in overlap regions between two adjacent images. Then, the energy cost of each pixel in the overlap regions is defined based on the classification probabilities of belonging to each of the specified classes. To find the optimal seamlines globally, we fuse the CNN-classified energy costs of all pixels into the graph cuts energy minimization framework. The main advantage of our proposed method is that the pixel similarity energy costs between two images are defined using the classification results of the CNN based semantic segmentation instead of using the image informations of color, gradient or texture as traditional methods do. Another advantage of our proposed method is that the semantic informations are fully used to guide the process of optimal seamline detection, which is more reasonable than only using the hand designed features defined to represent the image differences. Finally, the experimental results on several groups of challenging orthoimages show that the proposed method is capable of finding high-quality seamlines among urban and non-urban orthoimages, and outperforms the state-of-the-art algorithms and the commercial software based on the visual comparison, statistical evaluation and quantitative evaluation based on the structural similarity (SSIM index.

  10. Malignancy Detection on Mammography Using Dual Deep Convolutional Neural Networks and Genetically Discovered False Color Input Enhancement.

    Science.gov (United States)

    Teare, Philip; Fishman, Michael; Benzaquen, Oshra; Toledano, Eyal; Elnekave, Eldad

    2017-08-01

    Breast cancer is the most prevalent malignancy in the US and the third highest cause of cancer-related mortality worldwide. Regular mammography screening has been attributed with doubling the rate of early cancer detection over the past three decades, yet estimates of mammographic accuracy in the hands of experienced radiologists remain suboptimal with sensitivity ranging from 62 to 87% and specificity from 75 to 91%. Advances in machine learning (ML) in recent years have demonstrated capabilities of image analysis which often surpass those of human observers. Here we present two novel techniques to address inherent challenges in the application of ML to the domain of mammography. We describe the use of genetic search of image enhancement methods, leading us to the use of a novel form of false color enhancement through contrast limited adaptive histogram equalization (CLAHE), as a method to optimize mammographic feature representation. We also utilize dual deep convolutional neural networks at different scales, for classification of full mammogram images and derivative patches combined with a random forest gating network as a novel architectural solution capable of discerning malignancy with a specificity of 0.91 and a specificity of 0.80. To our knowledge, this represents the first automatic stand-alone mammography malignancy detection algorithm with sensitivity and specificity performance similar to that of expert radiologists.

  11. Super-Resolution Community Detection for Layer-Aggregated Multilayer Networks

    Directory of Open Access Journals (Sweden)

    Dane Taylor

    2017-09-01

    Full Text Available Applied network science often involves preprocessing network data before applying a network-analysis method, and there is typically a theoretical disconnect between these steps. For example, it is common to aggregate time-varying network data into windows prior to analysis, and the trade-offs of this preprocessing are not well understood. Focusing on the problem of detecting small communities in multilayer networks, we study the effects of layer aggregation by developing random-matrix theory for modularity matrices associated with layer-aggregated networks with N nodes and L layers, which are drawn from an ensemble of Erdős–Rényi networks with communities planted in subsets of layers. We study phase transitions in which eigenvectors localize onto communities (allowing their detection and which occur for a given community provided its size surpasses a detectability limit K^{*}. When layers are aggregated via a summation, we obtain K^{*}∝O(sqrt[NL]/T, where T is the number of layers across which the community persists. Interestingly, if T is allowed to vary with L, then summation-based layer aggregation enhances small-community detection even if the community persists across a vanishing fraction of layers, provided that T/L decays more slowly than O(L^{-1/2}. Moreover, we find that thresholding the summation can, in some cases, cause K^{*} to decay exponentially, decreasing by orders of magnitude in a phenomenon we call super-resolution community detection. In other words, layer aggregation with thresholding is a nonlinear data filter enabling detection of communities that are otherwise too small to detect. Importantly, different thresholds generally enhance the detectability of communities having different properties, illustrating that community detection can be obscured if one analyzes network data using a single threshold.

  12. Super-Resolution Community Detection for Layer-Aggregated Multilayer Networks.

    Science.gov (United States)

    Taylor, Dane; Caceres, Rajmonda S; Mucha, Peter J

    2017-01-01

    Applied network science often involves preprocessing network data before applying a network-analysis method, and there is typically a theoretical disconnect between these steps. For example, it is common to aggregate time-varying network data into windows prior to analysis, and the trade-offs of this preprocessing are not well understood. Focusing on the problem of detecting small communities in multilayer networks, we study the effects of layer aggregation by developing random-matrix theory for modularity matrices associated with layer-aggregated networks with N nodes and L layers, which are drawn from an ensemble of Erdős-Rényi networks with communities planted in subsets of layers. We study phase transitions in which eigenvectors localize onto communities (allowing their detection) and which occur for a given community provided its size surpasses a detectability limit K * . When layers are aggregated via a summation, we obtain [Formula: see text], where T is the number of layers across which the community persists. Interestingly, if T is allowed to vary with L , then summation-based layer aggregation enhances small-community detection even if the community persists across a vanishing fraction of layers, provided that T/L decays more slowly than ( L -1/2 ). Moreover, we find that thresholding the summation can, in some cases, cause K * to decay exponentially, decreasing by orders of magnitude in a phenomenon we call super-resolution community detection. In other words, layer aggregation with thresholding is a nonlinear data filter enabling detection of communities that are otherwise too small to detect. Importantly, different thresholds generally enhance the detectability of communities having different properties, illustrating that community detection can be obscured if one analyzes network data using a single threshold.

  13. Hole Detection for Quantifying Connectivity in Wireless Sensor Networks: A Survey

    Directory of Open Access Journals (Sweden)

    Pearl Antil

    2014-01-01

    Full Text Available Owing to random deployment, environmental factors, dynamic topology, and external attacks, emergence of holes in wireless sensor networks is inescapable. Hole is an area in sensor network around which sensors cease to sense or communicate due to drainage of battery or any fault, either temporary or permanent. Holes impair sensing and communication functions of network; thus their identification is a major concern. This paper discusses different types of holes and significance of hole detection in wireless sensor networks. Coverage hole detection schemes have been classified into three categories based on the type of information used by algorithms, computation model, and network dynamics for better understanding. Then, relative strengths and shortcomings of some of the existing coverage hole detection algorithms are discussed. The paper is concluded by highlighting various future research directions.

  14. Fault detection and classification in electrical power transmission system using artificial neural network.

    Science.gov (United States)

    Jamil, Majid; Sharma, Sanjeev Kumar; Singh, Rajveer

    2015-01-01

    This paper focuses on the detection and classification of the faults on electrical power transmission line using artificial neural networks. The three phase currents and voltages of one end are taken as inputs in the proposed scheme. The feed forward neural network along with back propagation algorithm has been employed for detection and classification of the fault for analysis of each of the three phases involved in the process. A detailed analysis with varying number of hidden layers has been performed to validate the choice of the neural network. The simulation results concluded that the present method based on the neural network is efficient in detecting and classifying the faults on transmission lines with satisfactory performances. The different faults are simulated with different parameters to check the versatility of the method. The proposed method can be extended to the Distribution network of the Power System. The various simulations and analysis of signals is done in the MATLAB(®) environment.

  15. Detection and localization of change points in temporal networks with the aid of stochastic block models

    Science.gov (United States)

    De Ridder, Simon; Vandermarliere, Benjamin; Ryckebusch, Jan

    2016-11-01

    A framework based on generalized hierarchical random graphs (GHRGs) for the detection of change points in the structure of temporal networks has recently been developed by Peel and Clauset (2015 Proc. 29th AAAI Conf. on Artificial Intelligence). We build on this methodology and extend it to also include the versatile stochastic block models (SBMs) as a parametric family for reconstructing the empirical networks. We use five different techniques for change point detection on prototypical temporal networks, including empirical and synthetic ones. We find that none of the considered methods can consistently outperform the others when it comes to detecting and locating the expected change points in empirical temporal networks. With respect to the precision and the recall of the results of the change points, we find that the method based on a degree-corrected SBM has better recall properties than other dedicated methods, especially for sparse networks and smaller sliding time window widths.

  16. Computational neural network regression model for Host based Intrusion Detection System

    Directory of Open Access Journals (Sweden)

    Sunil Kumar Gautam

    2016-09-01

    Full Text Available The current scenario of information gathering and storing in secure system is a challenging task due to increasing cyber-attacks. There exists computational neural network techniques designed for intrusion detection system, which provide security to single machine and entire network's machine. In this paper, we have used two types of computational neural network models, namely, Generalized Regression Neural Network (GRNN model and Multilayer Perceptron Neural Network (MPNN model for Host based Intrusion Detection System using log files that are generated by a single personal computer. The simulation results show correctly classified percentage of normal and abnormal (intrusion class using confusion matrix. On the basis of results and discussion, we found that the Host based Intrusion Systems Model (HISM significantly improved the detection accuracy while retaining minimum false alarm rate.

  17. Assessment of Satellite Capabilities to Detect Impacts of Oil and Natural Gas Activity by Analysis of SONGNEX 2015 Aircraft Measurements

    Science.gov (United States)

    Thayer, M. P.; Keutsch, F. N.; Wolfe, G.; St Clair, J. M.; Hanisco, T. F.; Aikin, K. C.; Brown, S. S.; Dubé, W.; Eilerman, S. J.; Gilman, J.; De Gouw, J. A.; Koss, A.; Lerner, B. M.; Neuman, J. A.; Peischl, J.; Ryerson, T. B.; Thompson, C. R.; Veres, P. R.; Warneke, C.; Washenfelder, R. A.; Wild, R. J.; Womack, C.; Yuan, B.; Zarzana, K. J.

    2017-12-01

    In the last decade, the rate of domestic energy production from oil and natural gas has grown dramatically, resulting in increased concurrent emissions of methane and other volatile organic compounds (VOCs). Products of VOC oxidation and radical cycling, such as tropospheric ozone (O3) and secondary organic aerosols (SOA), have detrimental impacts on human health and climate. The ability to monitor these emissions and their impact on atmospheric composition from remote-sensing platforms will benefit public health by improving air quality forecasts and identifying localized drivers of tropospheric pollution. New satellite-based instruments, such as TROPOMI (October 2017 launch) and TEMPO (2019-2021 projected launch), will be capable of measuring chemical species related to energy drilling and production on unprecedented spatial and temporal scales, however there is need for improved assessments of their capabilities with respect to specific applications. We use chemical and physical parameters measured via aircraft in the boundary layer and free troposphere during the Shale Oil and Natural Gas Nexus (SONGNEX 2015) field campaign to view chemical enhancements over tight oil and shale gas basins from a satellite perspective. Our in-situ data are used to calculate the planetary boundary layer contributions to the column densities for formaldehyde, glyoxal, O3, and NO2. We assess the spatial resolution and chemical precisions necessary to resolve various chemical features, and compare these limits to TEMPO and TROPOMI capabilities to show the degree to which their retrievals will be able to discern the signatures of oil and natural gas activity.

  18. A hybrid network-based method for the detection of disease-related genes

    Science.gov (United States)

    Cui, Ying; Cai, Meng; Dai, Yang; Stanley, H. Eugene

    2018-02-01

    Detecting disease-related genes is crucial in disease diagnosis and drug design. The accepted view is that neighbors of a disease-causing gene in a molecular network tend to cause the same or similar diseases, and network-based methods have been recently developed to identify novel hereditary disease-genes in available biomedical networks. Despite the steady increase in the discovery of disease-associated genes, there is still a large fraction of disease genes that remains under the tip of the iceberg. In this paper we exploit the topological properties of the protein-protein interaction (PPI) network to detect disease-related genes. We compute, analyze, and compare the topological properties of disease genes with non-disease genes in PPI networks. We also design an improved random forest classifier based on these network topological features, and a cross-validation test confirms that our method performs better than previous similar studies.

  19. An Improved Topology-Potential-Based Community Detection Algorithm for Complex Network

    Directory of Open Access Journals (Sweden)

    Zhixiao Wang

    2014-01-01

    Full Text Available Topology potential theory is a new community detection theory on complex network, which divides a network into communities by spreading outward from each local maximum potential node. At present, almost all topology-potential-based community detection methods ignore node difference and assume that all nodes have the same mass. This hypothesis leads to inaccuracy of topology potential calculation and then decreases the precision of community detection. Inspired by the idea of PageRank algorithm, this paper puts forward a novel mass calculation method for complex network nodes. A node’s mass obtained by our method can effectively reflect its importance and influence in complex network. The more important the node is, the bigger its mass is. Simulation experiment results showed that, after taking node mass into consideration, the topology potential of node is more accurate, the distribution of topology potential is more reasonable, and the results of community detection are more precise.

  20. On-Line Detection of Distributed Attacks from Space-Time Network Flow Patterns

    National Research Council Canada - National Science Library

    Baras, J. S; Cardenas, A. A; Ramezani, V

    2003-01-01

    .... The directionality of the change in a network flow is assumed to have an objective or target. The particular problem of detecting distributed denial of service attacks from distributed observations is presented as a working framework...

  1. Game theory and extremal optimization for community detection in complex dynamic networks.

    Science.gov (United States)

    Lung, Rodica Ioana; Chira, Camelia; Andreica, Anca

    2014-01-01

    The detection of evolving communities in dynamic complex networks is a challenging problem that recently received attention from the research community. Dynamics clearly add another complexity dimension to the difficult task of community detection. Methods should be able to detect changes in the network structure and produce a set of community structures corresponding to different timestamps and reflecting the evolution in time of network data. We propose a novel approach based on game theory elements and extremal optimization to address dynamic communities detection. Thus, the problem is formulated as a mathematical game in which nodes take the role of players that seek to choose a community that maximizes their profit viewed as a fitness function. Numerical results obtained for both synthetic and real-world networks illustrate the competitive performance of this game theoretical approach.

  2. Ischemia Detection Using Supervised Learning for Hierarchical Neural Networks Based on Kohonen-Maps

    National Research Council Canada - National Science Library

    Vladutu, L

    2001-01-01

    .... The motivation for developing the Supervising Network - Self Organizing Map (sNet-SOM) model is to design computationally effective solutions for the particular problem of ischemia detection and other similar applications...

  3. New Method for Leakage Detection by Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Mohammad Attari

    2018-03-01

    Full Text Available Nowadays water loss has been turned into a global concern and on the other hand the demand for water is increasing. This problem has made the demand management and consumption pattern reform necessary. One of the most important methods for managing water consumption is to decrease the water loss. In this study by using neural networks, a new method is presented to specify the location and quantity of leakages in water distribution networks.  In this method, by producing the training data and applying it to neural network, the network is able to determine approximate location and quantity of nodal leakage with receiving the nodal pressure. Production of training data is carried out by applying assumed leakage to specific nodes in the network and calculating the new nodal pressures. The results show that by minimum use of hydraulic data taken from pressures, not only this method can determine the location of nodal leakages, but also it can specify the amount of leakage on each node with reasonable accuracy.

  4. A Novel Architecture for Intrusion Detection in Mobile Ad hoc Network

    OpenAIRE

    Atul Patel; Ruchi Kansara; Dr. Paresh Virparia

    2011-01-01

    Today’s wireless networks are vulnerable in many ways including illegal use, unauthorized access, denial of service attacks, eavesdropping so called war chalking. These problems are one of the main issues for wider uses of wireless network. On wired network intruder can access by wire but in wireless it has possibilities to access the computer anywhere in neighborhood. However, securing MANETs is highly challenging issue due to their inherent characteristics. Intrusion detection is an importa...

  5. Using Cognitive Control in Software Defined Networking for Port Scan Detection

    Science.gov (United States)

    2017-07-01

    ARL-TR-8059 ● July 2017 US Army Research Laboratory Using Cognitive Control in Software -Defined Networking for Port Scan...Cognitive Control in Software -Defined Networking for Port Scan Detection by Vinod K Mishra Computational and Information Sciences Directorate, ARL...Technical Report 3. DATES COVERED (From - To) 15 June–31 July 2016 4. TITLE AND SUBTITLE Using Cognitive Control in Software -Defined Networking for

  6. Leakage Detection and Estimation Algorithm for Loss Reduction in Water Piping Networks

    OpenAIRE

    Kazeem B. Adedeji; Yskandar Hamam; Bolanle T. Abe; Adnan M. Abu-Mahfouz

    2017-01-01

    Water loss through leaking pipes constitutes a major challenge to the operational service of water utilities. In recent years, increasing concern about the financial loss and environmental pollution caused by leaking pipes has been driving the development of efficient algorithms for detecting leakage in water piping networks. Water distribution networks (WDNs) are disperse in nature with numerous number of nodes and branches. Consequently, identifying the segment(s) of the network and the exa...

  7. GLRT Based Anomaly Detection for Sensor Network Monitoring

    KAUST Repository

    Harrou, Fouzi

    2015-12-07

    Proper operation of antenna arrays requires continuously monitoring their performances. When a fault occurs in an antenna array, the radiation pattern changes and can significantly deviate from the desired design performance specifications. In this paper, the problem of fault detection in linear antenna arrays is addressed within a statistical framework. Specifically, a statistical fault detection method based on the generalized likelihood ratio (GLR) principle is utilized for detecting potential faults in linear antenna arrays. The proposed method relies on detecting deviations in the radiation pattern of the monitored array with respect to a reference (fault-free) one. To assess the abilities of the GLR based fault detection method, three case studies involving different types of faults have been performed. The simulation results clearly illustrate the effectiveness of the GLR-based fault detection method in monitoring the performance of linear antenna arrays.

  8. GLRT Based Anomaly Detection for Sensor Network Monitoring

    KAUST Repository

    Harrou, Fouzi; Sun, Ying

    2015-01-01

    Proper operation of antenna arrays requires continuously monitoring their performances. When a fault occurs in an antenna array, the radiation pattern changes and can significantly deviate from the desired design performance specifications. In this paper, the problem of fault detection in linear antenna arrays is addressed within a statistical framework. Specifically, a statistical fault detection method based on the generalized likelihood ratio (GLR) principle is utilized for detecting potential faults in linear antenna arrays. The proposed method relies on detecting deviations in the radiation pattern of the monitored array with respect to a reference (fault-free) one. To assess the abilities of the GLR based fault detection method, three case studies involving different types of faults have been performed. The simulation results clearly illustrate the effectiveness of the GLR-based fault detection method in monitoring the performance of linear antenna arrays.

  9. A genetic algorithm for optimization of neural network capable of learning to search for food in a maze

    Science.gov (United States)

    Budilova, E. V.; Terekhin, A. T.; Chepurnov, S. A.

    1994-09-01

    A hypothetical neural scheme is proposed that ensures efficient decision making by an animal searching for food in a maze. Only the general structure of the network is fixed; its quantitative characteristics are found by numerical optimization that simulates the process of natural selection. Selection is aimed at maximization of the expected number of descendants, which is directly related to the energy stored during the reproductive cycle. The main parameters to be optimized are the increments of the interneuronal links and the working-memory constants.

  10. The Efficacy of Epidemic Algorithms on Detecting Node Replicas in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Narasimha Shashidhar

    2015-12-01

    Full Text Available A node replication attack against a wireless sensor network involves surreptitious efforts by an adversary to insert duplicate sensor nodes into the network while avoiding detection. Due to the lack of tamper-resistant hardware and the low cost of sensor nodes, launching replication attacks takes little effort to carry out. Naturally, detecting these replica nodes is a very important task and has been studied extensively. In this paper, we propose a novel distributed, randomized sensor duplicate detection algorithm called Discard to detect node replicas in group-deployed wireless sensor networks. Our protocol is an epidemic, self-organizing duplicate detection scheme, which exhibits emergent properties. Epidemic schemes have found diverse applications in distributed computing: load balancing, topology management, audio and video streaming, computing aggregate functions, failure detection, network and resource monitoring, to name a few. To the best of our knowledge, our algorithm is the first attempt at exploring the potential of this paradigm to detect replicas in a wireless sensor network. Through analysis and simulation, we show that our scheme achieves robust replica detection with substantially lower communication, computational and storage requirements than prior schemes in the literature.

  11. Why General Outlier Detection Techniques Do Not Suffice For Wireless Sensor Networks?

    NARCIS (Netherlands)

    Zhang, Y.; Meratnia, Nirvana; Havinga, Paul J.M.

    2009-01-01

    Raw data collected in wireless sensor networks are often unreliable and inaccurate due to noise, faulty sensors and harsh environmental effects. Sensor data that significantly deviate from normal pattern of sensed data are often called outliers. Outlier detection in wireless sensor networks aims at

  12. Design of Detection and Prevention System of Unauthorized Data Sending from the Local Network

    Directory of Open Access Journals (Sweden)

    D. A. Moskvin

    2010-03-01

    Full Text Available Malware often aims at breaking confidentiality. A malicious program gets inside the local network, finds out necessary data and then illegally transfers this data to the intruder. Prevention of unauthorized data transfer requires development of a special software product, which will detect and prevent leak of information from the local network.

  13. Applying long short-term memory recurrent neural networks to intrusion detection

    Directory of Open Access Journals (Sweden)

    Ralf C. Staudemeyer

    2015-07-01

    Full Text Available We claim that modelling network traffic as a time series with a supervised learning approach, using known genuine and malicious behaviour, improves intrusion detection. To substantiate this, we trained long short-term memory (LSTM recurrent neural networks with the training data provided by the DARPA / KDD Cup ’99 challenge. To identify suitable LSTM-RNN network parameters and structure we experimented with various network topologies. We found networks with four memory blocks containing two cells each offer a good compromise between computational cost and detection performance. We applied forget gates and shortcut connections respectively. A learning rate of 0.1 and up to 1,000 epochs showed good results. We tested the performance on all features and on extracted minimal feature sets respectively. We evaluated different feature sets for the detection of all attacks within one network and also to train networks specialised on individual attack classes. Our results show that the LSTM classifier provides superior performance in comparison to results previously published results of strong static classifiers. With 93.82% accuracy and 22.13 cost, LSTM outperforms the winning entries of the KDD Cup ’99 challenge by far. This is due to the fact that LSTM learns to look back in time and correlate consecutive connection records. For the first time ever, we have demonstrated the usefulness of LSTM networks to intrusion detection.

  14. Deep Neural Network for Structural Prediction and Lane Detection in Traffic Scene.

    Science.gov (United States)

    Li, Jun; Mei, Xue; Prokhorov, Danil; Tao, Dacheng

    2017-03-01

    Hierarchical neural networks have been shown to be effective in learning representative image features and recognizing object classes. However, most existing networks combine the low/middle level cues for classification without accounting for any spatial structures. For applications such as understanding a scene, how the visual cues are spatially distributed in an image becomes essential for successful analysis. This paper extends the framework of deep neural networks by accounting for the structural cues in the visual signals. In particular, two kinds of neural networks have been proposed. First, we develop a multitask deep convolutional network, which simultaneously detects the presence of the target and the geometric attributes (location and orientation) of the target with respect to the region of interest. Second, a recurrent neuron layer is adopted for structured visual detection. The recurrent neurons can deal with the spatial distribution of visible cues belonging to an object whose shape or structure is difficult to explicitly define. Both the networks are demonstrated by the practical task of detecting lane boundaries in traffic scenes. The multitask convolutional neural network provides auxiliary geometric information to help the subsequent modeling of the given lane structures. The recurrent neural network automatically detects lane boundaries, including those areas containing no marks, without any explicit prior knowledge or secondary modeling.

  15. Anomaly based intrusion detection for a biometric identification system using neural networks

    CSIR Research Space (South Africa)

    Mgabile, T

    2012-10-01

    Full Text Available detection technique that analyses the fingerprint biometric network traffic for evidence of intrusion. The neural network algorithm that imitates the way a human brain works is used in this study to classify normal traffic and learn the correct traffic...

  16. A Privacy-Preserving Framework for Collaborative Intrusion Detection Networks Through Fog Computing

    DEFF Research Database (Denmark)

    Wang, Yu; Xie, Lin; Li, Wenjuan

    2017-01-01

    Nowadays, cyber threats (e.g., intrusions) are distributed across various networks with the dispersed networking resources. Intrusion detection systems (IDSs) have already become an essential solution to defend against a large amount of attacks. With the development of cloud computing, a modern IDS...

  17. A robust neural network-based approach for microseismic event detection

    KAUST Repository

    Akram, Jubran; Ovcharenko, Oleg; Peter, Daniel

    2017-01-01

    We present an artificial neural network based approach for robust event detection from low S/N waveforms. We use a feed-forward network with a single hidden layer that is tuned on a training dataset and later applied on the entire example dataset

  18. ATLANTIDES: An Architecture for Alert Verification in Network Intrusion Detection Systems

    NARCIS (Netherlands)

    Bolzoni, D.; Crispo, Bruno; Etalle, Sandro

    2007-01-01

    We present an architecture designed for alert verification (i.e., to reduce false positives) in network intrusion-detection systems. Our technique is based on a systematic (and automatic) anomaly-based analysis of the system output, which provides useful context information regarding the network

  19. Probability Model for Data Redundancy Detection in Sensor Networks

    Directory of Open Access Journals (Sweden)

    Suman Kumar

    2009-01-01

    Full Text Available Sensor networks are made of autonomous devices that are able to collect, store, process and share data with other devices. Large sensor networks are often redundant in the sense that the measurements of some nodes can be substituted by other nodes with a certain degree of confidence. This spatial correlation results in wastage of link bandwidth and energy. In this paper, a model for two associated Poisson processes, through which sensors are distributed in a plane, is derived. A probability condition is established for data redundancy among closely located sensor nodes. The model generates a spatial bivariate Poisson process whose parameters depend on the parameters of the two individual Poisson processes and on the distance between the associated points. The proposed model helps in building efficient algorithms for data dissemination in the sensor network. A numerical example is provided investigating the advantage of this model.

  20. User profiling and classification for fraud detection in mobile communications networks

    OpenAIRE

    Hollmén, Jaakko

    2000-01-01

    The topic of this thesis is fraud detection in mobile communications networks by means of user profiling and classification techniques. The goal is to first identify relevant user groups based on call data and then to assign a user to a relevant group. Fraud may be defined as a dishonest or illegal use of services, with the intention to avoid service charges. Fraud detection is an important application, since network operators lose a relevant portion of their revenue to fraud. Whereas the int...