WorldWideScience

Sample records for network detection capability

  1. Detection capability of the Italian network for teleseismic events

    Directory of Open Access Journals (Sweden)

    A. Marchetti

    1994-06-01

    Full Text Available The future GSE experiment is based on a global seismic monitoring system, that should be designed for monitoring compliance with a nuclear test ban treaty. Every country participating in the test will transmit data to the International Data Center. Because of the high quality of data required, we decided to conduct this study in order to determine the set of stations to be used in the experiment. The Italian telemetered seismological network can detect all events of at least magnitude 2.5 whose epicenters are inside the network itself. For external events the situation is different: the capabilíty of detection is conditioned not only by the noise condition of the station, but also by the relative position of epicenter and station. The ING bulletin (January 1991-June 1992 was the data set for the present work. Comparing these data with the National Earthquake Information Center (NEIC bulletin, we established which stations are most reliable in detecting teleseismic events and, moreover, how distance and back-azimuth can influence event detection. Furthermore, we investigated the reliability of the automatic acquisition system in relation to teleseismic event detection.

  2. Earthquake detection capability of the Swiss Seismic Network

    Science.gov (United States)

    Nanjo, K. Z.; Schorlemmer, D.; Woessner, J.; Wiemer, S.; Giardini, D.

    2010-06-01

    A reliable estimate of completeness magnitudes is vital for many seismicity- and hazard-related studies. Here we adopted and further developed the Probability-based Magnitude of Completeness (PMC) method. This method determines network detection completeness (MP) using only empirical data: earthquake catalogue, phase picks and station information. To evaluate the applicability to low- or moderate-seismicity regions, we performed a case study in Switzerland. The Swiss Seismic Network (SSN) at present is recording seismicity with one of the densest networks of broad-band sensors in Europe. Based on data from 1983 January 1 to 2008 March 31, we found strong spatio-temporal variability of network completeness: the highest value of MP in Switzerland at present is 2.5 in the far southwest, close to the national boundary, whereas MP is lower than 1.6 in high-seismicity areas. Thus, events of magnitude 2.5 can be detected in all of Switzerland. We evaluated the temporal evolution of MP for the last 20 yr, showing the successful improvement of the SSN. We next introduced the calculation of uncertainties to the probabilistic method using a bootstrap approach. The results show that the uncertainties in completeness magnitudes are generally less than 0.1 magnitude units, implying that the method generates stable estimates of completeness magnitudes. We explored the possible use of PMC: (1) as a tool to estimate the number of missing earthquakes in moderate-seismicity regions and (2) as a network planning tool with simulation computations of installations of one or more virtual stations to assess the completeness and identify appropriate locations for new station installations. We compared our results with an existing study of the completeness based on detecting the point of deviation from a power law in the earthquake-size distribution. In general, the new approach provides higher estimates of the completeness magnitude than the traditional one. We associate this observation

  3. 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

  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. Intrusion Detection System Requirements. A Capabilities Description in Terms of the Network Monitoring and Assessment Module of CSAP21

    National Research Council Canada - National Science Library

    Metcalf, Therese R; LaPadula, Leonard J

    2000-01-01

    ...) module of the Computer Security Assistance Program for the Twenty-First Century (CSAP21) architecture. The advantage of this approach is that it provides a global and comprehensive context in which to describe intrusion detection system...

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

    DEFF Research Database (Denmark)

    Hu, Yimei; Sørensen, Olav Jull

    implements its blue ocean strategy through purposively build multi-level networks, i.e. an intra network organization and interfirm innovation networks. In order to get more innovation output from external and internal networks, orchestration capability is needed and should be applied both externally...

  7. Network Power Fault Detection

    OpenAIRE

    Siviero, Claudio

    2013-01-01

    Network power fault detection. At least one first network device is instructed to temporarily disconnect from a power supply path of a network, and at least one characteristic of the power supply path of the network is measured at a second network device connected to the network while the at least one first network device is temporarily disconnected from the network

  8. Understanding the Relationship Between Organizational Networking and Network Capability

    Directory of Open Access Journals (Sweden)

    Hutu Raul Alexandru

    2015-07-01

    Full Text Available Organizational networking refers to firms‘ behaviours as activities, routines, practices which enable an organization to make sense of and capitalize on their networks of direct and indirect business relationships. The relationships are more important today when the business environment is more competitive. The firms can develop their organizational networking strategies by developing their network capability which refers to its ability to build, handle and exploit relationships. These capabilities are included in a complex configuration with other capabilities and competencies. The aim of this paper is to explore how network capabilities are structured and we tried to understand how they could be improved in order to obtain higher performance. Achieving a good network position that allows firms to make use of business opportunities is a main strategic aim of firms

  9. Network Detection Theory and Performance

    OpenAIRE

    Smith, Steven T.; Senne, Kenneth D.; Philips, Scott; Kao, Edward K.; Bernstein, Garrett

    2013-01-01

    Network detection is an important capability in many areas of applied research in which data can be represented as a graph of entities and relationships. Oftentimes the object of interest is a relatively small subgraph in an enormous, potentially uninteresting background. This aspect characterizes network detection as a "big data" problem. Graph partitioning and network discovery have been major research areas over the last ten years, driven by interest in internet search, cyber security, soc...

  10. Computational capabilities of graph neural networks.

    Science.gov (United States)

    Scarselli, Franco; Gori, Marco; Tsoi, Ah Chung; Hagenbuchner, Markus; Monfardini, Gabriele

    2009-01-01

    In this paper, we will consider the approximation properties of a recently introduced neural network model called graph neural network (GNN), which can be used to process-structured data inputs, e.g., acyclic graphs, cyclic graphs, and directed or undirected graphs. This class of neural networks implements a function tau(G,n) is an element of IR(m) that maps a graph G and one of its nodes n onto an m-dimensional Euclidean space. We characterize the functions that can be approximated by GNNs, in probability, up to any prescribed degree of precision. This set contains the maps that satisfy a property called preservation of the unfolding equivalence, and includes most of the practically useful functions on graphs; the only known exception is when the input graph contains particular patterns of symmetries when unfolding equivalence may not be preserved. The result can be considered an extension of the universal approximation property established for the classic feedforward neural networks (FNNs). Some experimental examples are used to show the computational capabilities of the proposed model.

  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. Capability of detecting ultraviolet counterparts of gravitational waves with GLUV

    Science.gov (United States)

    Ridden-Harper, Ryan; Tucker, B. E.; Sharp, R.; Gilbert, J.; Petkovic, M.

    2017-12-01

    With the discovery of gravitational waves (GWs), attention has turned towards detecting counterparts to these sources. In discussions on counterpart signatures and multimessenger follow-up strategies to the GW detections, ultraviolet (UV) signatures have largely been neglected, due to UV facilities being limited to SWIFT, which lacks high-cadence UV survey capabilities. In this paper, we examine the UV signatures from merger models for the major GW sources, highlighting the need for further modelling, while presenting requirements and a design for an effective UV survey telescope. Using the u΄-band models as an analogue, we find that a UV survey telescope requires a limiting magnitude of m_{u^' }}(AB)≈ 24 to fully complement the aLIGO range and sky localization. We show that a network of small, balloon-based UV telescopes with a primary mirror diameter of 30 cm could be capable of covering the aLIGO detection distance from ˜60 to 100 per cent for BNS events and ˜40 per cent for the black hole and a neutron star events. The sensitivity of UV emission to initial conditions suggests that a UV survey telescope would provide a unique data set, which can act as an effective diagnostic to discriminate between models.

  13. TCP Performance over Gigabit-Capable Passive Optical Networks

    Science.gov (United States)

    Orozco, Julio; Ros, David

    The deployment of optical access networks is considered by many as the sole solution able to cope with the ever-increasing bandwidth needs of data and media applications. Gigabit-capable Passive Optical Networks (GPON) are being adopted by many operators worldwide as their preferred fiber-to-the-home network architecture. In such systems, the Medium Access Control (MAC) layer is a key aspect of their operation and performance.

  14. 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...... of startups at inception, by 9,161 entrepreneurs, surveyed in Global Entrepreneurship Monitor in 49 countries. Co-creation is found to be reduced by the entrepreneur’s networking in the private sphere of family and friends, but to be benefiting from networking in the public sphere, especially by networking...... with investors and researchers simultaneously. The findings contribute to understanding capability building as embedded in networks around the startup....

  15. Evaluation of maritime emergency rescue capability based on network analysis

    Science.gov (United States)

    Haixiang, Pang; Yijia, Ma; Tianyu, Mao; Shengjing, Liu; Yajie, Zhang

    2017-12-01

    Maritime emergency rescue operations are complex and random, it leads to the complexity of the evaluation of maritime emergency rescue capability. In this paper, we considered the relationship between the evaluation indexes of maritime emergency rescue capability, used Analytic Network Process to determine the weight of each index, took into account the feedback relationship between indicators to determine the index weight, improved the scientific and reliability of the model, and combined with fuzzy comprehensive evaluation to evaluate the rescue capability. According to the evaluation results which combined with the index weight, maritime sector can propose a targeted improvement measures to effectively improve maritime emergency rescue capability.

  16. Digital associative memory neural network with optical learning capability

    Science.gov (United States)

    Watanabe, Minoru; Ohtsubo, Junji

    1994-12-01

    A digital associative memory neural network system with optical learning and recalling capabilities is proposed by using liquid crystal television spatial light modulators and an Optic RAM detector. In spite of the drawback of the limited memory capacity compared with optical analogue associative memory neural network, the proposed optical digital neural network has the advantage of all optical learning and recalling capabilities, thus an all optics network system is easily realized. Some experimental results of the learning and the recalling for character recognitions are presented. This new optical architecture offers compactness of the system and the fast learning and recalling properties. Based on the results, the practical system for the implementation of a faster optical digital associative memory neural network system with ferro-electric liquid crystal SLMs is also proposed.

  17. 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.

  18. Building capability through networking with investors and researchers

    DEFF Research Database (Denmark)

    Wang, Daojuan; Schøtt, Thomas

    -creation is embedded in the network around the starting entrepreneur, we expect. Co-creation benefits from networking with potential investors and with researchers and inventors, we hypothesize, and especially by networking with both investors and researchers concurrently. Co-creation is analyzed in a sample...... of startups at inception, by 9,161 entrepreneurs, surveyed in Global Entrepreneurship Monitor in 49 countries. Co-creation is found to be reduced by the entrepreneur’s networking in the private sphere of family and friends, but to be benefiting from networking in the public sphere, especially by networking...... with investors and researchers simultaneously. The findings contribute to understanding capability building as embedded in networks around the startup....

  19. 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

    1989-01-01

    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.

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

    KAUST Repository

    Shakir, Muhammad

    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.

  1. Anomaly Detection Techniques for Ad Hoc Networks

    Science.gov (United States)

    Cai, Chaoli

    2009-01-01

    Anomaly detection is an important and indispensable aspect of any computer security mechanism. Ad hoc and mobile networks consist of a number of peer mobile nodes that are capable of communicating with each other absent a fixed infrastructure. Arbitrary node movements and lack of centralized control make them vulnerable to a wide variety of…

  2. Doppler weather radar with predictive wind shear detection capabilities

    Science.gov (United States)

    Kuntman, Daryal

    1991-01-01

    The status of Bendix research on Doppler weather radar with predictive wind shear detection capability is given in viewgraph form. Information is given on the RDR-4A, a fully coherent, solid state transmitter having Doppler turbulence capability. Frequency generation data, plans, modifications, system characteristics and certification requirements are covered.

  3. Effects of Network Capabilities on Firm Performance across Cultures

    Directory of Open Access Journals (Sweden)

    Papastamatelou Julie

    2016-03-01

    Full Text Available The purpose of this study is to identify key factors related to network capabilities that enhance the performance of Chinese, Turkish and German firms. Chinese (n = 107, Turkish (n = 129 and German (n = 109 MBA-students completed a questionnaire, based on an earlier version developed by Kenny [2009], which included questions on the respective firm, its performance and network capabilities. The predictors of firm performance varied by country: in China “information sharing” and “trust” were important, in Turkey “network coordination” and in Germany “human capital resources.” In addition, each country had its own specific drivers of firm performance. The findings of this paper should enhance understanding of the cross-cultural differences and assist managers when planning to join foreign corporations.

  4. 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...... networks. It finds a number of common patterns highlighting organizational effects and managerial challenges faced by the companies regarding rapid changes in their networks configurations and capabilities. The paper details the variables determining these changes and suggests how the on-going interplay...

  5. Heterogeneous delivering capability promotes traffic efficiency in complex networks

    Science.gov (United States)

    Zhu, Yan-Bo; Guan, Xiang-Min; Zhang, Xue-Jun

    2015-12-01

    Traffic is one of the most fundamental dynamical processes in networked systems. With the homogeneous delivery capability of nodes, the global dynamic routing strategy proposed by Ling et al. [Phys. Rev. E81, 016113 (2010)] adequately uses the dynamic information during the process and thus it can reach a quite high network capacity. In this paper, based on the global dynamic routing strategy, we proposed a heterogeneous delivery allocation strategy of nodes on scale-free networks with consideration of nodes degree. It is found that the network capacity as well as some other indexes reflecting transportation efficiency are further improved. Our work may be useful for the design of more efficient routing strategies in communication or transportation systems.

  6. 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...... 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 mounted on the dolphin's pen. The echoes were filtered by a bank of continuous constant-Q digital filters...

  7. 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.

  8. 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.

  9. Establishing seismic network capabilities in Haïti

    Science.gov (United States)

    Clouard, Valerie; Saurel, Jean-Marie; Prepetit, Claude; McNamara, Daniel; Hough, Susan; Saint-Louis, Mildor; Altidor, Jean-Robert

    2014-05-01

    The January 12, 2010 earthquake ruptured a poorly instrumented region that is located on a complex, wide, deformed zone on the boundary between the Caribbean Plate and the North American Plate. This event evidenced the need for a permanent seismic network in Haiti. Immediately after the 2010 earthquake, a strong motion network was deployed by USGS and 3 broadband seismometers were installed by the NRCAN. All this instrumentation is still working, however, it is mainly located around Port-au-Prince. In 2011, the UTS (Technical Unit of Seismology) was created by the BME (Mining and Energy Bureau) to take in charge the seismic monitoring of the national territory and a Memorandum of Understanding was signed with IPGP that would help through its Antilles Volcano and Seismic Observatories. After a 2-month training in Martinique of Haitian operators, Earthworm and Seiscomp3 were installed on the UTS server and neighboring country stations were include to the detection network. To enlarge the seismic networks to the whole territory, 10 broadband seismometers and 6 accelerometers were acquired. With these new stations, which will be installed in 2014 in secured places equipped with internet or VSAT antenna and with network code AY, the seismic performance standards for the detection and analysis of earthquakes change: 1) Earthquake detection from 30 seconds to 10, 2) Minimum magnitude threshold from M3.8 to M2.8, and 3) Initial hypocenter error from 5km to less than 2 km. The remaining efforts should focus on permanent and qualified human resources to maintain these networks.

  10. Deep Space Network Capabilities for Receiving Weak Probe Signals

    Science.gov (United States)

    Asmar, Sami; Johnston, Doug; Preston, Robert

    2005-01-01

    Planetary probes can encounter mission scenarios where communication is not favorable during critical maneuvers or emergencies. Launch, initial acquisition, landing, trajectory corrections, safing. Communication challenges due to sub-optimum antenna pointing or transmitted power, amplitude/frequency dynamics, etc. Prevent lock-up on signal and extraction of telemetry. Examples: loss of Mars Observer, nutation of Ulysses, Galileo antenna, Mars Pathfinder and Mars Exploration Rovers Entry, Descent, and Landing, and the Cassini Saturn Orbit Insertion. A Deep Space Network capability to handle such cases has been used successfully to receive signals to characterize the scenario. This paper will describe the capability and highlight the cases of the critical communications for the Mars rovers and Saturn Orbit Insertion and preparation radio tracking of the Huygens probe at (non-DSN) radio telescopes.

  11. Dynamical detection of network communities

    Science.gov (United States)

    Quiles, Marcos G.; Macau, Elbert E. N.; Rubido, Nicolás

    2016-05-01

    A prominent feature of complex networks is the appearance of communities, also known as modular structures. Specifically, communities are groups of nodes that are densely connected among each other but connect sparsely with others. However, detecting communities in networks is so far a major challenge, in particular, when networks evolve in time. Here, we propose a change in the community detection approach. It underlies in defining an intrinsic dynamic for the nodes of the network as interacting particles (based on diffusive equations of motion and on the topological properties of the network) that results in a fast convergence of the particle system into clustered patterns. The resulting patterns correspond to the communities of the network. Since our detection of communities is constructed from a dynamical process, it is able to analyse time-varying networks straightforwardly. Moreover, for static networks, our numerical experiments show that our approach achieves similar results as the methodologies currently recognized as the most efficient ones. Also, since our approach defines an N-body problem, it allows for efficient numerical implementations using parallel computations that increase its speed performance.

  12. An Indoor Video Surveillance System with Intelligent Fall Detection Capability

    Directory of Open Access Journals (Sweden)

    Ming-Chih Chen

    2013-01-01

    Full Text Available This work presents a novel indoor video surveillance system, capable of detecting the falls of humans. The proposed system can detect and evaluate human posture as well. To evaluate human movements, the background model is developed using the codebook method, and the possible position of moving objects is extracted using the background and shadow eliminations method. Extracting a foreground image produces more noise and damage in this image. Additionally, the noise is eliminated using morphological and size filters and this damaged image is repaired. When the image object of a human is extracted, whether or not the posture has changed is evaluated using the aspect ratio and height of a human body. Meanwhile, the proposed system detects a change of the posture and extracts the histogram of the object projection to represent the appearance. The histogram becomes the input vector of K-Nearest Neighbor (K-NN algorithm and is to evaluate the posture of the object. Capable of accurately detecting different postures of a human, the proposed system increases the fall detection accuracy. Importantly, the proposed method detects the posture using the frame ratio and the displacement of height in an image. Experimental results demonstrate that the proposed system can further improve the system performance and the fall down identification accuracy.

  13. Reusable Social Networking Capabilities for an Earth Science Collaboratory

    Science.gov (United States)

    Lynnes, C.; Da Silva, D.; Leptoukh, G. G.; Ramachandran, R.

    2011-12-01

    A vast untapped resource of data, tools, information and knowledge lies within the Earth science community. This is due to the fact that it is difficult to share the full spectrum of these entities, particularly their full context. As a result, most knowledge exchange is through person-to-person contact at meetings, email and journal articles, each of which can support only a limited level of detail. We propose the creation of an Earth Science Collaboratory (ESC): a framework that would enable sharing of data, tools, workflows, results and the contextual knowledge about these information entities. The Drupal platform is well positioned to provide the key social networking capabilities to the ESC. As a proof of concept of a rich collaboration mechanism, we have developed a Drupal-based mechanism for graphically annotating and commenting on results images from analysis workflows in the online Giovanni analysis system for remote sensing data. The annotations can be tagged and shared with others in the community. These capabilities are further supplemented by a Research Notebook capability reused from another online analysis system named Talkoot. The goal is a reusable set of modules that can integrate with variety of other applications either within Drupal web frameworks or at a machine level.

  14. 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

  15. Detecting Hierarchical Structure in Networks

    DEFF Research Database (Denmark)

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

    2012-01-01

    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......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....... 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....

  16. Enhanced Microbial Detection Capabilities by a Rapid Portable Instrument

    Science.gov (United States)

    Morris, Heather; Monaco, Lisa; Wainwright, Norm; Steele, Andrew; Damon, Michael; Schenk, Alison; Stimpson, Eric; Maule, Jake; Effinger, Michael

    2010-01-01

    We present data describing a progression of continuing technology development - from expanding the detection capabilities of the current PTS unit to re-outfitting the instrument with a protein microarray increasing the number of detectable compounds. To illustrate the adaptability of the cartridge format, on-orbit operations data from the ISS demonstrate the detection of the fungal cell wall compound beta-glucan using applicable LOCAD-PTS cartridges. LOCAD-PTS is a handheld device consisting of a spectrophotometer, an onboard pumping mechanism, and data storage capabilities. A suite of interchangeable cartridges lined with four distinct capillaries allow a hydrated sample to mix with necessary reagents in the channels before being pumped to the optical well for spectrophotometric analysis. The reagents housed in one type of cartridge trigger a reaction based on the Limulus Amebocyte Lysate (LAL) assay, which results in the release of paranitroaniline dye. The dye is measured using a 395 nm filter. The LAL assay detects the Gram-negative bacterial cell wall molecule, endotoxin or lipopolysaccharide (LPS). The more dye released, the greater the concentration of endotoxin in the sample. Sampling, quantitative analysis, and data retrieval require less than 20 minutes. This is significantly faster than standard culture-based methods, which require at least a 24 hour incubation period.Using modified cartridges, we demonstrate the detection of Gram negative bacteria with protein microarray technology. Additionally, we provide data from multiple field tests where both standard and advanced PTS technologies were used. These tests investigate the transfer of target microbial molecules from one surface to another. Collectively, these data demonstrate that the new cartridges expand the number of compounds detected by LOCAD-PTS, while maintaining the rapid, in situ analysis characteristic of the instrument. The unit provides relevant data for verifying sterile sample collection

  17. 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

  18. 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.

  19. 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

  20. Artificial intelligence based event detection in wireless sensor networks

    OpenAIRE

    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 recent applications is on fast and reliable identification of out-of-ordinary situations and events. This new functionality of wireless sensor networks is known as event detection. Due to the fact t...

  1. Anomaly Detection Approaches for Communication Networks

    Science.gov (United States)

    Thottan, Marina; Liu, Guanglei; Ji, Chuanyi

    In recent years, network anomaly detection has become an important area for both commercial interests as well as academic research. Applications of anomaly detection typically stem from the perspectives of network monitoring and network security. In network monitoring, a service provider is often interested in capturing such network characteristics as heavy flows, flow size distributions, and the number of distinct flows. In network security, the interest lies in characterizing known or unknown anomalous patterns of an attack or a virus.

  2. A software tool for network intrusion detection

    CSIR Research Space (South Africa)

    Van der Walt, C

    2012-10-01

    Full Text Available This presentation illustrates how a recently developed software tool enables operators to easily monitor a network and detect intrusions without requiring expert knowledge of network intrusion detections....

  3. Network Capabilities in Project-Based Organizations ; A Case Study of Avantor AS

    OpenAIRE

    Hussain, Tahiya; Reinemo, Ine

    2016-01-01

    Masteroppgave(MSc) in Master of Science in Business, Strategy - Handelshøyskolen BI, 2016 The objectives of this thesis are to highlight the important elements and factors for creating and sustaining network capabilities in project-based organizations. A network capability is a firm’s ability to handle the relationships they are embedded in. The purpose is to provide new insight to the existing theory in an attempt to develop theory on network capabilities within the establishe...

  4. 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.

  5. Networked Adaptive Interactive Hybrid Systems (NAIHS) for multiplatform engagement capability

    NARCIS (Netherlands)

    Kester, L.J.H.M.

    2008-01-01

    Advances in network technologies enable distributed systems, operating in complex physical environments, to coordinate their activities over larger areas within shorter time intervals. Some envisioned application domains for such systems are defence, crisis management, traffic management and public

  6. 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.

  7. 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.

  8. Biological network motif detection and evaluation.

    Science.gov (United States)

    Kim, Wooyoung; Li, Min; Wang, Jianxin; Pan, Yi

    2011-01-01

    Molecular level of biological data can be constructed into system level of data as biological networks. Network motifs are defined as over-represented small connected subgraphs in networks and they have been used for many biological applications. Since network motif discovery involves computationally challenging processes, previous algorithms have focused on computational efficiency. However, we believe that the biological quality of network motifs is also very important. We define biological network motifs as biologically significant subgraphs and traditional network motifs are differentiated as structural network motifs in this paper. We develop five algorithms, namely, EDGEGO-BNM, EDGEBETWEENNESS-BNM, NMF-BNM, NMFGO-BNM and VOLTAGE-BNM, for efficient detection of biological network motifs, and introduce several evaluation measures including motifs included in complex, motifs included in functional module and GO term clustering score in this paper. Experimental results show that EDGEGO-BNM and EDGEBETWEENNESS-BNM perform better than existing algorithms and all of our algorithms are applicable to find structural network motifs as well. We provide new approaches to finding network motifs in biological networks. Our algorithms efficiently detect biological network motifs and further improve existing algorithms to find high quality structural network motifs, which would be impossible using existing algorithms. The performances of the algorithms are compared based on our new evaluation measures in biological contexts. We believe that our work gives some guidelines of network motifs research for the biological networks.

  9. Biological network motif detection and evaluation

    Directory of Open Access Journals (Sweden)

    Kim Wooyoung

    2011-12-01

    Full Text Available Abstract Background Molecular level of biological data can be constructed into system level of data as biological networks. Network motifs are defined as over-represented small connected subgraphs in networks and they have been used for many biological applications. Since network motif discovery involves computationally challenging processes, previous algorithms have focused on computational efficiency. However, we believe that the biological quality of network motifs is also very important. Results We define biological network motifs as biologically significant subgraphs and traditional network motifs are differentiated as structural network motifs in this paper. We develop five algorithms, namely, EDGEGO-BNM, EDGEBETWEENNESS-BNM, NMF-BNM, NMFGO-BNM and VOLTAGE-BNM, for efficient detection of biological network motifs, and introduce several evaluation measures including motifs included in complex, motifs included in functional module and GO term clustering score in this paper. Experimental results show that EDGEGO-BNM and EDGEBETWEENNESS-BNM perform better than existing algorithms and all of our algorithms are applicable to find structural network motifs as well. Conclusion We provide new approaches to finding network motifs in biological networks. Our algorithms efficiently detect biological network motifs and further improve existing algorithms to find high quality structural network motifs, which would be impossible using existing algorithms. The performances of the algorithms are compared based on our new evaluation measures in biological contexts. We believe that our work gives some guidelines of network motifs research for the biological networks.

  10. Computer Network Equipment for Intrusion Detection Research

    National Research Council Canada - National Science Library

    Ye, Nong

    2000-01-01

    .... To test the process model, the system-level intrusion detection techniques and the working prototype of the intrusion detection system, a set of computer and network equipment has been purchased...

  11. 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.

  12. 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

  13. Influence of aerosols on off-axis laser detection capabilities

    NARCIS (Netherlands)

    Kusmierczyk-Michulec, J.T.; Schleijpen, H.M.A.

    2009-01-01

    The radiation coming from a laser which operates in the coastal zone can be detected not only when a detector is placed in front of the laser beam but also when it is located outside the main beam direction. The reason is that in a real detection scheme the power collected by a detector not only

  14. 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.

  15. 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.

  16. 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.

  17. Detection of statistically significant network changes in complex biological networks.

    Science.gov (United States)

    Mall, Raghvendra; Cerulo, Luigi; Bensmail, Halima; Iavarone, Antonio; Ceccarelli, Michele

    2017-03-04

    Biological networks contribute effectively to unveil the complex structure of molecular interactions and to discover driver genes especially in cancer context. It can happen that due to gene mutations, as for example when cancer progresses, the gene expression network undergoes some amount of localized re-wiring. The ability to detect statistical relevant changes in the interaction patterns induced by the progression of the disease can lead to the discovery of novel relevant signatures. Several procedures have been recently proposed to detect sub-network differences in pairwise labeled weighted networks. In this paper, we propose an improvement over the state-of-the-art based on the Generalized Hamming Distance adopted for evaluating the topological difference between two networks and estimating its statistical significance. The proposed procedure exploits a more effective model selection criteria to generate p-values for statistical significance and is more efficient in terms of computational time and prediction accuracy than literature methods. Moreover, the structure of the proposed algorithm allows for a faster parallelized implementation. In the case of dense random geometric networks the proposed approach is 10-15x faster and achieves 5-10% higher AUC, Precision/Recall, and Kappa value than the state-of-the-art. We also report the application of the method to dissect the difference between the regulatory networks of IDH-mutant versus IDH-wild-type glioma cancer. In such a case our method is able to identify some recently reported master regulators as well as novel important candidates. We show that our network differencing procedure can effectively and efficiently detect statistical significant network re-wirings in different conditions. When applied to detect the main differences between the networks of IDH-mutant and IDH-wild-type glioma tumors, it correctly selects sub-networks centered on important key regulators of these two different subtypes. In

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

    Science.gov (United States)

    Pei, Sen; Tang, Shaoting; Zheng, Zhiming

    2015-01-01

    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.

  19. 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.

  20. Adaptive clustering algorithm for community detection in complex networks

    Science.gov (United States)

    Ye, Zhenqing; Hu, Songnian; Yu, Jun

    2008-10-01

    Community structure is common in various real-world networks; methods or algorithms for detecting such communities in complex networks have attracted great attention in recent years. We introduced a different adaptive clustering algorithm capable of extracting modules from complex networks with considerable accuracy and robustness. In this approach, each node in a network acts as an autonomous agent demonstrating flocking behavior where vertices always travel toward their preferable neighboring groups. An optimal modular structure can emerge from a collection of these active nodes during a self-organization process where vertices constantly regroup. In addition, we show that our algorithm appears advantageous over other competing methods (e.g., the Newman-fast algorithm) through intensive evaluation. The applications in three real-world networks demonstrate the superiority of our algorithm to find communities that are parallel with the appropriate organization in reality.

  1. Outlier Detection Method Use for the Network Flow Anomaly Detection

    Directory of Open Access Journals (Sweden)

    Rimas Ciplinskas

    2016-06-01

    Full Text Available New and existing methods of cyber-attack detection are constantly being developed and improved because there is a great number of attacks and the demand to protect from them. In prac-tice, current methods of attack detection operates like antivirus programs, i. e. known attacks signatures are created and attacks are detected by using them. These methods have a drawback – they cannot detect new attacks. As a solution, anomaly detection methods are used. They allow to detect deviations from normal network behaviour that may show a new type of attack. This article introduces a new method that allows to detect network flow anomalies by using local outlier factor algorithm. Accom-plished research allowed to identify groups of features which showed the best results of anomaly flow detection according the highest values of precision, recall and F-measure.

  2. Study of capabilities of the HAWC observatory to detect GRBs

    Energy Technology Data Exchange (ETDEWEB)

    Castillo, M A; Salazar, H [FCFM-BUAP, Avenida San Claudio y 18 Sur, Colonia San Manuel, Ciudad Universitaria, Puebla (Mexico); Villasenor, L, E-mail: mcastillomaldonado@yahoo.com [IFM-UMSNH, Edificio C-3, Ciudad Universitaria, Morelia (Mexico)

    2011-04-01

    We describe the simulation of atmospheric air showers originated by gamma rays with energies in the 0.1-1 TeV range. We study the properties of the secondary particles at a height above level of 4100 m corresponding to the height of the array of water Cherenkov detectors (WCDs) of the HAWC (High Altitude Water Cherenkov) Observatory. In particular we study the pile-up effect of the secondary particles as they arrive at the HAWC Observatory to discern the way in which the PMTs of the WCDs of HAWC can distinguish isolated secondary particles as a function of their signal thresholds. This study is relevant to the application of the single-particle counting technique often used to try to detect gamma ray bursts (GRBs) with ground-based experiments.

  3. Detection of Pigment Networks in Dermoscopy Images

    Science.gov (United States)

    Eltayef, Khalid; Li, Yongmin; Liu, Xiaohui

    2017-02-01

    One of the most important structures in dermoscopy images is the pigment network, which is also one of the most challenging and fundamental task for dermatologists in early detection of melanoma. This paper presents an automatic system to detect pigment network from dermoscopy images. The design of the proposed algorithm consists of four stages. First, a pre-processing algorithm is carried out in order to remove the noise and improve the quality of the image. Second, a bank of directional filters and morphological connected component analysis are applied to detect the pigment networks. Third, features are extracted from the detected image, which can be used in the subsequent stage. Fourth, the classification process is performed by applying feed-forward neural network, in order to classify the region as either normal or abnormal skin. The method was tested on a dataset of 200 dermoscopy images from Hospital Pedro Hispano (Matosinhos), and better results were produced compared to previous studies.

  4. Detecting Clusters/Communities in Social Networks.

    Science.gov (United States)

    Hoffman, Michaela; Steinley, Douglas; Gates, Kathleen M; Prinstein, Mitchell J; Brusco, Michael J

    2018-01-01

    Cohen's κ, a similarity measure for categorical data, has since been applied to problems in the data mining field such as cluster analysis and network link prediction. In this paper, a new application is examined: community detection in networks. A new algorithm is proposed that uses Cohen's κ as a similarity measure for each pair of nodes; subsequently, the κ values are then clustered to detect the communities. This paper defines and tests this method on a variety of simulated and real networks. The results are compared with those from eight other community detection algorithms. Results show this new algorithm is consistently among the top performers in classifying data points both on simulated and real networks. Additionally, this is one of the broadest comparative simulations for comparing community detection algorithms to date.

  5. Detecting controlling nodes of boolean regulatory networks.

    Science.gov (United States)

    Schober, Steffen; Kracht, David; Heckel, Reinhard; Bossert, Martin

    2011-10-11

    Boolean models of regulatory networks are assumed to be tolerant to perturbations. That qualitatively implies that each function can only depend on a few nodes. Biologically motivated constraints further show that functions found in Boolean regulatory networks belong to certain classes of functions, for example, the unate functions. It turns out that these classes have specific properties in the Fourier domain. That motivates us to study the problem of detecting controlling nodes in classes of Boolean networks using spectral techniques. We consider networks with unbalanced functions and functions of an average sensitivity less than 23k, where k is the number of controlling variables for a function. Further, we consider the class of 1-low networks which include unate networks, linear threshold networks, and networks with nested canalyzing functions. We show that the application of spectral learning algorithms leads to both better time and sample complexity for the detection of controlling nodes compared with algorithms based on exhaustive search. For a particular algorithm, we state analytical upper bounds on the number of samples needed to find the controlling nodes of the Boolean functions. Further, improved algorithms for detecting controlling nodes in large-scale unate networks are given and numerically studied.

  6. 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...

  7. 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.

  8. Multilayer Statistical Intrusion Detection in Wireless Networks

    Directory of Open Access Journals (Sweden)

    Noureddine Boudriga

    2008-12-01

    Full Text Available 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.

  9. 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...

  10. Detecting global bridges in networks

    OpenAIRE

    Jensen, Pablo; Morini, Matteo; Karsai, Márton; Venturini, Tommaso; Vespignani, Alessandro; Jacomy, Mathieu; Cointet, Jean-Philippe; Mercklé, Pierre; Fleury, Eric

    2015-01-01

    International audience; The identification of nodes occupying important positions in a network structure is crucial for the understanding of the associated real-world system. Usually, betweenness centrality is used to evaluate a node capacity to connect different graph regions. However, we argue here that this measure is not adapted for that task, as it gives equal weight to “local” centers (i.e. nodes of high degree central to a single region) and to “global” bridges, which connect different...

  11. A Comparative Analysis of Fortress (ES520) and Mesh Dynamics’ (4000 Series) Networking Capabilities During Coasts 2007 Field Experiments

    Science.gov (United States)

    2008-03-01

    features such as security, remote management , networking protocol, mobility, transportability, quality of service, and ruggedness. These features...specifications, which include radio layout, network remote management capability, mobility capability, multicast capability, security implementations, and...implementations. In comparing the two products Mesh Dynamics has the advantage in the categories of quality of service, remote management , and mobility. But

  12. Detecting communities through network data

    NARCIS (Netherlands)

    Bruggeman, J.; Traag, V.A.; Uitermark, J.

    2012-01-01

    Social life coalesces into communities through cooperation and conflict. As a case in point, Shwed and Bearman (2010) studied consensus and contention in scientific communities. They used a sophisticated modularity method to detect communities on the basis of scientific citations, which they then

  13. 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…

  14. 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

  15. Community detection based on network communicability

    Science.gov (United States)

    Estrada, Ernesto

    2011-03-01

    We propose a new method for detecting communities based on the concept of communicability between nodes in a complex network. This method, designated as N-ComBa K-means, uses a normalized version of the adjacency matrix to build the communicability matrix and then applies K-means clustering to find the communities in a graph. We analyze how this method performs for some pathological cases found in the analysis of the detection limit of communities and propose some possible solutions on the basis of the analysis of the ratio of local to global densities in graphs. We use four different quality criteria for detecting the best clustering and compare the new approach with the Girvan-Newman algorithm for the analysis of two "classical" networks: karate club and bottlenose dolphins. Finally, we analyze the more challenging case of homogeneous networks with community structure, for which the Girvan-Newman completely fails in detecting any clustering. The N-ComBa K-means approach performs very well in these situations and we applied it to detect the community structure in an international trade network of miscellaneous manufactures of metal having these characteristics. Some final remarks about the general philosophy of community detection are also discussed.

  16. Community detection based on network communicability.

    Science.gov (United States)

    Estrada, Ernesto

    2011-03-01

    We propose a new method for detecting communities based on the concept of communicability between nodes in a complex network. This method, designated as N-ComBa K-means, uses a normalized version of the adjacency matrix to build the communicability matrix and then applies K-means clustering to find the communities in a graph. We analyze how this method performs for some pathological cases found in the analysis of the detection limit of communities and propose some possible solutions on the basis of the analysis of the ratio of local to global densities in graphs. We use four different quality criteria for detecting the best clustering and compare the new approach with the Girvan-Newman algorithm for the analysis of two "classical" networks: karate club and bottlenose dolphins. Finally, we analyze the more challenging case of homogeneous networks with community structure, for which the Girvan-Newman completely fails in detecting any clustering. The N-ComBa K-means approach performs very well in these situations and we applied it to detect the community structure in an international trade network of miscellaneous manufactures of metal having these characteristics. Some final remarks about the general philosophy of community detection are also discussed.

  17. 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...

  18. Community detection by signaling on complex networks

    Science.gov (United States)

    Hu, Yanqing; Li, Menghui; Zhang, Peng; Fan, Ying; di, Zengru

    2008-07-01

    Based on a signaling process of complex networks, a method for identification of community structure is proposed. For a network with n nodes, every node is assumed to be a system which can send, receive, and record signals. Each node is taken as the initial signal source to excite the whole network one time. Then the source node is associated with an n -dimensional vector which records the effects of the signaling process. By this process, the topological relationship of nodes on the network could be transferred into a geometrical structure of vectors in n -dimensional Euclidean space. Then the best partition of groups is determined by F statistics and the final community structure is given by the K -means clustering method. This method can detect community structure both in unweighted and weighted networks. It has been applied to ad hoc networks and some real networks such as the Zachary karate club network and football team network. The results indicate that the algorithm based on the signaling process works well.

  19. Community detection by signaling on complex networks.

    Science.gov (United States)

    Hu, Yanqing; Li, Menghui; Zhang, Peng; Fan, Ying; Di, Zengru

    2008-07-01

    Based on a signaling process of complex networks, a method for identification of community structure is proposed. For a network with n nodes, every node is assumed to be a system which can send, receive, and record signals. Each node is taken as the initial signal source to excite the whole network one time. Then the source node is associated with an n -dimensional vector which records the effects of the signaling process. By this process, the topological relationship of nodes on the network could be transferred into a geometrical structure of vectors in n -dimensional Euclidean space. Then the best partition of groups is determined by F statistics and the final community structure is given by the K -means clustering method. This method can detect community structure both in unweighted and weighted networks. It has been applied to ad hoc networks and some real networks such as the Zachary karate club network and football team network. The results indicate that the algorithm based on the signaling process works well.

  20. Overlapping Community Detection based on Network Decomposition

    Science.gov (United States)

    Ding, Zhuanlian; Zhang, Xingyi; Sun, Dengdi; Luo, Bin

    2016-04-01

    Community detection in complex network has become a vital step to understand the structure and dynamics of networks in various fields. However, traditional node clustering and relatively new proposed link clustering methods have inherent drawbacks to discover overlapping communities. Node clustering is inadequate to capture the pervasive overlaps, while link clustering is often criticized due to the high computational cost and ambiguous definition of communities. So, overlapping community detection is still a formidable challenge. In this work, we propose a new overlapping community detection algorithm based on network decomposition, called NDOCD. Specifically, NDOCD iteratively splits the network by removing all links in derived link communities, which are identified by utilizing node clustering technique. The network decomposition contributes to reducing the computation time and noise link elimination conduces to improving the quality of obtained communities. Besides, we employ node clustering technique rather than link similarity measure to discover link communities, thus NDOCD avoids an ambiguous definition of community and becomes less time-consuming. We test our approach on both synthetic and real-world networks. Results demonstrate the superior performance of our approach both in computation time and accuracy compared to state-of-the-art algorithms.

  1. 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.

  2. Multilingual Text Detection with Nonlinear Neural Network

    Directory of Open Access Journals (Sweden)

    Lin Li

    2015-01-01

    Full Text Available Multilingual text detection in natural scenes is still a challenging task in computer vision. In this paper, we apply an unsupervised learning algorithm to learn language-independent stroke feature and combine unsupervised stroke feature learning and automatically multilayer feature extraction to improve the representational power of text feature. We also develop a novel nonlinear network based on traditional Convolutional Neural Network that is able to detect multilingual text regions in the images. The proposed method is evaluated on standard benchmarks and multilingual dataset and demonstrates improvement over the previous work.

  3. Realistic computer network simulation for network intrusion detection dataset generation

    Science.gov (United States)

    Payer, Garrett

    2015-05-01

    The KDD-99 Cup dataset is dead. While it can continue to be used as a toy example, the age of this dataset makes it all but useless for intrusion detection research and data mining. Many of the attacks used within the dataset are obsolete and do not reflect the features important for intrusion detection in today's networks. Creating a new dataset encompassing a large cross section of the attacks found on the Internet today could be useful, but would eventually fall to the same problem as the KDD-99 Cup; its usefulness would diminish after a period of time. To continue research into intrusion detection, the generation of new datasets needs to be as dynamic and as quick as the attacker. Simply examining existing network traffic and using domain experts such as intrusion analysts to label traffic is inefficient, expensive, and not scalable. The only viable methodology is simulation using technologies including virtualization, attack-toolsets such as Metasploit and Armitage, and sophisticated emulation of threat and user behavior. Simulating actual user behavior and network intrusion events dynamically not only allows researchers to vary scenarios quickly, but enables online testing of intrusion detection mechanisms by interacting with data as it is generated. As new threat behaviors are identified, they can be added to the simulation to make quicker determinations as to the effectiveness of existing and ongoing network intrusion technology, methodology and models.

  4. 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.

  5. 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.

  6. A Machine Learning Approach for Improving the Detection Capabilities at 3C Seismic Stations

    Science.gov (United States)

    Riggelsen, Carsten; Ohrnberger, Matthias

    2014-03-01

    We apply and evaluate a recent machine learning method for the automatic classification of seismic waveforms. The method relies on Dynamic Bayesian Networks (DBN) and supervised learning to improve the detection capabilities at 3C seismic stations. A time-frequency decomposition provides the basis for the required signal characteristics we need in order to derive the features defining typical "signal" and "noise" patterns. Each pattern class is modeled by a DBN, specifying the interrelationships of the derived features in the time-frequency plane. Subsequently, the models are trained using previously labeled segments of seismic data. The DBN models can now be compared against in order to determine the likelihood of new incoming seismic waveform segments to be either signal or noise. As the noise characteristics of seismic stations varies smoothly in time (seasonal variation as well as anthropogenic influence), we accommodate in our approach for a continuous adaptation of the DBN model that is associated with the noise class. Given the difficulty for obtaining a golden standard for real data (ground truth) the proof of concept and evaluation is shown by conducting experiments based on 3C seismic data from the International Monitoring Stations, BOSA and LPAZ.

  7. An artificial bioindicator system for network intrusion detection.

    Science.gov (United States)

    Blum, Christian; Lozano, José A; Davidson, Pedro Pinacho

    2015-01-01

    An artificial bioindicator system is developed in order to solve a network intrusion detection problem. The system, inspired by an ecological approach to biological immune systems, evolves a population of agents that learn to survive in their environment. An adaptation process allows the transformation of the agent population into a bioindicator that is capable of reacting to system anomalies. Two characteristics stand out in our proposal. On the one hand, it is able to discover new, previously unseen attacks, and on the other hand, contrary to most of the existing systems for network intrusion detection, it does not need any previous training. We experimentally compare our proposal with three state-of-the-art algorithms and show that it outperforms the competing approaches on widely used benchmark data.

  8. 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.

  9. Face detection by aggregated Bayesian network classifiers

    NARCIS (Netherlands)

    Pham, T.V.; Worring, M.; Smeulders, A.W.M.

    2002-01-01

    A face detection system is presented. A new classification method using forest-structured Bayesian networks is used. The method is used in an aggregated classifier to discriminate face from non-face patterns. The process of generating non-face patterns is integrated with the construction of the

  10. Detecting Spam at the Network Level

    NARCIS (Netherlands)

    Sperotto, Anna; Vliek, G.; Sadre, R.; Pras, Aiko

    2009-01-01

    Spam is increasingly a core problem affecting network security and performance. Indeed, it has been estimated that 80% of all email messages are spam. Content-based filters are a commonly deployed countermeasure, but the current research focus is now moving towards the early detection of spamming

  11. 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...

  12. 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.

  13. A dynamic evidential network for fall detection.

    Science.gov (United States)

    Aguilar, Paulo Armando Cavalcante; Boudy, Jerome; Istrate, Dan; Dorizzi, Bernadette; Mota, Joao Cesar Moura

    2014-07-01

    This study is part of the development of a remote home healthcare monitoring application designed to detect distress situations through several types of sensors. The multisensor fusion can provide more accurate and reliable information compared to information provided by each sensor separately. Furthermore, data from multiple heterogeneous sensors present in the remote home healthcare monitoring systems have different degrees of imperfection and trust. Among the multisensor fusion methods, Dempster-Shafer theory (DST) is currently considered the most appropriate for representing and processing the imperfect information. Based on a graphical representation of the DST called evidential networks, a structure of heterogeneous data fusion from multiple sensors for fall detection has been proposed. The evidential networks, implemented on our remote medical monitoring platform, are also proposed in this paper to maximize the performance of automatic fall detection and thus make the system more reliable. However, the presence of noise, the variability of recorded signals by the sensors, and the failing or unreliable sensors may thwart the evidential networks performance. In addition, the sensors signals nonstationary nature may degrade the experimental conditions. To compensate the nonstationary effect, the time evolution is considered by introducing the dynamic evidential network which was evaluated by the simulated fall scenarios corresponding to various use cases.

  14. Querying moving objects detected by sensor networks

    CERN Document Server

    Bestehorn, Markus

    2012-01-01

    Declarative query interfaces to Sensor Networks (SN) have become a commodity. These interfaces allow access to SN deployed for collecting data using relational queries. However, SN are not confined to data collection, but may track object movement, e.g., wildlife observation or traffic monitoring. While rational approaches are well suited for data collection, research on ""Moving Object Databases"" (MOD) has shown that relational operators are unsuitable to express information needs on object movement, i.e., spatio-temporal queries. ""Querying Moving Objects Detected by Sensor Networks"" studi

  15. An assessment on the PTS global radionuclide monitoring capabilities to detect the atmospheric traces of nuclear explosions

    Science.gov (United States)

    Becker, Andreas; Wotawa, Gerhard; Auer, Matthias; Krysta, Monika

    2010-05-01

    factor), are assessed. We examine the typical yields of a 1-kt atmospheric explosion for five key nuclides, Barium(Lanthanum)-140, for the 80 stations particulate network, and the four aforementioned gaseous nuclides, Xe-131m, Xe-133, Xe-133m, and Xe-135, for the 40 stations noble-gas network. The second factor (decay & dispersion) is determined by consideration of the half-life time of the respective nuclide and by evaluation of the so called source-receptor-sensitivity (SRS) files generated daily by the CTBTO for each station to diagnose the one-station probability within 5, 10 and 14 days. A one year time period was used (August 2008 to 31 July 2009), which considered samples from the radionuclide particulate and xenon stations, taking into account their detection limits (third factor). It should be noted that the contribution of station No. 35 of the 80 station IMS particulate network, intended for the Indian Subcontinent, was not considered. Despite the obvious sensitivity to the maximum atmospheric transport time allowed from the source to the first detecting station, there is a general observation of the prevailing impact of the meteorological wind patterns for the global distribution and average of the one-station detection probability. Therefore, certain gaps in the tropical belt can only be ‘filled' by extending the allowed transport time or supplementing stations. This is in particular true for the noble gas network that comprises only 50% of the stations. Obviously, adding the xenon monitoring capability at a few of the so far particulate only stations that monitor the ‘gap areas' is a ‘low hanging fruit'. Moreover, we observe that the shorter the half-life time the more the nuclide specific detection limits become relevant. These findings will be elaborated in all required detail in the presentation.

  16. 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...

  17. Detecting eavesdropping activity in fiber optic networks

    Science.gov (United States)

    MacDonald, Gregory G.

    The secure transmission of data is critical to governments, military organizations, financial institutions, health care providers and other enterprises. The primary method of securing in-transit data is though data encryption. A number of encryption methods exist but the fundamental approach is to assume an eavesdropper has access to the encrypted message but does not have the computing capability to decrypt the message in a timely fashion. Essentially, the strength of security depends on the complexity of the encryption method and the resources available to the eavesdropper. The development of future technologies, most notably quantum computers and quantum computing, is often cited as a direct threat to traditional encryption schemes. It seems reasonable that additional effort should be placed on prohibiting the eavesdropper from coming into possession of the encrypted message in the first place. One strategy for denying possession of the encrypted message is to secure the physical layer of the communications path. Because the majority of transmitted information is over fiber-optic networks, it seems appropriate to consider ways of enhancing the integrity and security of the fiber-based physical layer. The purpose of this research is to investigate the properties of light, as they are manifested in single mode fiber, as a means of insuring the integrity and security of the physical layer of a fiber-optic based communication link. Specifically, the approach focuses on the behavior of polarization in single mode fiber, as it is shown to be especially sensitive to fiber geometry. Fiber geometry is necessarily modified during the placement of optical taps. The problem of detecting activity associated with the placement of an optical tap is herein approached as a supervised machine learning anomaly identification task. The inputs include raw polarization measurements along with additional features derived from various visualizations of the raw data (the inputs are

  18. 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

  19. 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

  20. 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.

  1. 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.

  2. Gamma Ray burst detection and localization capabilities of the IBIS/INTEGRAL telescope Compton mode

    Energy Technology Data Exchange (ETDEWEB)

    Marcinkowski, R.; Denis, M. [CBK, Warsaw (Poland); Laurent, Ph.; Goldoni, P. [SAp CEA, Gif sur Yvette (France); APC, UMR, Paris (France); Bulik, T. [CAMK, Warsaw (Poland); Rau, A. [MPE, Garching (Germany)

    2005-07-15

    We present the capabilities of the IBIS/INTEGRAL Compton mode for the detection and localization of GRBs. Based on the example of GRB 030406 we demonstrate that the IBIS Compton mode is able to detect a GRB and (if it is strong enough) localize it with an accuracy of a few degrees. Energetic spectra extraction is also possible in the range from a few hundred keV to a few MeV.

  3. 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.

  4. Automatic detection of emerging threats to computer networks

    CSIR Research Space (South Africa)

    McDonald, A

    2015-10-01

    Full Text Available intrusion detection technology is to detect threats to networked information systems and networking infrastructure in an automated fashion, thereby providing an opportunity to deploy countermeasures. This presentation showcases the research and development...

  5. 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.

  6. Geographic wormhole detection in wireless sensor networks.

    Science.gov (United States)

    Sookhak, Mehdi; Akhundzada, Adnan; Sookhak, Alireza; Eslaminejad, Mohammadreza; Gani, Abdullah; Khurram Khan, Muhammad; Li, Xiong; Wang, Xiaomin

    2015-01-01

    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.

  7. 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.

  8. Simulated apoptosis/neurogenesis regulates learning and memory capabilities of adaptive neural networks.

    Science.gov (United States)

    Chambers, R Andrew; Potenza, Marc N; Hoffman, Ralph E; Miranker, Willard

    2004-04-01

    Characterization of neuronal death and neurogenesis in the adult brain of birds, humans, and other mammals raises the possibility that neuronal turnover represents a special form of neuroplasticity associated with stress responses, cognition, and the pathophysiology and treatment of psychiatric disorders. Multilayer neural network models capable of learning alphabetic character representations via incremental synaptic connection strength changes were used to assess additional learning and memory effects incurred by simulation of coordinated apoptotic and neurogenic events in the middle layer. Using a consistent incremental learning capability across all neurons and experimental conditions, increasing the number of middle layer neurons undergoing turnover increased network learning capacity for new information, and increased forgetting of old information. Simulations also showed that specific patterns of neural turnover based on individual neuronal connection characteristics, or the temporal-spatial pattern of neurons chosen for turnover during new learning impacts new learning performance. These simulations predict that apoptotic and neurogenic events could act together to produce specific learning and memory effects beyond those provided by ongoing mechanisms of connection plasticity in neuronal populations. Regulation of rates as well as patterns of neuronal turnover may serve an important function in tuning the informatic properties of plastic networks according to novel informational demands. Analogous regulation in the hippocampus may provide for adaptive cognitive and emotional responses to novel and stressful contexts, or operate suboptimally as a basis for psychiatric disorders. The implications of these elementary simulations for future biological and neural modeling research on apoptosis and neurogenesis are discussed.

  9. Fault Detection for Quantized Networked Control Systems

    Directory of Open Access Journals (Sweden)

    Wei-Wei Che

    2013-01-01

    Full Text Available The fault detection problem in the finite frequency domain for networked control systems with signal quantization is considered. With the logarithmic quantizer consideration, a quantized fault detection observer is designed by employing a performance index which is used to increase the fault sensitivity in finite frequency domain. The quantized measurement signals are dealt with by utilizing the sector bound method, in which the quantization error is treated as sector-bounded uncertainty. By using the Kalman-Yakubovich-Popov (GKYP Lemma, an iterative LMI-based optimization algorithm is developed for designing the quantized fault detection observer. And a numerical example is given to illustrate the effectiveness of the proposed method.

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

    DEFF Research Database (Denmark)

    Niang, Mohamed; Wæhrens, Brian Vejrum

    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...... and strategic implications of the model emphasize increased interrelations and a need for coordination resulting in rising coordination costs. Decentralized control is viewed a mean to combine the advantages of centralization and decentralization. Originality/Value: While the offshoring of production has widely...

  11. 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.

  12. Case study of the development of a SANDF tactical data link network enabling capability [Journal article

    CSIR Research Space (South Africa)

    Smith

    2011-11-01

    Full Text Available stream_source_info Smith_2011_ABSTARCT ONLY.pdf.txt stream_content_type text/plain stream_size 5823 Content-Encoding ISO-8859-1 stream_name Smith_2011_ABSTARCT ONLY.pdf.txt Content-Type text/plain; charset=ISO-8859... SANDF TACTICAL DATA LINK NETWORK ENABLING CAPABILITY Corn? J. Smith and Jacobus P. Venter * Abstract. In the scope of Tactical Data Links (TDL), the South African National Defence Force (SANDF) started the journey to establish a national TDL...

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

    Science.gov (United States)

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

    2009-07-01

    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.

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

    Energy Technology Data Exchange (ETDEWEB)

    Vega, J.J. [Departamento del Acelerador, Gerencia de Ciencias Ambientales, Instituto Nacional de Investigaciones Nucleares, Apartado Postal 18-1027, Mexico D.F. 11801 (Mexico)], E-mail: jjvc@nuclear.inin.mx; Reynoso, R. [Departamento del Acelerador, Gerencia de Ciencias Ambientales, Instituto Nacional de Investigaciones Nucleares, Apartado Postal 18-1027, Mexico D.F. 11801 (Mexico); Calvet, H. Carrillo [Laboratorio de Dinamica no Lineal, Facultad de Ciencias, Universidad Nacional Autonoma de Mexico, Mexico D.F. 04510 (Mexico)

    2009-07-21

    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.

  15. 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.

  16. 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.

  17. 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

  18. Over a century of detection and quantification capabilities in analytical chemistry--historical overview and trends.

    Science.gov (United States)

    Belter, Magdalena; Sajnóg, Adam; Barałkiewicz, Danuta

    2014-11-01

    The detection limit (LD) and the quantification limit (LQ) are important parameters in the validation process. Estimation of these parameters is especially important when trace and ultra-trace quantities of analyte are to be detected. When the apparatus response from the analyte is below the detection limit, it does not necessarily mean that the analyte is not present in the sample. It may be a message that the analyte concentration could be below the detection capabilities of the instrument or analytical method. By using a more sensitive detector or a different analytical method it is possible to quantitatively determine the analyte in a given sample. The terms associated with detection capabilities have been present in the scientific literature for at least the past 100 years. Numerous terms, definitions and approaches to calculations have been presented during that time period. This paper is an attempt to collect and summarize the principal approaches to the definition and calculation of detection and quantification abilities published from the beginning of 20th century up until the present. Some of the most important methods are described in detail. Furthermore, the authors would like to popularize the knowledge of metrology in chemistry, particularly that part of it which concerns validation of the analytical procedure. Copyright © 2014 Elsevier B.V. All rights reserved.

  19. Methods and applications for detecting structure in complex networks

    Science.gov (United States)

    Leicht, Elizabeth A.

    The use of networks to represent systems of interacting components is now common in many fields including the biological, physical, and social sciences. Network models are widely applicable due to their relatively simple framework of vertices and edges. Network structure, patterns of connection between vertices, impacts both the functioning of networks and processes occurring on networks. However, many aspects of network structure are still poorly understood. This dissertation presents a set of network analysis methods and applications to real-world as well as simulated networks. The methods are divided into two main types: linear algebra formulations and probabilistic mixture model techniques. Network models lend themselves to compact mathematical representation as matrices, making linear algebra techniques useful probes of network structure. We present methods for the detection of two distinct, but related, network structural forms. First, we derive a measure of vertex similarity based upon network structure. The method builds on existing ideas concerning calculation of vertex similarity, but generalizes and extends the scope to large networks. Second, we address the detection of communities or modules in a specific class of networks, directed networks. We propose a method for detecting community structure in directed networks, which is an extension of a community detection method previously only known for undirected networks. Moving away from linear algebra formulations, we propose two methods for network structure detection based on probabilistic techniques. In the first method, we use the machinery of the expectation-maximization (EM) algorithm to probe patterns of connection among vertices in static networks. The technique allows for the detection of a broad range of types of structure in networks. The second method focuses on time evolving networks. We propose an application of the EM algorithm to evolving networks that can reveal significant structural

  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. The architecture of a network level intrusion detection system

    Energy Technology Data Exchange (ETDEWEB)

    Heady, R.; Luger, G.; Maccabe, A.; Servilla, M. [New Mexico Univ., Albuquerque, NM (United States). Dept. of Computer Science

    1990-08-15

    This paper presents the preliminary architecture of a network level intrusion detection system. The proposed system will monitor base level information in network packets (source, destination, packet size, and time), learning the normal patterns and announcing anomalies as they occur. The goal of this research is to determine the applicability of current intrusion detection technology to the detection of network level intrusions. In particular, the authors are investigating the possibility of using this technology to detect and react to worm programs.

  2. On Functional Module Detection in Metabolic Networks

    Directory of Open Access Journals (Sweden)

    Ina Koch

    2013-08-01

    Full Text Available Functional modules of metabolic networks are essential for understanding the metabolism of an organism as a whole. With the vast amount of experimental data and the construction of complex and large-scale, often genome-wide, models, the computer-aided identification of functional modules becomes more and more important. Since steady states play a key role in biology, many methods have been developed in that context, for example, elementary flux modes, extreme pathways, transition invariants and place invariants. Metabolic networks can be studied also from the point of view of graph theory, and algorithms for graph decomposition have been applied for the identification of functional modules. A prominent and currently intensively discussed field of methods in graph theory addresses the Q-modularity. In this paper, we recall known concepts of module detection based on the steady-state assumption, focusing on transition-invariants (elementary modes and their computation as minimal solutions of systems of Diophantine equations. We present the Fourier-Motzkin algorithm in detail. Afterwards, we introduce the Q-modularity as an example for a useful non-steady-state method and its application to metabolic networks. To illustrate and discuss the concepts of invariants and Q-modularity, we apply a part of the central carbon metabolism in potato tubers (Solanum tuberosum as running example. The intention of the paper is to give a compact presentation of known steady-state concepts from a graph-theoretical viewpoint in the context of network decomposition and reduction and to introduce the application of Q-modularity to metabolic Petri net models.

  3. Network anomaly detection system with optimized DS evidence theory.

    Science.gov (United States)

    Liu, Yuan; Wang, Xiaofeng; Liu, Kaiyu

    2014-01-01

    Network anomaly detection has been focused on by more people with the fast development of computer network. Some researchers utilized fusion method and DS evidence theory to do network anomaly detection but with low performance, and they did not consider features of network-complicated and varied. To achieve high detection rate, we present a novel network anomaly detection system with optimized Dempster-Shafer evidence theory (ODS) and regression basic probability assignment (RBPA) function. In this model, we add weights for each sensor to optimize DS evidence theory according to its previous predict accuracy. And RBPA employs sensor's regression ability to address complex network. By four kinds of experiments, we find that our novel network anomaly detection model has a better detection rate, and RBPA as well as ODS optimization methods can improve system performance significantly.

  4. 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.

  5. 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.

  6. Evaluation of Long-Range Lightning Detection Networks Using TRMM/LIS Observations

    Science.gov (United States)

    Rudlosky, Scott D.; Holzworth, Robert H.; Carey, Lawrence D.; Schultz, Chris J.; Bateman, Monte; Cecil, Daniel J.; Cummins, Kenneth L.; Petersen, Walter A.; Blakeslee, Richard J.; Goodman, Steven J.

    2011-01-01

    Recent advances in long-range lightning detection technologies have improved our understanding of thunderstorm evolution in the data sparse oceanic regions. Although the expansion and improvement of long-range lightning datasets have increased their applicability, these applications (e.g., data assimilation, atmospheric chemistry, and aviation weather hazards) require knowledge of the network detection capabilities. Toward this end, the present study evaluates data from the World Wide Lightning Location Network (WWLLN) using observations from the Lightning Imaging Sensor (LIS) aboard the Tropical Rainfall Measurement Mission (TRMM) satellite. The study documents the WWLLN detection efficiency and location accuracy relative to LIS observations, describes the spatial variability in these performance metrics, and documents the characteristics of LIS flashes that are detected by WWLLN. Improved knowledge of the WWLLN detection capabilities will allow researchers, algorithm developers, and operational users to better prepare for the spatial and temporal coverage of the upcoming GOES-R Geostationary Lightning Mapper (GLM).

  7. 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.

  8. 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.

  9. 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.

  10. fraud detection in mobile communications networks using user

    African Journals Online (AJOL)

    DEPT OF AGRICULTURAL ENGINEERING

    Keywords: Call data, fraud detection, neural networks, probabilistic models, user profiling ... Intrusion detection approach can be divided into two classes of .... Raw call data. Call data simulator. SOM Neural. Network. Probabilistic. System. Monitoring. Database. Database. Fig. 3: Mobile communication detection tools.

  11. Wireless Sensor Network for Forest Fire Detection

    Directory of Open Access Journals (Sweden)

    Emansa Hasri Putra

    2013-09-01

    Full Text Available Forest fires are one of problems that threaten sustainability of the forest. Early prevention system for indications of forest fires is absolutely necessary. The extent of the forest to be one of the problems encountered in the forest condition monitoring. To overcome the problems of forest extent, designed a system of forest fire detection system by adopting the Wireless Sensor Network (WSN using multiple sensor nodes. Each sensor node has a microcontroller, transmitter/receiver and three sensors. Measurement method is performed by measuring the temperature, flame, the levels of methane, hydrocarbons, and CO2 in some forest area and the combustion of peat in a simulator. From results of measurements of temperature, levels of methane, a hydrocarbon gas and CO2 in an open area indicates there are no signs of fires due to the value of the temperature, methane, hydrocarbon gas, and CO2 is below the measurement in the space simulator.

  12. 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.

  13. 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.

  14. Emergency response networks for disaster monitoring and detection from space

    Science.gov (United States)

    Vladimirova, Tanya; Sweeting, Martin N.; Vitanov, Ivan; Vitanov, Valentin I.

    2009-05-01

    Numerous man-made and natural disasters have stricken mankind since the beginning of the new millennium. The scale and impact of such disasters often prevent the collection of sufficient data for an objective assessment and coordination of timely rescue and relief missions on the ground. As a potential solution to this problem, in recent years constellations of Earth observation small satellites and in particular micro-satellites (<100 kg) in low Earth orbit have emerged as an efficient platform for reliable disaster monitoring. The main task of the Earth observation satellites is to capture images of the Earth surface using various techniques. For a large number of applications the resulting delay between image capture and delivery is not acceptable, in particular for rapid response remote sensing aiming at disaster monitoring and detection. In such cases almost instantaneous data availability is a strict requirement to enable an assessment of the situation and instigate an adequate response. Examples include earthquakes, volcanic eruptions, flooding, forest fires and oil spills. The proposed solution to this issue are low-cost networked distributed satellite systems in low Earth orbit capable of connecting to terrestrial networks and geostationary Earth orbit spacecraft in real time. This paper discusses enabling technologies for rapid response disaster monitoring and detection from space such as very small satellite design, intersatellite communication, intelligent on-board processing, distributed computing and bio-inspired routing techniques.

  15. 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.

  16. Tuneable drug-loading capability of chitosan hydrogels with varied network architectures

    CERN Document Server

    Tronci, Giuseppe; Russell, Stephen J; Wood, David J; Akashi, Mitsuru

    2013-01-01

    Advanced bioactive systems with defined macroscopic properties and spatio-temporal sequestration of extracellular biomacromolecules are highly desirable for next generation therapeutics. Here, chitosan hydrogels were prepared with neutral or negatively-charged crosslinkers in order to promote selective electrostatic complexation with charged drugs. Chitosan (CT) was functionalised with varied dicarboxylic acids, such as tartaric acid (TA), poly(ethylene glycol) bis(carboxymethyl) ether (PEG), 1.4-Phenylenediacetic acid (4Ph) and 5-Sulfoisophthalic acid monosodium salt (PhS), whereby PhS was hypothesised to act as a simple mimetic of heparin. ATR FT-IR showed the presence of C=O amide I, N-H amide II and C=O ester bands, providing evidence of covalent network formation. The crosslinker content was reversely quantified by 1H-NMR on partially-degraded network oligomers, so that 18 mol% PhS was exemplarily determined. Swellability, compressability, material morphology, and drug-loading capability were successfull...

  17. DETECTING NETWORK ATTACKS IN COMPUTER NETWORKS BY USING DATA MINING METHODS

    OpenAIRE

    Platonov, V. V.; Semenov, P. O.

    2016-01-01

    The article describes an approach to the development of an intrusion detection system for computer networks. It is shown that the usage of several data mining methods and tools can improve the efficiency of protection computer networks against network at-tacks due to the combination of the benefits of signature detection and anomalies detection and the opportunity of adaptation the sys-tem for hardware and software structure of the computer network.

  18. Statement of capabilities: Micropower Impulse Radar (MIR) technology applied to mine detection and imaging

    Energy Technology Data Exchange (ETDEWEB)

    Azevedo, S.G.; Gavel, D.T.; Mast, J.E.; Warhus, J.P.

    1995-03-13

    The Lawrence Livermore National Laboratory (LLNL) has developed radar and imaging technologies with potential applications in mine detection by the armed forces and other agencies involved in demining efforts. These new technologies use a patented ultra-wideband (impulse) radar technology that is compact, low-cost, and low power. Designated as Micropower Impulse Radar, these compact, self-contained radars can easily be assembled into arrays to form complete ground penetrating radar imaging systems. LLNL has also developed tomographic reconstruction and signal processing software capable of producing high-resolution 2-D and 3-D images of objects buried in materials like soil or concrete from radar data. Preliminary test results have shown that a radar imaging system using these technologies has the ability to image both metallic and plastic land mine surrogate targets buried in 5 to 10 cm of moist soil. In dry soil, the system can detect buried objects to a depth of 30 cm and more. This report describes LLNL`s unique capabilities and technologies that can be applied to the demining problem.

  19. Probablilistic evaluation of earthquake detection and location capability for Illinois, Indiana, Kentucky, Ohio, and West Virginia

    Energy Technology Data Exchange (ETDEWEB)

    Mauk, F.J.; Christensen, D.H.

    1980-09-01

    Probabilistic estimations of earthquake detection and location capabilities for the states of Illinois, Indiana, Kentucky, Ohio and West Virginia are presented in this document. The algorithm used in these epicentrality and minimum-magnitude estimations is a version of the program NETWORTH by Wirth, Blandford, and Husted (DARPA Order No. 2551, 1978) which was modified for local array evaluation at the University of Michigan Seismological Observatory. Estimations of earthquake detection capability for the years 1970 and 1980 are presented in four regional minimum m/sub b/ magnitude contour maps. Regional 90% confidence error ellipsoids are included for m/sub b/ magnitude events from 2.0 through 5.0 at 0.5 m/sub b/ unit increments. The close agreement between these predicted epicentral 90% confidence estimates and the calculated error ellipses associated with actual earthquakes within the studied region suggest that these error determinations can be used to estimate the reliability of epicenter location. 8 refs., 14 figs., 2 tabs.

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

    DEFF Research Database (Denmark)

    Jiang, Jiuchuan; Jaeger, Manfred

    2014-01-01

    . In this paper we propose to use relational Bayesian networks for the specification of probabilistic network models, and develop inference techniques that solve the community detection problem based on these models. The use of relational Bayesian networks as a flexible high-level modeling framework enables us......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...

  1. 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.

  2. 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.

  3. 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

  4. 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.

  5. Managing Supply Chain Networks: A Framework for Achieving Superior Performance through Leadership Capabilities Development in Supply Chain Node

    OpenAIRE

    Cooper, Sharp; Watson, Derek; Worrall, Rob

    2016-01-01

    Leadership capability is acknowledged as a major challenge for organizations and a pre-requisite for sustaining high levels of organizational performance and supply chain competitiveness. Recent research highlights how globalisation has led to the extension of domestic supply chains, particularly SME ones, to include both suppliers and customers globally. This paper examines the role capabilities development in managers and leaders as nexus of their supply chain networks have to play in achie...

  6. 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.

  7. Modern Community Detection Methods in Social Networks

    Directory of Open Access Journals (Sweden)

    V. O. Chesnokov

    2017-01-01

    Full Text Available Social network structure is not homogeneous. Groups of vertices which have a lot of links between them are called communities. A survey of algorithms discovering such groups is presented in the article.A popular approach to community detection is to use an graph clustering algorithm.  Methods based on inner metric optimization are common. 5 groups of algorithms are listed: based on optimization, joining vertices into clusters by some closeness measure, special subgraphs discovery, partitioning graph by deleting edges,  and based on a dynamic process or generative model.Overlapping community detection algorithms are usually just modified graph clustering algorithms. Other approaches do exist, e.g. ones based on edges clustering or constructing communities around randomly chosen vertices. Methods based on nonnegative matrix factorization are also used, but they have high computational complexity. Algorithms based on label propagation lack this disadvantage. Methods based on affiliation model are perspective. This model claims that communities define the structure of a graph.Algorithms which use node attributes are considered: ones based on latent Dirichlet allocation, initially used for text clustering, and CODICIL, where edges of node content relevance are added to the original edge set. 6 classes are listed for algorithms for graphs with node attributes: changing egdes’ weights, changing vertex distance function, building augmented graph with nodes and attributes, based on stochastic  models, partitioning attribute space and others.Overlapping community detection algorithms which effectively use node attributes are just started to appear. Methods based on partitioning attribute space,  latent Dirichlet allocation,  stochastic  models and  nonnegative matrix factorization are considered. The most effective algorithm on real datasets is CESNA. It is based on affiliation model. However, it gives results which are far from ground truth

  8. TARANIS XGRE and IDEE detection capability of terrestrial gamma-ray flashes and associated electron beams

    Science.gov (United States)

    Sarria, David; Lebrun, Francois; Blelly, Pierre-Louis; Chipaux, Remi; Laurent, Philippe; Sauvaud, Jean-Andre; Prech, Lubomir; Devoto, Pierre; Pailot, Damien; Baronick, Jean-Pierre; Lindsey-Clark, Miles

    2017-07-01

    With a launch expected in 2018, the TARANIS microsatellite is dedicated to the study of transient phenomena observed in association with thunderstorms. On board the spacecraft, XGRE and IDEE are two instruments dedicated to studying terrestrial gamma-ray flashes (TGFs) and associated terrestrial electron beams (TEBs). XGRE can detect electrons (energy range: 1 to 10 MeV) and X- and gamma-rays (energy range: 20 keV to 10 MeV) with a very high counting capability (about 10 million counts per second) and the ability to discriminate one type of particle from another. The IDEE instrument is focused on electrons in the 80 keV to 4 MeV energy range, with the ability to estimate their pitch angles. Monte Carlo simulations of the TARANIS instruments, using a preliminary model of the spacecraft, allow sensitive area estimates for both instruments. This leads to an averaged effective area of 425 cm2 for XGRE, used to detect X- and gamma-rays from TGFs, and the combination of XGRE and IDEE gives an average effective area of 255 cm2 which can be used to detect electrons/positrons from TEBs. We then compare these performances to RHESSI, AGILE and Fermi GBM, using data extracted from literature for the TGF case and with the help of Monte Carlo simulations of their mass models for the TEB case. Combining this data with the help of the MC-PEPTITA Monte Carlo simulations of TGF propagation in the atmosphere, we build a self-consistent model of the TGF and TEB detection rates of RHESSI, AGILE and Fermi. It can then be used to estimate that TARANIS should detect about 200 TGFs yr-1 and 25 TEBs yr-1.

  9. Social Circles Detection from Ego Network and Profile Information

    Science.gov (United States)

    2014-12-19

    0704-0188 3. DATES COVERED (From - To) - UU UU UU UU Approved for public release; distribution is unlimited. Social Circles Detection from Ego Network...Research Triangle Park, NC 27709-2211 ego network, social copying community REPORT DOCUMENTATION PAGE 11. SPONSOR/MONITOR’S REPORT NUMBER(S) 10. SPONSOR...ABSTRACT Social Circles Detection from Ego Network and Profile Information Report Title This report presents a study making our first approach to the

  10. 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.

  11. On the capability of smartphones to perform as communication gateways in medical wireless personal area networks.

    Science.gov (United States)

    Morón, María José; Luque, Rafael; Casilari, Eduardo

    2014-01-02

    This paper evaluates and characterizes the technical performance of medical wireless personal area networks (WPANs) that are based on smartphones. For this purpose,a prototype of a health telemonitoring system is presented. The prototype incorporates a commercial Android smartphone, which acts as a relay point, or "gateway", between a set of wireless medical sensors and a data server. Additionally, the paper investigates if the conventional capabilities of current commercial smartphones can be affected by their use as gateways or "Holters" in health monitoring applications. Specifically, the profiling has focused on the CPU and power consumption of the mobile devices. These metrics have been measured under several test conditions modifying the smartphone model, the type of sensors 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 impact of the use of the Wi-Fi interface or the employed method to encode and forward the data that are collected from the sensors.

  12. 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.

  13. Demonstrating the capability of radiography for detection of large planar defects in thick-section welds

    Energy Technology Data Exchange (ETDEWEB)

    Wooldridge, A.B. [Magnox Electric, Berkeley (United Kingdom); Chapman, R.K.; Woodcock, G.S. [Nuclear Electric, Gloucester (United Kingdom); Munns, I.J.; Georgiou, G.A. [TWI - World Centre for Materials Joining Technology, Cambridge (United Kingdom)

    1997-12-31

    Demonstrating the capability of radiography is important for justifying the integrity of certain nuclear power plant components. In particular, Magnox Electric plc has been investigating the reliability of the radiography performed on Magnox steel reactor pressure vessel welds during construction. This work has concentrated on planar defects of structurally significant size. This paper describes an extensive series of experimental studies of radiographic capability, for material thicknesses in the range 50-114 mm. These studies have been supported by surveys of the relevant parameters of real metallurgical defects to confirm the realism of the defects used. The results have been interpreted using a well-established, albeit simplified, theoretical model of the radiographic process, but further work on a more comprehensive theoretical model is in progress to provide more precise comparisons of theoretical and experimental results. Considerable care has been taken to produce planar defects which realistically simulate those which might conceivably occur during welding of thick-section ferritic steel pressure vessels. One key feature is the orientation of the defect relative to the radiographic beam, and this can be controlled reasonably precisely when inducing defects in test specimens. Another crucial parameter for radiographic detection is the crack face separation (gape), which can only be measured by sectioning. (orig.)

  14. A multimodal micro-optrode combining field and single unit recording, multispectral detection and photolabeling capabilities.

    Directory of Open Access Journals (Sweden)

    Suzie Dufour

    Full Text Available Microelectrodes have been very instrumental and minimally invasive for in vivo functional studies from deep brain structures. However they are limited in the amount of information they provide. Here, we describe a, aluminum-coated, fibre optic-based glass microprobe with multiple electrical and optical detection capabilities while retaining tip dimensions that enable single cell measurements (diameter ≤10 µm. The probe enables optical separation from individual cells in transgenic mice expressing multiple fluorescent proteins in distinct populations of neurons within the same deep brain nucleus. It also enables color conversion of photoswitchable fluorescent proteins, which can be used for post-hoc identification of the recorded cells. While metal coating did not significantly improve the optical separation capabilities of the microprobe, the combination of metal on the outside of the probe and of a hollow core within the fiber yields a microelectrode enabling simultaneous single unit and population field potential recordings. The extended range of functionalities provided by the same microprobe thus opens several avenues for multidimensional structural and functional interrogation of single cells and their surrounding deep within the intact nervous system.

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

    CERN Document Server

    Heason, D J

    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, ...

  16. EVALUATE THE CAPABILITY OF LANDSAT8 OPERATIONAL LAND IMAGER FOR SHORELINE CHANGE DETECTION/INLAND WATER STUDIES

    National Research Council Canada - National Science Library

    W. Pervez; S. A. Khan; E. Hussain; F. Amir; M. A. Maud

    2017-01-01

    This paper explored the capability of Landsat-8 Operational Land Imager (OLI) for post classification change detection analysis and mapping application because of its enhanced features from previous Landsat series...

  17. The global radioxenon background and its impact on the detection capability of underground nuclear explosions (Invited)

    Science.gov (United States)

    Ringbom, A.

    2010-12-01

    A detailed knowledge of both the spatial and isotopic distribution of anthropogenic radioxenon is essential in investigations of the performance of the radioxenon part of the IMS, as well as in the development of techniques to discriminate radioxenon signatures from a nuclear explosion from other sources. Further, the production processes in the facilities causing the radioxenon background has to be understood and be compatible with simulations. In this work, several aspects of the observed atmospheric radioxenon background are investigated, including the global distribution as well as the current understanding of the observed isotopic ratios. Analyzed radioxenon data from the IMS, as well as from other measurement stations, are used to create an up-to-date description of the global radioxenon background, including all four CTBT relevant xenon isotopes (133Xe, 131mXe, 133mXe, and 135Xe). In addition, measured isotopic ratios will be compared to simulations of neutron induced fission of 235U, and the uncertainties will be discussed. Finally, the impact of the radioxenon background on the detection capability of the IMS will be investigated. This work is a continuation of studies [1,2] that was presented at the International Scientific Studies conference held in Vienna in 2009. [1] A. Ringbom, et.al., “Characterization of the global distribution of atmospheric radioxenons”, International Scientific Studies Conference on CTBT Verification, 10-12 June 2009. [2] R. D'Amours and A. Ringbom, “A study on the global detection capability of IMS for all CTBT relevant xenon isotopes“, International Scientific Studies Conference on CTBT Verification, 10-12 June 2009.

  18. 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.

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

    Science.gov (United States)

    2017-04-01

    Time, Evolution, Networks , and Function’ (program manager : C. Macedonia). Part of this program focuses on robustness of networks , which compliments...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

  20. Text-Attentional Convolutional Neural Network for Scene Text Detection.

    Science.gov (United States)

    He, Tong; Huang, Weilin; Qiao, Yu; Yao, Jian

    2016-06-01

    Recent deep learning models have demonstrated strong capabilities for classifying text and non-text components in natural images. They extract a high-level feature globally computed from a whole image component (patch), where the cluttered background information may dominate true text features in the deep representation. This leads to less discriminative power and poorer robustness. In this paper, we present a new system for scene text detection by proposing a novel text-attentional convolutional neural network (Text-CNN) that particularly focuses on extracting text-related regions and features from the image components. We develop a new learning mechanism to train the Text-CNN with multi-level and rich supervised information, including text region mask, character label, and binary text/non-text information. The rich supervision information enables the Text-CNN with a strong capability for discriminating ambiguous texts, and also increases its robustness against complicated background components. The training process is formulated as a multi-task learning problem, where low-level supervised information greatly facilitates the main task of text/non-text classification. In addition, a powerful low-level detector called contrast-enhancement maximally stable extremal regions (MSERs) is developed, which extends the widely used MSERs by enhancing intensity contrast between text patterns and background. This allows it to detect highly challenging text patterns, resulting in a higher recall. Our approach achieved promising results on the ICDAR 2013 data set, with an F-measure of 0.82, substantially improving the state-of-the-art results.

  1. Text-Attentional Convolutional Neural Networks for Scene Text Detection.

    Science.gov (United States)

    He, Tong; Huang, Weilin; Qiao, Yu; Yao, Jian

    2016-03-28

    Recent deep learning models have demonstrated strong capabilities for classifying text and non-text components in natural images. They extract a high-level feature computed globally from a whole image component (patch), where the cluttered background information may dominate true text features in the deep representation. This leads to less discriminative power and poorer robustness. In this work, we present a new system for scene text detection by proposing a novel Text-Attentional Convolutional Neural Network (Text-CNN) that particularly focuses on extracting text-related regions and features from the image components. We develop a new learning mechanism to train the Text-CNN with multi-level and rich supervised information, including text region mask, character label, and binary text/nontext information. The rich supervision information enables the Text-CNN with a strong capability for discriminating ambiguous texts, and also increases its robustness against complicated background components. The training process is formulated as a multi-task learning problem, where low-level supervised information greatly facilitates main task of text/non-text classification. In addition, a powerful low-level detector called Contrast- Enhancement Maximally Stable Extremal Regions (CE-MSERs) is developed, which extends the widely-used MSERs by enhancing intensity contrast between text patterns and background. This allows it to detect highly challenging text patterns, resulting in a higher recall. Our approach achieved promising results on the ICDAR 2013 dataset, with a F-measure of 0.82, improving the state-of-the-art results substantially.

  2. Colonoscopic polyp detection using convolutional neural networks

    Science.gov (United States)

    Park, Sun Young; Sargent, Dusty

    2016-03-01

    Computer aided diagnosis (CAD) systems for medical image analysis rely on accurate and efficient feature extraction methods. Regardless of which type of classifier is used, the results will be limited if the input features are not diagnostically relevant and do not properly discriminate between the different classes of images. Thus, a large amount of research has been dedicated to creating feature sets that capture the salient features that physicians are able to observe in the images. Successful feature extraction reduces the semantic gap between the physician's interpretation and the computer representation of images, and helps to reduce the variability in diagnosis between physicians. Due to the complexity of many medical image classification tasks, feature extraction for each problem often requires domainspecific knowledge and a carefully constructed feature set for the specific type of images being classified. In this paper, we describe a method for automatic diagnostic feature extraction from colonoscopy images that may have general application and require a lower level of domain-specific knowledge. The work in this paper expands on our previous CAD algorithm for detecting polyps in colonoscopy video. In that work, we applied an eigenimage model to extract features representing polyps, normal tissue, diverticula, etc. from colonoscopy videos taken from various viewing angles and imaging conditions. Classification was performed using a conditional random field (CRF) model that accounted for the spatial and temporal adjacency relationships present in colonoscopy video. In this paper, we replace the eigenimage feature descriptor with features extracted from a convolutional neural network (CNN) trained to recognize the same image types in colonoscopy video. The CNN-derived features show greater invariance to viewing angles and image quality factors when compared to the eigenimage model. The CNN features are used as input to the CRF classifier as before. We report

  3. Assessment of an Onboard EO Sensor to Enable Detect-and-Sense Capability for UAVs Operating in a Cluttered Environment

    Science.gov (United States)

    2017-09-01

    environment . To have high reliability, maintainability, and availability rates. Consumer demands shape the technological advancements to...ONBOARD EO SENSOR TO ENABLE DETECT-AND-SENSE CAPABILITY FOR UAVs OPERATING IN A CLUTTERED ENVIRONMENT by Wee Kiong Ang September 2017...OF AN ONBOARD EO SENSOR TO ENABLE DETECT- AND-SENSE CAPABILITY FOR UAVs OPERATING IN A CLUTTERED ENVIRONMENT 5. FUNDING NUMBERS 6. AUTHOR(S) Wee

  4. Vessel detection in ultrasound images using deep convolutional neural networks

    OpenAIRE

    Smistad, Erik; Løvstakken, Lasse

    2016-01-01

    Deep convolutional neural networks have achieved great results on image classification problems. In this paper, a new method using a deep convolutional neural network for detecting blood vessels in B-mode ultrasound images is presented. Automatic blood vessel detection may be useful in medical applications such as deep venous thrombosis detection, anesthesia guidance and catheter placement. The proposed method is able to determine the position and size of the vessels in images in real-time. 1...

  5. Promoting scientific collaboration and research through integrated social networking capabilities within the OpenTopography Portal

    Science.gov (United States)

    Nandigam, V.; Crosby, C. J.; Baru, C.

    2009-04-01

    LiDAR (Light Distance And Ranging) topography data offer earth scientists the opportunity to study the earth's surface at very high resolutions. As a result, the popularity of these data is growing dramatically. However, the management, distribution, and analysis of community LiDAR data sets is a challenge due to their massive size (multi-billion point, mutli-terabyte). We have also found that many earth science users of these data sets lack the computing resources and expertise required to process these data. We have developed the OpenTopography Portal to democratize access to these large and computationally challenging data sets. The OpenTopography Portal uses cyberinfrastructure technology developed by the GEON project to provide access to LiDAR data in a variety of formats. LiDAR data products available range from simple Google Earth visualizations of LiDAR-derived hillshades to 1 km2 tiles of standard digital elevation model (DEM) products as well as LiDAR point cloud data and user generated custom-DEMs. We have found that the wide spectrum of LiDAR users have variable scientific applications, computing resources and technical experience and thus require a data system with multiple distribution mechanisms and platforms to serve a broader range of user communities. Because the volume of LiDAR topography data available is rapidly expanding, and data analysis techniques are evolving, there is a need for the user community to be able to communicate and interact to share knowledge and experiences. To address this need, the OpenTopography Portal enables social networking capabilities through a variety of collaboration tools, web 2.0 technologies and customized usage pattern tracking. Fundamentally, these tools offer users the ability to communicate, to access and share documents, participate in discussions, and to keep up to date on upcoming events and emerging technologies. The OpenTopography portal achieves the social networking capabilities by integrating various

  6. Improving diagnostic capability for HPV disease internationally within the NIH-NIAID-Division of AIDS Clinical Trial Networks

    Science.gov (United States)

    Godfrey, Catherine C.; Michelow, Pamela M.; Godard, Mandana; Sahasrabuddhe, Vikrant V.; Darden, Janice; Firnhaber, Cynthia S.; Wetherall, Neal T.; Bremer, James; Coombs, Robert W.; Wilkin, Timothy

    2014-01-01

    Objectives To evaluate an external quality assurance (EQA) program for the laboratory diagnosis of human papillomavirus (HPV) disease that was established to improve international research capability within the Division of AIDS at the National Institute of Allergy and Infectious Disease–supported Adult AIDS Clinical Trials Group network. Methods A three-component EQA scheme was devised comprising assessments of diagnostic accuracy of cytotechnologists and pathologists using available EQA packages, review of quality and accuracy of clinical slides from local sites by an outside expert, and HPV DNA detection using the commercially available HPV test kit. Results Seven laboratories and 17 pathologists in Africa, India, and South America participated. EQA scores were suboptimal for standard packages in three of seven laboratories. There was good agreement between the local laboratory and the central reader 70% of the time (90% confidence interval, 42%-98%). Performance on the College of American Pathologists’ HPV DNA testing panel was successful in all laboratories tested. Conclusions The prequalifying EQA round identified correctable issues that will improve the laboratory diagnosis of HPV related cervical disease at the international sites and will provide a mechanism for ongoing education and continuous quality improvement. PMID:24225757

  7. Improving the detectability and imaging capability of ground penetrating radar using novel antenna concepts

    Science.gov (United States)

    Koyadan Koroth, Ajith; Bhattacharya, Amitabha

    2017-04-01

    Antennas are key components of Ground Penetrating Radar (GPR) instrumentation. A carefully designed antenna can improve the detectability and imaging capability of a GPR to a great extent without changing the other instrumentations. In this work, we propose four different types of antennas for GPR. They are modifications of a conventional bowtie antenna with great improvement in performance parameters. The designed antennas has also been tested in a stepped frequency type GPR and two dimensional scan images of various targets are presented. Bowtie antennas have been traditionally employed in GPR for its wide impedance bandwidth and radiation properties. The researchers proposed resistive loading to improve the bandwidth of the bowtie antenna and for low ringing pulse radiation. But this method was detrimental for antenna gain and efficiency. Bowtie antennas have a very wide impedance bandwidth. But the useful bandwidth of the antenna has been limited by the radiation pattern bandwidth. The boresight gain of bowtie antennas are found to be unstable beyond a 4:1 bandwidth. In this work, these problems have been addressed and maximum usable bandwidth for the bowtie antennas has been achieved. In this work, four antennas have been designed: namely, 1.) RC loaded bowtie antennas, 2.) RC loaded bowtie with metamaterial lens, 3.) Loop loaded bowtie, 4.) Loop loaded bowtie with directors. The designed antennas were characterized for different parameters like impedance bandwidth, radiation pattern and, gain. In antenna 1, a combined resistive-capacitive loading has been applied by periodic slot cut on the arms of the bowtie and pasting a planar graphite sheet over it. Graphite having a less conductance compared to copper acts as resistive loading. This would minimize the losses compared to lumped resistive loading. The antenna had a 10:1 impedance bandwidth and, a 5:1 pattern bandwidth. In antenna 2, a metamaterial lens has been designed to augment the antenna 1, to improve

  8. A Study of the Classification Capabilities of Neural Networks Using Unsupervised Learning: A Comparison with K-Means Clustering.

    Science.gov (United States)

    Balakrishnan, P. V. (Sunder); And Others

    1994-01-01

    A simulation study compares nonhierarchical clustering capabilities of a class of neural networks using Kohonen learning with a K-means clustering procedure. The focus is on the ability of the procedures to recover correctly the known cluster structure in the data. Advantages and disadvantages of the procedures are reviewed. (SLD)

  9. Evaluation of Anomaly Detection Capability for Ground-Based Pre-Launch Shuttle Operations. Chapter 8

    Science.gov (United States)

    Martin, Rodney Alexander

    2010-01-01

    This chapter will provide a thorough end-to-end description of the process for evaluation of three different data-driven algorithms for anomaly detection to select the best candidate for deployment as part of a suite of IVHM (Integrated Vehicle Health Management) technologies. These algorithms were deemed to be sufficiently mature enough to be considered viable candidates for deployment in support of the maiden launch of Ares I-X, the successor to the Space Shuttle for NASA's Constellation program. Data-driven algorithms are just one of three different types being deployed. The other two types of algorithms being deployed include a "nile-based" expert system, and a "model-based" system. Within these two categories, the deployable candidates have already been selected based upon qualitative factors such as flight heritage. For the rule-based system, SHINE (Spacecraft High-speed Inference Engine) has been selected for deployment, which is a component of BEAM (Beacon-based Exception Analysis for Multimissions), a patented technology developed at NASA's JPL (Jet Propulsion Laboratory) and serves to aid in the management and identification of operational modes. For the "model-based" system, a commercially available package developed by QSI (Qualtech Systems, Inc.), TEAMS (Testability Engineering and Maintenance System) has been selected for deployment to aid in diagnosis. In the context of this particular deployment, distinctions among the use of the terms "data-driven," "rule-based," and "model-based," can be found in. Although there are three different categories of algorithms that have been selected for deployment, our main focus in this chapter will be on the evaluation of three candidates for data-driven anomaly detection. These algorithms will be evaluated upon their capability for robustly detecting incipient faults or failures in the ground-based phase of pre-launch space shuttle operations, rather than based oil heritage as performed in previous studies. Robust

  10. 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.

  11. 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. Adnan Hamad, Dingli Yu, JB Gomm, Mahavir S Sangha. Abstract. Fault detection and isolation have become one of the most important aspects of automobile design. A fault detection (FD) scheme is developed for automotive engines in this paper.

  12. 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

  13. Neuromorphic computing applications for network intrusion detection systems

    Science.gov (United States)

    Garcia, Raymond C.; Pino, Robinson E.

    2014-05-01

    What is presented here is a sequence of evolving concepts for network intrusion detection. These concepts start with neuromorphic structures for XOR-based signature matching and conclude with computationally based network intrusion detection system with an autonomous structuring algorithm. There is evidence that neuromorphic computation for network intrusion detection is fractal in nature under certain conditions. Specifically, the neural structure can take fractal form when simple neural structuring is autonomous. A neural structure is fractal by definition when its fractal dimension exceeds the synaptic matrix dimension. The authors introduce the use of fractal dimension of the neuromorphic structure as a factor in the autonomous restructuring feedback loop.

  14. Network cluster detecting in associated bi-graph picture

    CERN Document Server

    He, Zhe; Xu, Rui-Jie; Wang, Bing-Hong; Ou-Yang, Zhong-Can

    2014-01-01

    We find that there is a close relationship between the associated bigraph and the clustering. the imbedding of the bigraph into some space can identify the clusters. Thus, we propose a new method for network cluster detecting through associated bigraph,of which the physical meaning is clear and the time complexity is acceptable. These characteristics help people to understand the structure and character of networks. We uncover the clusters on serval real networks in this paper as examples. The Zachary Network, which presents the structure of a karate club,can be partation into two clusters correctly by this method. And the Dolphin network is partitioned reasonably.

  15. 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

  16. ANOMALY NETWORK INTRUSION DETECTION SYSTEM BASED ON DISTRIBUTED TIME-DELAY NEURAL NETWORK (DTDNN

    Directory of Open Access Journals (Sweden)

    LAHEEB MOHAMMAD IBRAHIM

    2010-12-01

    Full Text Available In this research, a hierarchical off-line anomaly network intrusion detection system based on Distributed Time-Delay Artificial Neural Network is introduced. This research aims to solve a hierarchical multi class problem in which the type of attack (DoS, U2R, R2L and Probe attack detected by dynamic neural network. The results indicate that dynamic neural nets (Distributed Time-Delay Artificial Neural Network can achieve a high detection rate, where the overall accuracy classification rate average is equal to 97.24%.

  17. 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.

  18. Node Attribute-enhanced Community Detection in Complex Networks.

    Science.gov (United States)

    Jia, Caiyan; Li, Yafang; Carson, Matthew B; Wang, Xiaoyang; Yu, Jian

    2017-05-25

    Community detection involves grouping the nodes of a network such that nodes in the same community are more densely connected to each other than to the rest of the network. Previous studies have focused mainly on identifying communities in networks using node connectivity. However, each node in a network may be associated with many attributes. Identifying communities in networks combining node attributes has become increasingly popular in recent years. Most existing methods operate on networks with attributes of binary, categorical, or numerical type only. In this study, we introduce kNN-enhance, a simple and flexible community detection approach that uses node attribute enhancement. This approach adds the k Nearest Neighbor (kNN) graph of node attributes to alleviate the sparsity and the noise effect of an original network, thereby strengthening the community structure in the network. We use two testing algorithms, kNN-nearest and kNN-Kmeans, to partition the newly generated, attribute-enhanced graph. Our analyses of synthetic and real world networks have shown that the proposed algorithms achieve better performance compared to existing state-of-the-art algorithms. Further, the algorithms are able to deal with networks containing different combinations of binary, categorical, or numerical attributes and could be easily extended to the analysis of massive networks.

  19. Malicious node detection in ad-hoc wireless networks

    Science.gov (United States)

    Griswold, Richard L.; Medidi, Sirisha R.

    2003-07-01

    Advances in wireless communications and the proliferation of mobile computing devices has led to the rise of a new type of computer network: the ad-hoc wireless network. Ad-hoc networks are characterized by a lack of fixed infrastructure, which give ad-hoc networks a great deal of flexibility, but also increases the risk of security problems. In wired networks, key pieces of network infrastructure are secured to prevent unauthorized physical access and tampering. Network administrators ensure that everything is properly configured and are on-hand to fix problems and deal with intrusions. In contrast, the nodes in an ad-hoc network are responsible for routing and forwarding data in the network, and there are no network administrators to handle potential problems. This makes an ad-hoc network more vulnerable to a misconfigured, faulty, or compromised node. We propose a means for a node in an ad-hoc network to detect and handle these malicious nodes by comparing data available to the routing protocol, such as cached routes in Dynamic Source Routing, ICMP messages, and transport layer information, such as TCP timeouts. This data can then be used along with network probes to isolate the malicious node.

  20. AN IMMUNE AGENTS SYSTEM FOR NETWORK INTRUSIONS DETECTION

    OpenAIRE

    Noria Benyettou; Abdelkader Benyettou; Vincent Rodin

    2014-01-01

    With the development growing of network technology, computer networks became increasingly wide and opened. This evolution gave birth to new techniques allowing accessibility of networks and information systems with an aim of facilitating the transactions. Consequently, these techniques gave also birth to new forms of threats. In this article, we present the utility to use a system of intrusion detection through a presentation of these characteristics. Using as inspiration the i...

  1. 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...

  2. Error detection capability of a novel transmission detector: a validation study for online VMAT monitoring

    Science.gov (United States)

    Pasler, Marlies; Michel, Kilian; Marrazzo, Livia; Obenland, Michael; Pallotta, Stefania; Björnsgard, Mari; Lutterbach, Johannes

    2017-09-01

    The purpose of this study was to characterize a new single large-area ionization chamber, the integral quality monitor system (iRT, Germany), for online and real-time beam monitoring. Signal stability, monitor unit (MU) linearity and dose rate dependence were investigated for static and arc deliveries and compared to independent ionization chamber measurements. The dose verification capability of the transmission detector system was evaluated by comparing calculated and measured detector signals for 15 volumetric modulated arc therapy plans. The error detection sensitivity was tested by introducing MLC position and linac output errors. Deviations in dose distributions between the original and error-induced plans were compared in terms of detector signal deviation, dose-volume histogram (DVH) metrics and 2D γ-evaluation (2%/2 mm and 3%/3 mm). The detector signal is linearly dependent on linac output and shows negligible (metrics and detector signal deviation was found (e.g. PTV D mean: R 2  =  0.97). Positional MLC errors of 1 mm and errors in linac output of 2% were identified with the transmission detector system. The extensive tests performed in this investigation show that the new transmission detector provides a stable and sensitive cumulative signal output and is suitable for beam monitoring during patient treatment.

  3. 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

  4. Outlier detection techniques for wireless sensor networks: A survey

    NARCIS (Netherlands)

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

    2010-01-01

    In the field of wireless sensor networks, those 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

  5. Fraud Detection In Mobile Communications Networks Using User ...

    African Journals Online (AJOL)

    Fraud detection is an important application, since network operators lose a relevant portion of their revenue to fraud. The intentions of mobile phone users cannot be well observed except through the call data. The call data is used in describing behavioural patterns of users. Neural networks and probabilistic models are ...

  6. A distributed data base management capability for the deep space network

    Science.gov (United States)

    Bryan, A. I.

    1976-01-01

    The Configuration Control and Audit Assembly (CCA) is reported that has been designed to provide a distributed data base management capability for the DSN. The CCA utilizes capabilities provided by the DSN standard minicomputer and the DSN standard nonreal time high level management oriented programming language, MBASIC. The characteristics of the CCA for the first phase of implementation are described.

  7. Spectral methods for network community detection and graph partitioning

    OpenAIRE

    Newman, M.E.J.

    2013-01-01

    We consider three distinct and well studied problems concerning network structure: community detection by modularity maximization, community detection by statistical inference, and normalized-cut graph partitioning. Each of these problems can be tackled using spectral algorithms that make use of the eigenvectors of matrix representations of the network. We show that with certain choices of the free parameters appearing in these spectral algorithms the algorithms for all three problems are, in...

  8. Integrating Wireless Networking for Radiation Detection

    Science.gov (United States)

    Board, Jeremy; Barzilov, Alexander; Womble, Phillip; Paschal, Jon

    2006-10-01

    As wireless networking becomes more available, new applications are being developed for this technology. Our group has been studying the advantages of wireless networks of radiation detectors. With the prevalence of the IEEE 802.11 standard (``WiFi''), we have developed a wireless detector unit which is comprised of a 5 cm x 5 cm NaI(Tl) detector, amplifier and data acquisition electronics, and a WiFi transceiver. A server may communicate with the detector unit using a TCP/IP network connected to a WiFi access point. Special software on the server will perform radioactive isotope determination and estimate dose-rates. We are developing an enhanced version of the software which utilizes the receiver signal strength index (RSSI) to estimate source strengths and to create maps of radiation intensity.

  9. Detecting connectivity changes in neuronal networks.

    Science.gov (United States)

    Berry, Tyrus; Hamilton, Franz; Peixoto, Nathalia; Sauer, Timothy

    2012-08-15

    We develop a method from semiparametric statistics (Cox, 1972) for the purpose of tracking links and connection strengths over time in a neuronal network from spike train data. We consider application of the method as implemented in Masud and Borisyuk (2011), and evaluate its use on data generated independently of the Cox model hypothesis, in particular from a spiking model of Izhikevich in four different dynamical regimes. Then, we show how the Cox method can be used to determine statistically significant changes in network connectivity over time. Our methodology is demonstrated using spike trains from multi-electrode array measurements of networks of cultured mammalian spinal cord cells. Copyright © 2012 Elsevier B.V. All rights reserved.

  10. Correlating intrusion detection alerts on bot malware infections using neural network

    DEFF Research Database (Denmark)

    Kidmose, Egon; Stevanovic, Matija; Pedersen, Jens Myrup

    2016-01-01

    Millions of computers are infected with bot malware, form botnets and enable botmaster to perform malicious and criminal activities. Intrusion Detection Systems are deployed to detect infections, but they raise many correlated alerts for each infection, requiring a large manual investigation effort...... part, as such knowledge is inferred by Neural Networks. Evaluation has been performed with traffic traces of real bot binaries executed in a lab setup. The method is trained on labelled Intrusion Detection System alerts and is capable of correctly predicting which of seven incidents an alert pertains......, 56.15% of the times. Based on the observed performance it is concluded that the task of understanding Intrusion Detection System alerts can be handled by a Neural Network, showing the potential for reducing the need for manual processing of alerts. Finally, it should be noted that, this is achieved...

  11. Detecting emotional contagion in massive social networks.

    Directory of Open Access Journals (Sweden)

    Lorenzo Coviello

    Full Text Available Happiness and other emotions spread between people in direct contact, but it is unclear whether massive online social networks also contribute to this spread. Here, we elaborate a novel method for measuring the contagion of emotional expression. With data from millions of Facebook users, we show that rainfall directly influences the emotional content of their status messages, and it also affects the status messages of friends in other cities who are not experiencing rainfall. For every one person affected directly, rainfall alters the emotional expression of about one to two other people, suggesting that online social networks may magnify the intensity of global emotional synchrony.

  12. Detecting emotional contagion in massive social networks.

    Science.gov (United States)

    Coviello, Lorenzo; Sohn, Yunkyu; Kramer, Adam D I; Marlow, Cameron; Franceschetti, Massimo; Christakis, Nicholas A; Fowler, James H

    2014-01-01

    Happiness and other emotions spread between people in direct contact, but it is unclear whether massive online social networks also contribute to this spread. Here, we elaborate a novel method for measuring the contagion of emotional expression. With data from millions of Facebook users, we show that rainfall directly influences the emotional content of their status messages, and it also affects the status messages of friends in other cities who are not experiencing rainfall. For every one person affected directly, rainfall alters the emotional expression of about one to two other people, suggesting that online social networks may magnify the intensity of global emotional synchrony.

  13. The early detection research network: 10-year outlook.

    Science.gov (United States)

    Srivastava, Sudhir

    2013-01-01

    The National Cancer Institute's Early Detection Research Network (EDRN) has made significant progress in developing an organized effort for discovering and validating biomarkers, building resources to support this effort, demonstrating the capabilities of several genomic and proteomic platforms, identifying candidate biomarkers, and undertaking multicenter validation studies. In its first 10 years, the EDRN went from a groundbreaking concept to an operational success. The EDRN has established clear milestones for reaching a decision of "go" or "no go" during the biomarker development process. Milestones are established on the basis of statistical criteria, performance characteristics of biomarkers, and anticipated clinical use. More than 300 biomarkers have been stopped from further development. To date, the EDRN has prioritized more than 300 biomarkers and has completed more than 10 validation studies. The US Food and Drug Administration has now cleared 5 biomarkers for various clinical endpoints. The EDRN today combines numerous collaborative and multidisciplinary investigator-initiated projects with a strong national administrative and data infrastructure. The EDRN has created a rigorous peer-review system that ensures that preliminary data--analytical, clinical, and quantitative--are of excellent quality. The process begins with an internal review with clinical, biostatistical, and analytical expertise. The project then receives external peer review and, finally, National Cancer Institute program staff review, resulting in an exceptionally robust and high-quality validation trial. © 2012 American Association for Clinical Chemistry

  14. CMIP: a software package capable of reconstructing genome-wide regulatory networks using gene expression data.

    Science.gov (United States)

    Zheng, Guangyong; Xu, Yaochen; Zhang, Xiujun; Liu, Zhi-Ping; Wang, Zhuo; Chen, Luonan; Zhu, Xin-Guang

    2016-12-23

    A gene regulatory network (GRN) represents interactions of genes inside a cell or tissue, in which vertexes and edges stand for genes and their regulatory interactions respectively. Reconstruction of gene regulatory networks, in particular, genome-scale networks, is essential for comparative exploration of different species and mechanistic investigation of biological processes. Currently, most of network inference methods are computationally intensive, which are usually effective for small-scale tasks (e.g., networks with a few hundred genes), but are difficult to construct GRNs at genome-scale. Here, we present a software package for gene regulatory network reconstruction at a genomic level, in which gene interaction is measured by the conditional mutual information measurement using a parallel computing framework (so the package is named CMIP). The package is a greatly improved implementation of our previous PCA-CMI algorithm. In CMIP, we provide not only an automatic threshold determination method but also an effective parallel computing framework for network inference. Performance tests on benchmark datasets show that the accuracy of CMIP is comparable to most current network inference methods. Moreover, running tests on synthetic datasets demonstrate that CMIP can handle large datasets especially genome-wide datasets within an acceptable time period. In addition, successful application on a real genomic dataset confirms its practical applicability of the package. This new software package provides a powerful tool for genomic network reconstruction to biological community. The software can be accessed at http://www.picb.ac.cn/CMIP/ .

  15. The ground truth about metadata and community detection in networks.

    Science.gov (United States)

    Peel, Leto; Larremore, Daniel B; Clauset, Aaron

    2017-05-01

    Across many scientific domains, there is a common need to automatically extract a simplified view or coarse-graining of how a complex system's components interact. This general task is called community detection in networks and is analogous to searching for clusters in independent vector data. It is common to evaluate the performance of community detection algorithms by their ability to find so-called ground truth communities. This works well in synthetic networks with planted communities because these networks' links are formed explicitly based on those known communities. However, there are no planted communities in real-world networks. Instead, it is standard practice to treat some observed discrete-valued node attributes, or metadata, as ground truth. We show that metadata are not the same as ground truth and that treating them as such induces severe theoretical and practical problems. We prove that no algorithm can uniquely solve community detection, and we prove a general No Free Lunch theorem for community detection, which implies that there can be no algorithm that is optimal for all possible community detection tasks. However, community detection remains a powerful tool and node metadata still have value, so a careful exploration of their relationship with network structure can yield insights of genuine worth. We illustrate this point by introducing two statistical techniques that can quantify the relationship between metadata and community structure for a broad class of models. We demonstrate these techniques using both synthetic and real-world networks, and for multiple types of metadata and community structures.

  16. 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.

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

    Science.gov (United States)

    Kaliappan, Jayakumar; Thiagarajan, Revathi; Sundararajan, Karpagam

    2015-01-01

    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. 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.

  19. Detecting Change in Longitudinal Social Networks

    Science.gov (United States)

    2011-01-01

    reasons as the Hamming distance. The quadratic assignment procedure ( QAP ) and its multiple regression counterpart MRQAP (Krackhardt, 1987, 1992) has...Human Organization 35:269-286. Krackhardt, D. (1987). “ QAP Partialling as a Test of Spuriousness.” Social Networks 9: 171-186. Krackhardt, D. (1992

  20. 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.

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

    Science.gov (United States)

    Kang, Min-Joo

    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. PMID:27271802

  2. 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.

  3. 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.

  4. SA-SOM algorithm for detecting communities in complex networks

    Science.gov (United States)

    Chen, Luogeng; Wang, Yanran; Huang, Xiaoming; Hu, Mengyu; Hu, Fang

    2017-10-01

    Currently, community detection is a hot topic. This paper, based on the self-organizing map (SOM) algorithm, introduced the idea of self-adaptation (SA) that the number of communities can be identified automatically, a novel algorithm SA-SOM of detecting communities in complex networks is proposed. Several representative real-world networks and a set of computer-generated networks by LFR-benchmark are utilized to verify the accuracy and the efficiency of this algorithm. The experimental findings demonstrate that this algorithm can identify the communities automatically, accurately and efficiently. Furthermore, this algorithm can also acquire higher values of modularity, NMI and density than the SOM algorithm does.

  5. 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.

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

    Science.gov (United States)

    Zonglin, Li; Guangmin, Hu; Xingmiao, Yao; Dan, Yang

    2008-12-01

    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.

  7. 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.

  8. 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.

  9. Z-Score-Based Modularity for Community Detection in Networks.

    Science.gov (United States)

    Miyauchi, Atsushi; Kawase, Yasushi

    2016-01-01

    Identifying community structure in networks is an issue of particular interest in network science. The modularity introduced by Newman and Girvan is the most popular quality function for community detection in networks. In this study, we identify a problem in the concept of modularity and suggest a solution to overcome this problem. Specifically, we obtain a new quality function for community detection. We refer to the function as Z-modularity because it measures the Z-score of a given partition with respect to the fraction of the number of edges within communities. Our theoretical analysis shows that Z-modularity mitigates the resolution limit of the original modularity in certain cases. Computational experiments using both artificial networks and well-known real-world networks demonstrate the validity and reliability of the proposed quality function.

  10. 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.

  11. Structured learning via convolutional neural networks for vehicle detection

    Science.gov (United States)

    Maqueda, Ana I.; del Blanco, Carlos R.; Jaureguizar, Fernando; García, Narciso

    2017-05-01

    One of the main tasks in a vision-based traffic monitoring system is the detection of vehicles. Recently, deep neural networks have been successfully applied to this end, outperforming previous approaches. However, most of these works generally rely on complex and high-computational region proposal networks. Others employ deep neural networks as a segmentation strategy to achieve a semantic representation of the object of interest, which has to be up-sampled later. In this paper, a new design for a convolutional neural network is applied to vehicle detection in highways for traffic monitoring. This network generates a spatially structured output that encodes the vehicle locations. Promising results have been obtained in the GRAM-RTM dataset.

  12. Group on Earth Observations (GEO) Global Drought Monitor Portal: Adding Capabilities for Forecasting Hydrological Extremes and Early Warning Networking

    Science.gov (United States)

    Pozzi, W.; de Roo, A.; Vogt, J.; Lawford, R. G.; Pappenberger, F.; Heim, R. R.; Stefanski, R.

    2011-12-01

    for Africa (DEWFORA) to strengthen preparedness and adaptation; 3) setting up an Early Warning System network for drought ( to be developed through World Meteorological Organization WMO); and 4) adding global remote sensing drought monitoring capabilities (soil moisture anomalies). Flooding represents positive precipitation anomalies, whereas drought represents negative precipitation anomalies. The JRC combined Hydrologic Extremes platform will include multiple models and tools, such as; 1) JRC Global Flood Detection System and Global Flood Early Warning System; 2) the WMO Flash Flood Guidance system; 3) the Dartmouth Flood Observatory; 4) a suite of monitored and forecasted drought and water scarcity indicators through the various drought observatories accessible through the GEO Global Drought Monitor Portal. The GEO Global Drought and Flooding systems represent the "applications-side" of water activities within the GEO Work Plan and are supported by the "Research and Development (R&D) side" of water activities within the new 2012-2015 GEO Work Plan.

  13. 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...... on a real 37-bus weak distribution system confirmed that the proposed controller can enhance the FRT capability....

  14. Capabilities of scatterometer for detection of diurnal thaw and refreeze of snow cover

    Science.gov (United States)

    Bartsch, A.; Naeimi, V.; Wagner, W.

    2009-04-01

    Microwave sensors with short wavelengths such as SeaWinds Quikscat (Ku-band) are sensitive to changes at snow surfaces due to thaw. Especially scatterometer can provide several measurements per day at high latitudes. Diurnal differences are investigated in a range of studies since they indicate exactly when snowmelt is taking place. Large changes in backscatter between morning and evening acquisitions are characteristic for the snowmelt period, when freezing takes place over night and thawing of the surface during the day. A change from volume to surface scattering occurs in case of melting. The actual number of dates of snow thaw is of most interest for glacier mass balance studies but the final disappearance of snow together with the length of spring thaw is required in regions with seasonal snow cover. When significant changes due to freeze/thaw cycling cease, closed snow cover also disappears. The exact day of year of beginning and end of freeze/thaw cycling can be clearly determined using QuikScat with consideration of long-term noise in order to exclude unnatural effects and changes in soil moisture and snow pack characteristics. SeaWinds Quikscat measurements are available since 1999. The first entire snowmelt period on the northern hemisphere is covered in 2000. A further scatteromter which provides the necessary observation intervals at high latitudes is the Metop ASCAT. It acquires data with %80 daily global coverage but at a longer wavelength (C-band) and different incidence angles since 2007. Comparison examples showing the capabilities of the two different sensors for the purpose of snowmelt detection are presented for high latitude regions and mountainous terrain at mid latitudes (Alps).

  15. Detecting Target Data in Network Traffic

    Science.gov (United States)

    2017-03-01

    packets, such as unauthorized connections to services like FTP and SSH connections, as well as RDP and MSSQL. Stateful firewalls are designed to...Hashdb can also be used to analyze network traffic and embedded content in other documents. There are hashdb libraries for the Python and C...amount of data that it logs. Bro will look at to DNS traffic, HTTP requests, and if any other connections attempted to be made over FTP, SSH and other

  16. Detecting functional hubs of ictogenic networks.

    Science.gov (United States)

    Zubler, Frederic; Gast, Heidemarie; Abela, Eugenio; Rummel, Christian; Hauf, Martinus; Wiest, Roland; Pollo, Claudio; Schindler, Kaspar

    2015-03-01

    Quantitative EEG (qEEG) has modified our understanding of epileptic seizures, shifting our view from the traditionally accepted hyper-synchrony paradigm toward more complex models based on re-organization of functional networks. However, qEEG measurements are so far rarely considered during the clinical decision-making process. To better understand the dynamics of intracranial EEG signals, we examine a functional network derived from the quantification of information flow between intracranial EEG signals. Using transfer entropy, we analyzed 198 seizures from 27 patients undergoing pre-surgical evaluation for pharmaco-resistant epilepsy. During each seizure we considered for each network the in-, out- and total "hubs", defined respectively as the time and the EEG channels with the maximal incoming, outgoing or total (bidirectional) information flow. In the majority of cases we found that the hubs occur around the middle of seizures, and interestingly not at the beginning or end, where the most dramatic EEG signal changes are found by visual inspection. For the patients who then underwent surgery, good postoperative clinical outcome was on average associated with a higher percentage of out- or total-hubs located in the resected area (for out-hubs p = 0.01, for total-hubs p = 0.04). The location of in-hubs showed no clear predictive value. We conclude that the study of functional networks based on qEEG measurements may help to identify brain areas that are critical for seizure generation and are thus potential targets for focused therapeutic interventions.

  17. Modularity detection in protein-protein interaction networks.

    Science.gov (United States)

    Narayanan, Tejaswini; Gersten, Merril; Subramaniam, Shankar; Grama, Ananth

    2011-12-29

    Many recent studies have investigated modularity in biological networks, and its role in functional and structural characterization of constituent biomolecules. A technique that has shown considerable promise in the domain of modularity detection is the Newman and Girvan (NG) algorithm, which relies on the number of shortest-paths across pairs of vertices in the network traversing a given edge, referred to as the betweenness of that edge. The edge with the highest betweenness is iteratively eliminated from the network, with the betweenness of the remaining edges recalculated in every iteration. This generates a complete dendrogram, from which modules are extracted by applying a quality metric called modularity denoted by Q. This exhaustive computation can be prohibitively expensive for large networks such as Protein-Protein Interaction Networks. In this paper, we present a novel optimization to the modularity detection algorithm, in terms of an efficient termination criterion based on a target edge betweenness value, using which the process of iterative edge removal may be terminated. We validate the robustness of our approach by applying our algorithm on real-world protein-protein interaction networks of Yeast, C.Elegans and Drosophila, and demonstrate that our algorithm consistently has significant computational gains in terms of reduced runtime, when compared to the NG algorithm. Furthermore, our algorithm produces modules comparable to those from the NG algorithm, qualitatively and quantitatively. We illustrate this using comparison metrics such as module distribution, module membership cardinality, modularity Q, and Jaccard Similarity Coefficient. We have presented an optimized approach for efficient modularity detection in networks. The intuition driving our approach is the extraction of holistic measures of centrality from graphs, which are representative of inherent modular structure of the underlying network, and the application of those measures to

  18. 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

  19. Automated Network Anomaly Detection with Learning, Control and Mitigation

    Science.gov (United States)

    Ippoliti, Dennis

    2014-01-01

    Anomaly detection is a challenging problem that has been researched within a variety of application domains. In network intrusion detection, anomaly based techniques are particularly attractive because of their ability to identify previously unknown attacks without the need to be programmed with the specific signatures of every possible attack.…

  20. 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

  1. Specification Mining for Intrusion Detection in Networked Control Systems

    NARCIS (Netherlands)

    Caselli, M.; Zambon, Emmanuele; Amann, Johanna; Sommer, Robin; Kargl, Frank

    2016-01-01

    This paper discusses a novel approach to specification-based intrusion detection in the field of networked control systems. Our approach reduces the substantial human effort required to deploy a specification-based intrusion detection system by automating the development of its specification rules.

  2. Bayesian network models for error detection in radiotherapy plans.

    Science.gov (United States)

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

    2015-04-07

    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.

  3. Achieving fast and stable failure detection in WDM Networks

    Science.gov (United States)

    Gao, Donghui; Zhou, Zhiyu; Zhang, Hanyi

    2005-02-01

    In dynamic networks, the failure detection time takes a major part of the convergence time, which is an important network performance index. To detect a node or link failure in the network, traditional protocols, like Hello protocol in OSPF or RSVP, exchanges keep-alive messages between neighboring nodes to keep track of the link/node state. But by default settings, it can get a minimum detection time in the measure of dozens of seconds, which can not meet the demands of fast network convergence and failure recovery. When configuring the related parameters to reduce the detection time, there will be notable instability problems. In this paper, we analyzed the problem and designed a new failure detection algorithm to reduce the network overhead of detection signaling. Through our experiment we found it is effective to enhance the stability by implicitly acknowledge other signaling messages as keep-alive messages. We conducted our proposal and the previous approaches on the ASON test-bed. The experimental results show that our algorithm gives better performances than previous schemes in about an order magnitude reduction of both false failure alarms and queuing delay to other messages, especially under light traffic load.

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

    Science.gov (United States)

    2017-04-01

    Undergrad. Res. Fellowship, visiting from Caltech. Undergraduate Eugene Park Math Duke Models of self-healing networks (undergrad. senior thesis...Graduate student Anastasia Deckard Math Duke 3rd/4th year PhD. Wrote software for simulation. Undergraduate Nick Day Math LIMS Summer project at...have been accepted by, or have been submitted to, peer-reviewed journals . Easily repairable networks R Farr, J Harer, T Fink Phys. Rev. Lett., 113, 13

  5. Acquisition in a World of Joint Capabilities: Methods for Understanding Cross-Organizational Network Performance

    Science.gov (United States)

    2016-04-30

    UNL Robert Flowe, Office of Acquisition Resources & Analysis, OUSD (AT&L) Brendan Fernes , Student, The Cooper Union An Optimization-Based Approach... diversity of network partners based upon the rank abundance curve. The β3 is the percent of network partners that are considered joint programs. The β4...program element. The second was a diversity measure. Diversity was measured by the slope of the rank abundance curve. The percent of the partners that

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

    Directory of Open Access Journals (Sweden)

    Tao Ma

    2016-10-01

    Full Text Available 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.

  7. 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.

  8. On the impact of a doubled sampling frequency on the detection capability and accuracy of a xenon station at the example of the German IMS RN station Schauinsland

    Science.gov (United States)

    Becker, Andreas; Schlosser, Clemens; Auer, Matthias; Gohla, Herbert; Kumberg, Timo; Wernsperger, Bernd

    2010-05-01

    In order to detect any kind of nuclear explosion world-wide the Provisional Technical Secretariat to the Comprehensive Nuclear-Test-Ban Treaty (CTBT) is building up a verification regime that performs global monitoring for typical signals expected from such an event. Backbone of this regime is the 321 facilities International Monitoring System (IMS) comprising also 80 stations to monitor for airborne radionuclides known to be fission or activation products of a nuclear explosion. Whereas particulate radionuclides are very likely fully contained in the cavity of an underground nuclear test explosion, radioactive noble gases bear a good chance to be still vented or seeped through the lithosphere into the atmosphere. As the corresponding relevant isotopes Xe-131m, Xe-133, Xe-133m, and Xe-135, which have the highest fission yields among the noble gases, are also not subdued to wet deposition in the atmosphere, they were regarded as important enough to add a xenon detection capability to 50% of the aforementioned 80 radionuclide stations. This, however, requires measurement methods being completely different to the one utilized for particulate monitoring. Despite tremendous progress that has been made with regard to the detection capability of radio-xenon systems in the past 10 years, gaining one order of magnitude in this metric, certain challenges still occur with regard to noble gas monitoring: • Only four xenon isotopes instead of more than 90 different particulate radio-isotopes are characteristic for the detection of a nuclear explosion with the IMS. • These four nuclides feature very different - abundances (background concentrations) that are strongly related to their different half-life times and the site. • There are known but CTBT irrelevant sources of radioxenon surrounding noble-gas stations at partly short distances (at least much shorter than the average station to station distance of the noble gas network). • Mountainous IMS stations and their

  9. Intrusion Detection Systems in Wireless Sensor Networks: A Review

    OpenAIRE

    Nabil Ali Alrajeh; Khan, S.; Bilal Shams

    2013-01-01

    Wireless Sensor Networks (WSNs) consist of sensor nodes deployed in a manner to collect information about surrounding environment. Their distributed nature, multihop data forwarding, and open wireless medium are the factors that make WSNs highly vulnerable to security attacks at various levels. Intrusion Detection Systems (IDSs) can play an important role in detecting and preventing security attacks. This paper presents current Intrusion Detection Systems and some open research problems relat...

  10. 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.

  11. 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.

  12. Bayesian network models for error detection in radiotherapy plans

    Science.gov (United States)

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

    2015-04-01

    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.

  13. A Comparative Analysis of Community Detection Algorithms on Artificial Networks.

    Science.gov (United States)

    Yang, Zhao; Algesheimer, René; Tessone, Claudio J

    2016-08-01

    Many community detection algorithms have been developed to uncover the mesoscopic properties of complex networks. However how good an algorithm is, in terms of accuracy and computing time, remains still open. Testing algorithms on real-world network has certain restrictions which made their insights potentially biased: the networks are usually small, and the underlying communities are not defined objectively. In this study, we employ the Lancichinetti-Fortunato-Radicchi benchmark graph to test eight state-of-the-art algorithms. We quantify the accuracy using complementary measures and algorithms' computing time. Based on simple network properties and the aforementioned results, we provide guidelines that help to choose the most adequate community detection algorithm for a given network. Moreover, these rules allow uncovering limitations in the use of specific algorithms given macroscopic network properties. Our contribution is threefold: firstly, we provide actual techniques to determine which is the most suited algorithm in most circumstances based on observable properties of the network under consideration. Secondly, we use the mixing parameter as an easily measurable indicator of finding the ranges of reliability of the different algorithms. Finally, we study the dependency with network size focusing on both the algorithm's predicting power and the effective computing time.

  14. 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.

  15. 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 appropriately named Link-ZA. This paper expands on the implementation evolution and challenges of the standard over the last 10 years and provides a generic TDL Capability Model with a strategy for establishing interoperability between different implementations...

  16. 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.

  17. 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.

  18. Using new edges for anomaly detection in computer networks

    Energy Technology Data Exchange (ETDEWEB)

    Neil, Joshua Charles

    2017-07-04

    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.

  19. 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.

  20. Radiation detection and wireless networked early warning

    Science.gov (United States)

    Burns, David A.; Litz, Marc S.; Carroll, James J.; Katsis, Dimosthenis

    2012-06-01

    We have designed a compact, wireless, GPS-enabled array of inexpensive radiation sensors based on scintillation counting. Each sensor has a scintillator, photomultiplier tube, and pulse-counting circuit that includes a comparator, digital potentiometer and microcontroller. This design provides a high level of sensitivity and reliability. A 0.2 m2 PV panel powers each sensor providing a maintenance-free 24/7 energy source. The sensor can be mounted within a roadway light-post and monitor radiological activity along transport routes. Each sensor wirelessly transmits real-time data (as counts per second) up to 2 miles with a XBee radio module, and the data is received by a XBee receive-module on a computer. Data collection software logs the information from all sensors and provides real-time identification of radiation events. Measurements performed to-date demonstrate the ability of a sensor to detect a 20 μCi source at 3.5 meters when packaged with a PVT (plastic) scintillator, and 7 meters for a sensor with a CsI crystal (more expensive but ~5 times more sensitive). It is calculated that the sensor-architecture can detect sources moving as fast as 130 km/h based on the current data rate and statistical bounds of 3-sigma threshold detection. The sensor array is suitable for identifying and tracking a radiation threat from a dirty bomb along roadways.

  1. An Optimized Hidden Node Detection Paradigm for Improving the Coverage and Network Efficiency in Wireless Multimedia Sensor Networks

    Directory of Open Access Journals (Sweden)

    Adwan Alanazi

    2016-09-01

    Full Text Available Successful transmission of online multimedia streams in wireless multimedia sensor networks (WMSNs is a big challenge due to their limited bandwidth and power resources. The existing WSN protocols are not completely appropriate for multimedia communication. The effectiveness of WMSNs varies, and it depends on the correct location of its sensor nodes in the field. Thus, maximizing the multimedia coverage is the most important issue in the delivery of multimedia contents. The nodes in WMSNs are either static or mobile. Thus, the node connections change continuously due to the mobility in wireless multimedia communication that causes an additional energy consumption, and synchronization loss between neighboring nodes. In this paper, we introduce an Optimized Hidden Node Detection (OHND paradigm. The OHND consists of three phases: hidden node detection, message exchange, and location detection. These three phases aim to maximize the multimedia node coverage, and improve energy efficiency, hidden node detection capacity, and packet delivery ratio. OHND helps multimedia sensor nodes to compute the directional coverage. Furthermore, an OHND is used to maintain a continuous node– continuous neighbor discovery process in order to handle the mobility of the nodes. We implement our proposed algorithms by using a network simulator (NS2. The simulation results demonstrate that nodes are capable of maintaining direct coverage and detecting hidden nodes in order to maximize coverage and multimedia node mobility. To evaluate the performance of our proposed algorithms, we compared our results with other known approaches.

  2. An Optimized Hidden Node Detection Paradigm for Improving the Coverage and Network Efficiency in Wireless Multimedia Sensor Networks

    Science.gov (United States)

    Alanazi, Adwan; Elleithy, Khaled

    2016-01-01

    Successful transmission of online multimedia streams in wireless multimedia sensor networks (WMSNs) is a big challenge due to their limited bandwidth and power resources. The existing WSN protocols are not completely appropriate for multimedia communication. The effectiveness of WMSNs varies, and it depends on the correct location of its sensor nodes in the field. Thus, maximizing the multimedia coverage is the most important issue in the delivery of multimedia contents. The nodes in WMSNs are either static or mobile. Thus, the node connections change continuously due to the mobility in wireless multimedia communication that causes an additional energy consumption, and synchronization loss between neighboring nodes. In this paper, we introduce an Optimized Hidden Node Detection (OHND) paradigm. The OHND consists of three phases: hidden node detection, message exchange, and location detection. These three phases aim to maximize the multimedia node coverage, and improve energy efficiency, hidden node detection capacity, and packet delivery ratio. OHND helps multimedia sensor nodes to compute the directional coverage. Furthermore, an OHND is used to maintain a continuous node– continuous neighbor discovery process in order to handle the mobility of the nodes. We implement our proposed algorithms by using a network simulator (NS2). The simulation results demonstrate that nodes are capable of maintaining direct coverage and detecting hidden nodes in order to maximize coverage and multimedia node mobility. To evaluate the performance of our proposed algorithms, we compared our results with other known approaches. PMID:27618048

  3. Exploring the limits of community detection strategies in complex networks

    OpenAIRE

    Aldecoa, Rodrigo; Marín, Ignacio

    2013-01-01

    The characterization of network community structure has profound implications in several scientific areas. Therefore, testing the algorithms developed to establish the optimal division of a network into communities is a fundamental problem in the field. We performed here a highly detailed evaluation of community detection algorithms, which has two main novelties: 1) using complex closed benchmarks, which provide precise ways to assess whether the solutions generated by the algorithms are opti...

  4. 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.

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

    Science.gov (United States)

    Kim, Jinki; Yi, Gwan-Su

    2013-01-01

    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.

  6. 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.

  7. Exploring the limits of community detection strategies in complex networks.

    Science.gov (United States)

    Aldecoa, Rodrigo; Marín, Ignacio

    2013-01-01

    The characterization of network community structure has profound implications in several scientific areas. Therefore, testing the algorithms developed to establish the optimal division of a network into communities is a fundamental problem in the field. We performed here a highly detailed evaluation of community detection algorithms, which has two main novelties: 1) using complex closed benchmarks, which provide precise ways to assess whether the solutions generated by the algorithms are optimal; and, 2) A novel type of analysis, based on hierarchically clustering the solutions suggested by multiple community detection algorithms, which allows to easily visualize how different are those solutions. Surprise, a global parameter that evaluates the quality of a partition, confirms the power of these analyses. We show that none of the community detection algorithms tested provide consistently optimal results in all networks and that Surprise maximization, obtained by combining multiple algorithms, obtains quasi-optimal performances in these difficult benchmarks.

  8. 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%.

  9. Fiber Bragg Grating sensor for fault detection in radial and network transmission lines.

    Science.gov (United States)

    Moghadas, Amin A; Shadaram, Mehdi

    2010-01-01

    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.

  10. 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.

  11. Comparison of the Performance and Capabilities of Femtocell versus Wi-Fi Networks

    Science.gov (United States)

    2012-09-01

    of Electrical and Electronics Engineers IKEV2 Internet Key Exchange Version 2 INTSERV Integrated Services IP Internet Protocol IPSEC ...correctly identify valid femtocells within the network. Another means of ensuring security is the use of Internet Protocol Security ( IPsec ). IPsec is a...gateway uses Internet Protocol Security ( IPSec ) and Internet Key Exchange (IKEv2) Internet security protocols for encryption support and for the

  12. Applying the concept of network enabled capabilities to incident management in the Netherlands

    NARCIS (Netherlands)

    Immers, L.H.; Huisken, G.

    2008-01-01

    The application of Incident Management to the Dutch road network suffers from serious problems in terms of availability of accurate and up-to-date information. In this paper we present an approach aimed at diminishing the occurrence of misunderstandings. This approach is based on the concept of

  13. Auditory Display as a Tool for Teaching Network Intrusion Detection

    Directory of Open Access Journals (Sweden)

    M.A. Garcia-Ruiz

    2008-06-01

    Full Text Available Teaching network intrusion detection, or NID(the identification of violations of a security policy in acomputer network is a challenging task, because studentsneed to analyze many data from network logs and in realtime to identify patterns of network attacks, making theseactivities visually tiring. This paper describes an ongoingresearch concerned with designing and applying sounds thatrepresent meaningful information in interfaces(sonification to support teaching of NID. An usability testwas conducted with engineering students. Natural soundeffects (auditory icons and musical sounds (earcons wereused to represent network attacks. A post-activityquestionnaire showed that most students preferred auditoryicons for analyzing NID, and all of them were veryinterested in the design and application of sonifications.

  14. Detecting Statistically Significant Communities of Triangle Motifs in Undirected Networks

    Science.gov (United States)

    2016-04-26

    Granovetter, M. (1983), “The strength of weak ties: A network theory revisited,” Sociological Theory 1 pp. 201-233. [4] Lancichinetti, A., Fortunato, S...AFRL-AFOSR-UK-TR-2015-0025 Detecting Statistically Signicant Communities of Triangle Motifs in Undirected Networks Marcus Perry IMPERIAL COLLEGE OF...triangle motifs in undirected networks 5a.  CONTRACT NUMBER 5b.  GRANT NUMBER FA9550-15-1-0019 5c.  PROGRAM ELEMENT NUMBER 61102F 6. AUTHOR(S) Marcus Perry

  15. 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

  16. Community detection in complex networks via adapted Kuramoto dynamics

    Science.gov (United States)

    Maia, Daniel M. N.; de Oliveira, João E. M.; Quiles, Marcos G.; Macau, Elbert E. N.

    2017-12-01

    Based on the Kuramoto model, a new network model, namely, the generalized Kuramoto model with Fourier term, is introduced for studying community detection in complex networks. In particular, the Fourier term provides a natural phase locking of the trajectories into a pre-defined number of clusters. A mathematical approach is used to study the behavior of the solutions and its properties. Conditions for properly choosing the coupling parameters so that phase locking takes place are presented and a quality function called clustering density is introduced to measure the effectiveness of the communities identification. Illustrations with real and synthetic networks with community structure are presented.

  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. Weak electric fields detectability in a noisy neural network.

    Science.gov (United States)

    Zhao, Jia; Deng, Bin; Qin, Yingmei; Men, Cong; Wang, Jiang; Wei, Xile; Sun, Jianbing

    2017-02-01

    We investigate the detectability of weak electric field in a noisy neural network based on Izhikevich neuron model systematically. The neural network is composed of excitatory and inhibitory neurons with similar ratio as that in the mammalian neocortex, and the axonal conduction delays between neurons are also considered. It is found that the noise intensity can modulate the detectability of weak electric field. Stochastic resonance (SR) phenomenon induced by white noise is observed when the weak electric field is added to the network. It is interesting that SR almost disappeared when the connections between neurons are cancelled, suggesting the amplification effects of the neural coupling on the synchronization of neuronal spiking. Furthermore, the network parameters, such as the connection probability, the synaptic coupling strength, the scale of neuron population and the neuron heterogeneity, can also affect the detectability of the weak electric field. Finally, the model sensitivity is studied in detail, and results show that the neural network model has an optimal region for the detectability of weak electric field signal.

  19. Module detection in complex networks using integer optimisation

    Science.gov (United States)

    2010-01-01

    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. PMID:21073720

  20. 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.

  1. Analysis of the Deployment Quality for Intrusion Detection in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Noureddine Assad

    2015-01-01

    Full Text Available The intrusion detection application in a homogeneous wireless sensor network is defined as a mechanism to detect unauthorized intrusions or anomalous moving attackers in a field of interest. The quality of deterministic sensor nodes deployment can be determined sufficiently by a rigorous analysis before the deployment. However, when random deployment is required, determining the deployment quality becomes challenging. An area may require that multiple nodes monitor each point from the sensing area; this constraint is known as k-coverage where k is the number of nodes. The deployment quality of sensor nodes depends directly on node density and sensing range; mainly a random sensor nodes deployment is required. The major question is centred around the problem of network coverage, how can we guarantee that each point of the sensing area is covered by the required number of sensor nodes and what a sufficient condition to guarantee the network coverage? To deal with this, probabilistic intrusion detection models are adopted, called single/multi-sensing detection, and the deployment quality issue is surveyed and analysed in terms of coverage. We evaluate the capability of our probabilistic model in homogeneous wireless sensor network, in terms of sensing range, node density, and intrusion distance.

  2. Enterprise network intrusion detection and prevention system (ENIDPS)

    Science.gov (United States)

    Akujuobi, C. M.; Ampah, N. K.

    2007-04-01

    Securing enterprise networks comes under two broad topics: Intrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS). The right combination of selected algorithms/techniques under both topics produces better security for a given network. This approach leads to using layers of physical, administrative, electronic, and encrypted systems to protect valuable resources. So far, there is no algorithm, which guarantees absolute protection for a given network from intruders. Intrusion Prevention Systems like IPSec, Firewall, Sender ID, Domain Keys Identified Mail (DKIM) etc. do not guarantee absolute security just like existing Intrusion Detection Systems. Our approach focuses on developing an IDS, which will detect all intruders that bypass the IPS and at the same time will be used in updating the IPS, since the IPS fail to prevent some intruders from entering a given network. The new IDS will employ both signature-based detection and anomaly detection as its analysis strategy. It should therefore be able to detect known and unknown intruders or attacks and further isolate those sources of attack within the network. Both real-time and off-line IDS predictions will be applied under the analysis and response stages. The basic IDS architecture will involve both centralized and distributed/heterogeneous architecture to ensure effective detection. Pro-active responses and corrective responses will be employed. The new security system, which will be made up of both IDS and IPS, should be less expensive to implement compared to existing ones. Finally, limitations of existing security systems have to be eliminated with the introduction of the new security system.

  3. 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.

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

    DEFF Research Database (Denmark)

    Mykhaylenko, Alona

    ENGLISH SUMMARY The challenges and opportunities of globalisation tempt firms to reconfigure their operations and relocate (or offshore) various activities to the most advantageous destinations. Such offshore operations tend to gradually become complex and intertwined, leading to the transition....... 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...... of experiential knowledge enabling the intra-organisational network evolution process, its drivers, and relationships between the parts of the model. The findings also suggest the existence of distinguishable evolutionary stages. Additionally, the resu lts indicate that changes in particular network configuration...

  5. WHY ENTREPRENEUR OVERCONFIDENCE AFFECT ITS PROJECT FINANCIAL CAPABILITY: EVIDENCE FROM TUNISIA USING THE BAYESIAN NETWORK METHOD

    OpenAIRE

    Salima TAKTAK; AZOUZI Mohamed Ali; Triki, Mohamed

    2013-01-01

    This article discusses the effect of the entrepreneur’s profile on financing his creative project. It analyzes the impact of overconfidence on improving perceptions financing capacity of the project. To analyze this relationship we used networks as Bayesian data analysis method. Our sample is composed of 200 entrepreneurs. Our results show a high level of entrepreneur’s overconfidence positively affects the evaluation of financing capacity of the project.

  6. 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.

  7. Graph spectra and the detectability of community structure in networks.

    Science.gov (United States)

    Nadakuditi, Raj Rao; Newman, M E J

    2012-05-04

    We study networks that display community structure--groups of nodes within which connections are unusually dense. Using methods from random matrix theory, we calculate the spectra of such networks in the limit of large size, and hence demonstrate the presence of a phase transition in matrix methods for community detection, such as the popular modularity maximization method. The transition separates a regime in which such methods successfully detect the community structure from one in which the structure is present but is not detected. By comparing these results with recent analyses of maximum-likelihood methods, we are able to show that spectral modularity maximization is an optimal detection method in the sense that no other method will succeed in the regime where the modularity method fails.

  8. 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

    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...... 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....

  9. Topology detection for adaptive protection of distribution networks

    Energy Technology Data Exchange (ETDEWEB)

    Sachdev, M.S.; Sidhu, T.S.; Talukdar, B.K. [Univ. of Saskatchewan, Saskatoon, Saskatchewan (Canada). Power System Research Group

    1995-12-31

    A general purpose network topology detection technique suitable for use in adaptive relaying applications is presented in this paper. Three test systems were used to check the performance of the proposed technique. Results obtained from the tests are included. The proposed technique was implemented in the laboratory as a part of the implementation of the adaptive protection scheme. The execution times of the topology detection software were monitored and were found to be acceptable.

  10. 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.

  11. ENTREPRENEURIAL CAPABILITIES

    DEFF Research Database (Denmark)

    Rasmussen, Lauge Baungaard; Nielsen, Thorkild

    2003-01-01

    The aim of this article is to analyse entrepreneurship from an action research perspective. What is entrepreneurship about? Which are the fundamental capabilities and processes of entrepreneurship? To answer these questions the article includes a case study of a Danish entrepreneur and his networks...

  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. On the Capability of Artificial Neural Networks to Compensate Nonlinearities in Wavelength Sensing

    Science.gov (United States)

    Hafiane, Mohamed Lamine; Dibi, Zohir; Manck, Otto

    2009-01-01

    An intelligent sensor for light wavelength readout, suitable for visible range optical applications, has been developed. Using buried triple photo-junction as basic pixel sensing element in combination with artificial neural network (ANN), the wavelength readout with a full-scale error of less than 1.5% over the range of 400 to 780 nm can be achieved. Through this work, the applicability of the ANN approach in optical sensing is investigated and compared with conventional methods, and a good compromise between accuracy and the possibility for on-chip implementation was thus found. Indeed, this technique can serve different purposes and may replace conventional methods. PMID:22574051

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

    Science.gov (United States)

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

    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.

  16. Analysis of Community Detection Algorithms for Large Scale Cyber Networks

    Energy Technology Data Exchange (ETDEWEB)

    Mane, Prachita; Shanbhag, Sunanda; Kamath, Tanmayee; Mackey, Patrick S.; Springer, John

    2016-09-30

    The aim of this project is to use existing community detection algorithms on an IP network dataset to create supernodes within the network. This study compares the performance of different algorithms on the network in terms of running time. The paper begins with an introduction to the concept of clustering and community detection followed by the research question that the team aimed to address. Further the paper describes the graph metrics that were considered in order to shortlist algorithms followed by a brief explanation of each algorithm with respect to the graph metric on which it is based. The next section in the paper describes the methodology used by the team in order to run the algorithms and determine which algorithm is most efficient with respect to running time. Finally, the last section of the paper includes the results obtained by the team and a conclusion based on those results as well as future work.

  17. 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.

  18. Automatic Data Collection Design for Neural Networks Detection of ...

    African Journals Online (AJOL)

    However, in Nigeria, collecting fraudulent data is relatively difficult and the human labour involved is expensive and risky. This paper examines some formal procedures for data collection and proposes designing an automatic data collection system for detection of occupational frauds using artificial neural networks.

  19. 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…

  20. 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...

  1. automatic data collection design for neural networks detection

    African Journals Online (AJOL)

    Dr Obe

    data collection system for detection of occupational frauds using artificial neural networks. .... an issue). Limitations. (i) Little flexibility for people to raise their own issues (ii) Little opportunity for people to respond in their own words (iii) Little opportunity to go into depth on any issue (iv) ..... Lecture notes in Artificial Intelligence.

  2. 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

  3. 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

  4. 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

  5. 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

  6. Early detection network design and search strategy issues

    Science.gov (United States)

    We conducted a series of field and related modeling studies (2005-2012) to evaluate search strategies for Great Lakes coastal ecosystems that are at risk of invasion by non-native aquatic species. In developing a network, we should design to achieve an acceptable limit of detect...

  7. A Vehicle Detection Algorithm Based on Deep Belief Network

    Directory of Open Access Journals (Sweden)

    Hai Wang

    2014-01-01

    Full Text Available Vision based vehicle detection is a critical technology that plays an important role in not only vehicle active safety but also road video surveillance application. Traditional shallow model based vehicle detection algorithm still cannot meet the requirement of accurate vehicle detection in these applications. In this work, a novel deep learning based vehicle detection algorithm with 2D deep belief network (2D-DBN is proposed. In the algorithm, the proposed 2D-DBN architecture uses second-order planes instead of first-order vector as input and uses bilinear projection for retaining discriminative information so as to determine the size of the deep architecture which enhances the success rate of vehicle detection. On-road experimental results demonstrate that the algorithm performs better than state-of-the-art vehicle detection algorithm in testing data sets.

  8. RDR-4B doppler weather radar with forward looking wind shear detection capability

    Science.gov (United States)

    Grasley, Steven S.

    1992-01-01

    The topics are presented in viewgraph form and include the following: Bendix/King atmospheric transport and dispersion (ATAD) position; RDR-4A technical baseline; RTA-4A characteristics; RDR-4 antenna characteristics; modification of RDR-4A to RDR-4B; RDR-4A functional block diagram; RDR-4B characteristics; development/test plan; CV-580 testing capability; CV-580 test results; Continental A300 test configuration; Continental Data Recording Program operational considerations; Continental A300 test results; and display considerations.

  9. Optimal Power Allocation of Relay Sensor Node Capable of Energy Harvesting in Cooperative Cognitive Radio Network.

    Science.gov (United States)

    Son, Pham Ngoc; Har, Dongsoo; Cho, Nam Ik; Kong, Hyung Yun

    2017-03-21

    A cooperative cognitive radio scheme exploiting primary signals for energy harvesting is proposed. The relay sensor node denoted as the secondary transmitter (ST) harvests energy from the primary signal transmitted from the primary transmitter, and then uses it to transmit power superposed codes of the secrecy signal of the secondary network (SN) and of the primary signal of the primary network (PN). The harvested energy is split into two parts according to a power splitting ratio, one for decoding the primary signal and the other for charging the battery. In power superposition coding, the amount of fractional power allocated to the primary signal is determined by another power allocation parameter (e.g., the power sharing coefficient). Our main concern is to investigate the impact of the two power parameters on the performances of the PN and the SN. Analytical or mathematical expressions of the outage probabilities of the PN and the SN are derived in terms of the power parameters, location of the ST, channel gain, and other system related parameters. A jointly optimal power splitting ratio and power sharing coefficient for achieving target outage probabilities of the PN and the SN, are found using these expressions and validated by simulations.

  10. 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.)

  11. Detection of Significant Pneumococcal Meningitis Biomarkers by Ego Network.

    Science.gov (United States)

    Wang, Qian; Lou, Zhifeng; Zhai, Liansuo; Zhao, Haibin

    2017-06-01

    To identify significant biomarkers for detection of pneumococcal meningitis based on ego network. Based on the gene expression data of pneumococcal meningitis and global protein-protein interactions (PPIs) data recruited from open access databases, the authors constructed a differential co-expression network (DCN) to identify pneumococcal meningitis biomarkers in a network view. Here EgoNet algorithm was employed to screen the significant ego networks that could accurately distinguish pneumococcal meningitis from healthy controls, by sequentially seeking ego genes, searching candidate ego networks, refinement of candidate ego networks and significance analysis to identify ego networks. Finally, the functional inference of the ego networks was performed to identify significant pathways for pneumococcal meningitis. By differential co-expression analysis, the authors constructed the DCN that covered 1809 genes and 3689 interactions. From the DCN, a total of 90 ego genes were identified. Starting from these ego genes, three significant ego networks (Module 19, Module 70 and Module 71) that could predict clinical outcomes for pneumococcal meningitis were identified by EgoNet algorithm, and the corresponding ego genes were GMNN, MAD2L1 and TPX2, respectively. Pathway analysis showed that these three ego networks were related to CDT1 association with the CDC6:ORC:origin complex, inactivation of APC/C via direct inhibition of the APC/C complex pathway, and DNA strand elongation, respectively. The authors successfully screened three significant ego modules which could accurately predict the clinical outcomes for pneumococcal meningitis and might play important roles in host response to pathogen infection in pneumococcal meningitis.

  12. Effects of oral administration of metronidazole and doxycycline on olfactory capabilities of explosives detection dogs.

    Science.gov (United States)

    Jenkins, Eileen K; Lee-Fowler, Tekla M; Angle, T Craig; Behrend, Ellen N; Moore, George E

    2016-08-01

    OBJECTIVE To determine effects of oral administration of metronidazole or doxycycline on olfactory function in explosives detection (ED) dogs. ANIMALS 18 ED dogs. PROCEDURES Metronidazole was administered (25 mg/kg, PO, q 12 h for 10 days); the day prior to drug administration was designated day 0. Odor detection threshold was measured with a standard scent wheel and 3 explosives (ammonium nitrate, trinitrotoluene, and smokeless powder; weight, 1 to 500 mg) on days 0, 5, and 10. Lowest repeatable weight detected was recorded as the detection threshold. There was a 10-day washout period, and doxycycline was administered (5 mg/kg, PO, q 12 h for 10 days) and the testing protocol repeated. Degradation changes in the detection threshold for dogs were assessed. RESULTS Metronidazole administration resulted in degradation of the detection threshold for 2 of 3 explosives (ammonium nitrate and trinitrotoluene). Nine of 18 dogs had a degradation of performance in response to 1 or more explosives (5 dogs had degradation on day 5 or 10 and 4 dogs had degradation on both days 5 and 10). There was no significant degradation during doxycycline administration. CONCLUSIONS AND CLINICAL RELEVANCE Degradation in the ability to detect odors of explosives during metronidazole administration at 25 mg/kg, PO, every 12 hours, indicated a potential risk for use of this drug in ED dogs. Additional studies will be needed to determine whether lower doses would have the same effect. Doxycycline administered at the tested dose appeared to be safe for use in ED dogs.

  13. Fuzzy analysis of community detection in complex networks

    Science.gov (United States)

    Zhang, Dawei; Xie, Fuding; Zhang, Yong; Dong, Fangyan; Hirota, Kaoru

    2010-11-01

    A snowball algorithm is proposed to find community structures in complex networks by introducing the definition of community core and some quantitative conditions. A community core is first constructed, and then its neighbors, satisfying the quantitative conditions, will be tied to this core until no node can be added. Subsequently, one by one, all communities in the network are obtained by repeating this process. The use of the local information in the proposed algorithm directly leads to the reduction of complexity. The algorithm runs in O(n+m) time for a general network and O(n) for a sparse network, where n is the number of vertices and m is the number of edges in a network. The algorithm fast produces the desired results when applied to search for communities in a benchmark and five classical real-world networks, which are widely used to test algorithms of community detection in the complex network. Furthermore, unlike existing methods, neither global modularity nor local modularity is utilized in the proposal. By converting the considered problem into a graph, the proposed algorithm can also be applied to solve other cluster problems in data mining.

  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. BOUNDARY DETECTION ALGORITHMS IN WIRELESS SENSOR NETWORKS: A SURVEY

    Directory of Open Access Journals (Sweden)

    Lanny Sitanayah

    2009-01-01

    Full Text Available Wireless sensor networks (WSNs comprise a large number of sensor nodes, which are spread out within a region and communicate using wireless links. In some WSN applications, recognizing boundary nodes is important for topology discovery, geographic routing and tracking. In this paper, we study the problem of recognizing the boundary nodes of a WSN. We firstly identify the factors that influence the design of algorithms for boundary detection. Then, we classify the existing work in boundary detection, which is vital for target tracking to detect when the targets enter or leave the sensor field.

  16. Analysis of Intrusion Detection and Attack Proliferation in Computer Networks

    Science.gov (United States)

    Rangan, Prahalad; Knuth, Kevin H.

    2007-11-01

    One of the popular models to describe computer worm propagation is the Susceptible-Infected (SI) model [1]. This model of worm propagation has been implemented on the simulation toolkit Network Simulator v2 (ns-2) [2]. The ns-2 toolkit has the capability to simulate networks of different topologies. The topology studied in this work, however, is that of a simple star-topology. This work introduces our initial efforts to learn the relevant quantities describing an infection given synthetic data obtained from running the ns-2 worm model. We aim to use Bayesian methods to gain a predictive understanding of how computer infections spread in real world network topologies. This understanding would greatly reinforce dissemination of targeted immunization strategies, which may prevent real-world epidemics. The data consist of reports of infection from a subset of nodes in a large network during an attack. The infection equation obtained from [1] enables us to derive a likelihood function for the infection reports. This prior information can be used in the Bayesian framework to obtain the posterior probabilities for network properties of interest, such as the rate at which nodes contact one another (also referred to as contact rate or scan rate). Our preliminary analyses indicate an effective spread rate of only 1/5th the actual scan rate used for a star-type of topology. This implies that as the population becomes saturated with infected nodes the actual spread rate will become much less than the scan rate used in the simulation.

  17. 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.

  18. Analysis of Eddy Current Capabilities for the Detection of Outer Diameter Stress Corrosion Cracking in Small Bore Metallic Structures

    Science.gov (United States)

    Wincheski, Buzz; Williams, Phillip; Simpson, John

    2007-01-01

    The use of eddy current techniques for the detection of outer diameter damage in tubing and many complex aerospace structures often requires the use of an inner diameter probe due to a lack of access to the outside of the part. In small bore structures the probe size and orientation are constrained by the inner diameter of the part, complicating the optimization of the inspection technique. Detection of flaws through a significant remaining wall thickness becomes limited not only by the standard depth of penetration, but also geometrical aspects of the probe. Recently, an orthogonal eddy current probe was developed for detection of such flaws in Space Shuttle Primary Reaction Control System (PRCS) Thrusters. In this case, the detection of deeply buried stress corrosion cracking by an inner diameter eddy current probe was sought. Probe optimization was performed based upon the limiting spatial dimensions, flaw orientation, and required detection sensitivity. Analysis of the probe/flaw interaction was performed through the use of finite and boundary element modeling techniques. Experimental data for the flaw detection capabilities, including a probability of detection study, will be presented along with the simulation data. The results of this work have led to the successful deployment of an inspection system for the detection of stress corrosion cracking in Space Shuttle Primary Reaction Control System (PRCS) Thrusters.

  19. 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...

  20. Robust Meter Network for Water Distribution Pipe Burst Detection

    Directory of Open Access Journals (Sweden)

    Donghwi Jung

    2017-10-01

    Full Text Available 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 meter placement model that maximizes the detection probability, minimizes false alarms, and maximizes the robustness of a meter network given a predefined number of meters. A meter network’s robustness is defined as its ability to consistently provide quality data in the event of meter failure. Based on a single-meter failure simulation, a robustness indicator for the meter network is introduced and maximized as the third objective of the proposed model. The proposed model was applied to the Austin network to determine the independent placement of pipe flow and pressure meters with three or five available meters. The results showed that the proposed model is a useful tool for determining meter locations to secure high detectability and robustness.

  1. 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.

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

    CERN Document Server

    Mirjalily, G

    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 exa...

  3. Glomerulus Classification and Detection Based on Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Jaime Gallego

    2018-01-01

    Full Text Available Glomerulus classification and detection in kidney tissue segments are key processes in nephropathology used for the correct diagnosis of the diseases. In this paper, we deal with the challenge of automating Glomerulus classification and detection from digitized kidney slide segments using a deep learning framework. The proposed method applies Convolutional Neural Networks (CNNs between two classes: Glomerulus and Non-Glomerulus, to detect the image segments belonging to Glomerulus regions. We configure the CNN with the public pre-trained AlexNet model and adapt it to our system by learning from Glomerulus and Non-Glomerulus regions extracted from training slides. Once the model is trained, labeling is performed by applying the CNN classification to the image blocks under analysis. The results of the method indicate that this technique is suitable for correct Glomerulus detection in Whole Slide Images (WSI, showing robustness while reducing false positive and false negative detections.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. A New Fault-tolerant Switched Reluctance Motor with reliable fault detection capability

    DEFF Research Database (Denmark)

    Lu, Kaiyuan

    2014-01-01

    Fault-Tolerant Switched Reluctance (FTSR) motor is proposed in this paper. A unique feature of this special design is that it allows use of the unexcited phase coils as search coils for fault detection. Therefore this new motor has all the advantages of using search coils for reliable fault detection......For reliable fault detection, often, search coils are used in many fault-tolerant drives. The search coils occupy extra slot space. They are normally open-circuited and are not used for torque production. This degrades the motor performance, increases the cost and manufacture complexity. A new...... while no extra search coil is actually needed. The motor itself is able to continue to work under any faulted conditions, providing fault-tolerant features. The working principle, performance evaluation of this motor will be demonstrated in this paper and Finite Element Analysis results are provided....

  9. A graph clustering method for community detection in complex networks

    Science.gov (United States)

    Zhou, HongFang; Li, Jin; Li, JunHuai; Zhang, FaCun; Cui, YingAn

    2017-03-01

    Information mining from complex networks by identifying communities is an important problem in a number of research fields, including the social sciences, biology, physics and medicine. First, two concepts are introduced, Attracting Degree and Recommending Degree. Second, a graph clustering method, referred to as AR-Cluster, is presented for detecting community structures in complex networks. Third, a novel collaborative similarity measure is adopted to calculate node similarities. In the AR-Cluster method, vertices are grouped together based on calculated similarity under a K-Medoids framework. Extensive experimental results on two real datasets show the effectiveness of AR-Cluster.

  10. High impedance fault detection in low voltage networks

    Energy Technology Data Exchange (ETDEWEB)

    Christie, R.D. (Univ. of Washington, Seattle, WA (United States). Dept. of Electrical Engineering); Zadehgol, H.; Habib, M.M. (Seattle City Light, WA (United States))

    1993-10-01

    High impedance faults are those with fault current magnitude similar to load currents. Experimental results were obtained that conform operating experience that such faults can occur in the low voltage (600V and below) underground distribution networks typically found in urban power systems. These faults produce current waveforms qualitatively similar to those found on overhead feeders, but quantitatively smaller. Loose connectors can produce similar, but cleaner current characteristics. Noisy loads remain a major impediment to reliable detection. Design and installation of an inexpensive prototype fault detector on the Seattle City Light street network is described.

  11. Network Intrusion Detection System – A Novel Approach

    Directory of Open Access Journals (Sweden)

    Krish Pillai

    2013-08-01

    Full Text Available Network intrusion starts off with a series of unsuccessful breakin attempts and results eventually with the permanent or transient failure of an authentication or authorization system. Due to the current complexity of authentication systems, clandestine attempts at intrusion generally take considerable time before the system gets compromised or damaging change is affected to the system giving administrators a window of opportunity to proactively detect and prevent intrusion. Therefore maintaining a high level of sensitivity to abnormal access patterns is a very effective way of preventing possible break-ins. Under normal circumstances, gross errors on the part of the user can cause authentication and authorization failures on all systems. A normal distribution of failed attempts should be tolerated while abnormal attempts should be recognized as such and flagged. But one cannot manage what one cannot measure. This paper proposes a method that can efficiently quantify the behaviour of users on a network so that transient changes in usage can be detected, categorized based on severity, and closely investigated for possible intrusion. The author proposes the identification of patterns in protocol usage within a network to categorize it for surveillance. Statistical anomaly detection, under which category this approach falls, generally uses simple statistical tests such as mean and standard deviation to detect behavioural changes. The author proposes a novel approach using spectral density as opposed to using time domain data, allowing a clear separation or access patterns based on periodicity. Once a spectral profile has been identified for network, deviations from this profile can be used as an indication of a destabilized or compromised network. Spectral analysis of access patterns is done using the Fast Fourier Transform (FFT, which can be computed in Θ(N log N operations. The paper justifies the use of this approach and presents preliminary

  12. Mars Biosignature - Detection Capabilities: A Method for Objective Comparison of In Situ Measurements and Sample Return

    Science.gov (United States)

    Weisbin, Charles R.; Lincoln, William; Papanastassiou, Dimitri A.; Coleman, Max L.

    2013-01-01

    A Mars sample-return mission has been proposed within NASA's Mars Exploration Program. Studying Martian samples in laboratories on Earth could address many important issues in planetary science, but arguably none is as scientifically compelling as the question of whether biosignatures indicative of past or present life exist on that planet. It is reasonable to ask before embarking on a sample-return mission whether equivalent investigation of Martian biosignatures could be conducted in situ. This study presents an approach to (1)identifying an optimal instrument suite for in situ detection of biosignatures on Mars,and (2)comparing the projected confidence level of in situ detection in a 2026 timeframe to that of Earth-based analysis. We identify a set of candidate instruments, the development of which is projected to be achievable by 2026 well within a $200 million cost cap. Assuming that any biosignatures near the surface of Mars are similar to those of terrestrial life, we find that this instrument suite, if successfully developed and deployed, would enable in situ biosignature detection at essentially the same level of confidence as that of Earth-based analysis of the same samples. At a cost cap of half that amount,the confidence level of in situbiosignature detection analysis could reach about 90% that of Earth-based investigations.

  13. Evaluation of the GPM-DPR snowfall detection capability: Comparison with CloudSat-CPR

    Science.gov (United States)

    Casella, Daniele; Panegrossi, Giulia; Sanò, Paolo; Marra, Anna Cinzia; Dietrich, Stefano; Johnson, Benjamin T.; Kulie, Mark S.

    2017-11-01

    An important objective of the Global Precipitation Measurement (GPM) mission is the detection of falling snow, since it accounts for a significant fraction of precipitation in the mid-high latitudes. The GPM Core Observatory carries the first spaceborne Dual-frequency Precipitation Radar (DPR), designed with enhanced sensitivity to detect lighter liquid and solid precipitation. The primary goal of this study is to assess the DPR's ability to identify snowfall using near-coincident CloudSat Cloud Profiling Radar (CPR) observations and products as an independent reference dataset. CloudSat near global coverage and high sensitivity of the W-band CPR make it very suitable for snowfall-related research. While DPR/CPR radar sensitivity disparities contribute substantially to snowfall detection differences, this study also analyzes other factors such as precipitation phase discriminators that produce snowfall identification discrepancies. Results show that even if the occurrence of snowfall events correctly detected by DPR products is quite small compared to CPR (around 5-7%), the fraction of snowfall mass is not negligible (29-34%). A direct comparison of CPR and DPR reflectivities illustrates that DPR misdetection is worsened by a noise-reducing DPR algorithm component that corrects the measured reflectivity. This procedure eliminates the receiver noise and side lobe clutter effects, but also removes radar signals related to snowfall events that are associated with relatively low reflectivity values. In an effort to increase DPR signal fidelity associated with snowfall, this paper proposes a simple algorithm using matched DPR Ku/Ka radar reflectivities producing an increase of the fraction of snowfall mass detected by DPR up to 59%.

  14. Composite Event Specification and Detection for Supporting Active Capability in an OODBMS: Semantics Architecture and Implementation.

    Science.gov (United States)

    1995-03-01

    terms of the ’AND’ operator and since this definition itself is questionable these operator semantics are also unclear. " The automaton for the ’AND...Proceedings 17th International Cono frencc on Very Large Data Bases, Barcelona ( Catalonia , Spain), Sept. 1.991. 65 [FM87] C. L. Forgy and J... Catalonia , Spain), Sep. 1991. [GJS92a] N. H. Gehani, H. V. Jagadish, and 0. Shmueli. COMPOSE A System For Composite Event Specification and Detection

  15. The Capabilities and Limitations of Clinical Magnetic Resonance Imaging for Detecting Kidney Stones: A Retrospective Study.

    Science.gov (United States)

    Ibrahim, El-Sayed H; Cernigliaro, Joseph G; Bridges, Mellena D; Pooley, Robert A; Haley, William E

    2016-01-01

    The purpose of this work was to investigate the performance of currently available magnetic resonance imaging (MRI) for detecting kidney stones, compared to computed tomography (CT) results, and to determine the characteristics of successfully detected stones. Patients who had undergone both abdominal/pelvic CT and MRI exams within 30 days were studied. The images were reviewed by two expert radiologists blinded to the patients' respective radiological diagnoses. The study consisted of four steps: (1) reviewing the MRI images and determining whether any kidney stone(s) are identified; (2) reviewing the corresponding CT images and confirming whether kidney stones are identified; (3) reviewing the MRI images a second time, armed with the information from the corresponding CT, noting whether any kidney stones are positively identified that were previously missed; (4) for all stones MRI-confirmed on previous steps, the radiologist experts being asked to answer whether in retrospect, with knowledge of size and location on corresponding CT, these stones would be affirmed as confidently identified on MRI or not. In this best-case scenario involving knowledge of stones and their locations on concurrent CT, radiologist experts detected 19% of kidney stones on MRI, with stone size being a major factor for stone identification.

  16. Query-Based Outlier Detection in Heterogeneous Information Networks

    Science.gov (United States)

    Kuck, Jonathan; Zhuang, Honglei; Yan, Xifeng; Cam, Hasan; Han, Jiawei

    2015-01-01

    Outlier or anomaly detection in large data sets is a fundamental task in data science, with broad applications. However, in real data sets with high-dimensional space, most outliers are hidden in certain dimensional combinations and are relative to a user’s search space and interest. It is often more effective to give power to users and allow them to specify outlier queries flexibly, and the system will then process such mining queries efficiently. In this study, we introduce the concept of query-based outlier in heterogeneous information networks, design a query language to facilitate users to specify such queries flexibly, define a good outlier measure in heterogeneous networks, and study how to process outlier queries efficiently in large data sets. Our experiments on real data sets show that following such a methodology, interesting outliers can be defined and uncovered flexibly and effectively in large heterogeneous networks. PMID:27064397

  17. Fault detection on a sewer network by a combination of a Kalman filter and a binary sequential probability ratio test

    Science.gov (United States)

    Piatyszek, E.; Voignier, P.; Graillot, D.

    2000-05-01

    One of the aims of sewer networks is the protection of population against floods and the reduction of pollution rejected to the receiving water during rainy events. To meet these goals, managers have to equip the sewer networks with and to set up real-time control systems. Unfortunately, a component fault (leading to intolerable behaviour of the system) or sensor fault (deteriorating the process view and disturbing the local automatism) makes the sewer network supervision delicate. In order to ensure an adequate flow management during rainy events it is essential to set up procedures capable of detecting and diagnosing these anomalies. This article introduces a real-time fault detection method, applicable to sewer networks, for the follow-up of rainy events. This method consists in comparing the sensor response with a forecast of this response. This forecast is provided by a model and more precisely by a state estimator: a Kalman filter. This Kalman filter provides not only a flow estimate but also an entity called 'innovation'. In order to detect abnormal operations within the network, this innovation is analysed with the binary sequential probability ratio test of Wald. Moreover, by crossing available information on several nodes of the network, a diagnosis of the detected anomalies is carried out. This method provided encouraging results during the analysis of several rains, on the sewer network of Seine-Saint-Denis County, France.

  18. Bayesian neural networks for detecting epistasis in genetic association studies.

    Science.gov (United States)

    Beam, Andrew L; Motsinger-Reif, Alison; Doyle, Jon

    2014-11-21

    Discovering causal genetic variants from large genetic association studies poses many difficult challenges. Assessing which genetic markers are involved in determining trait status is a computationally demanding task, especially in the presence of gene-gene interactions. A non-parametric Bayesian approach in the form of a Bayesian neural network is proposed for use in analyzing genetic association studies. Demonstrations on synthetic and real data reveal they are able to efficiently and accurately determine which variants are involved in determining case-control status. By using graphics processing units (GPUs) the time needed to build these models is decreased by several orders of magnitude. In comparison with commonly used approaches for detecting interactions, Bayesian neural networks perform very well across a broad spectrum of possible genetic relationships. The proposed framework is shown to be a powerful method for detecting causal SNPs while being computationally efficient enough to handle large datasets.

  19. Fabric Defect Detection Using Local Homogeneity Analysis and Neural Network

    Directory of Open Access Journals (Sweden)

    Ali Rebhi

    2015-01-01

    Full Text Available In the textile manufacturing industry, fabric defect detection becomes a necessary and essential step in quality control. The investment in this field is more than economical when reduction in labor cost and associated benefits are considered. Moreover, the development of a wholly automated inspection system requires efficient and robust algorithms. To overcome this problem, in this paper, we present a new fabric defect detection scheme which uses the local homogeneity and neural network. Its first step consists in computing a new homogeneity image denoted as H-image. The second step is devoted to the application of the discrete cosine transform (DCT to the H-image and the extraction of different representative energy features of each DCT block. These energy features are used by the back-propagation neural network to judge the existence of fabric defect. Simulations on different fabric images and different defect aspects show that the proposed method achieves an average accuracy of 97.35%.

  20. Glaucoma detection based on deep convolutional neural network.

    Science.gov (United States)

    Xiangyu Chen; Yanwu Xu; Damon Wing Kee Wong; Tien Yin Wong; Jiang Liu

    2015-08-01

    Glaucoma is a chronic and irreversible eye disease, which leads to deterioration in vision and quality of life. In this paper, we develop a deep learning (DL) architecture with convolutional neural network for automated glaucoma diagnosis. Deep learning systems, such as convolutional neural networks (CNNs), can infer a hierarchical representation of images to discriminate between glaucoma and non-glaucoma patterns for diagnostic decisions. The proposed DL architecture contains six learned layers: four convolutional layers and two fully-connected layers. Dropout and data augmentation strategies are adopted to further boost the performance of glaucoma diagnosis. Extensive experiments are performed on the ORIGA and SCES datasets. The results show area under curve (AUC) of the receiver operating characteristic curve in glaucoma detection at 0.831 and 0.887 in the two databases, much better than state-of-the-art algorithms. The method could be used for glaucoma detection.

  1. Decentralized Detection in Wireless Sensor Networks with Channel Fading Statistics

    Directory of Open Access Journals (Sweden)

    Bin Liu

    2006-12-01

    Full Text Available Existing channel aware signal processing design for decentralized detection in wireless sensor networks typically assumes the clairvoyant case, that is, global channel state information (CSI is known at the design stage. In this paper, we consider the distributed detection problem where only the channel fading statistics, instead of the instantaneous CSI, are available to the designer. We investigate the design of local decision rules for the following two cases: (1 fusion center has access to the instantaneous CSI; (2 fusion center does not have access to the instantaneous CSI. As expected, in both cases, the optimal local decision rules that minimize the error probability at the fusion center amount to a likelihood ratio test (LRT. Numerical analysis reveals that the detection performance appears to be more sensitive to the knowledge of CSI at the fusion center. The proposed design framework that utilizes only partial channel knowledge will enable distributed design of a decentralized detection wireless sensor system.

  2. Decentralized Detection in Wireless Sensor Networks with Channel Fading Statistics

    Directory of Open Access Journals (Sweden)

    Liu Bin

    2007-01-01

    Full Text Available Existing channel aware signal processing design for decentralized detection in wireless sensor networks typically assumes the clairvoyant case, that is, global channel state information (CSI is known at the design stage. In this paper, we consider the distributed detection problem where only the channel fading statistics, instead of the instantaneous CSI, are available to the designer. We investigate the design of local decision rules for the following two cases: (1 fusion center has access to the instantaneous CSI; (2 fusion center does not have access to the instantaneous CSI. As expected, in both cases, the optimal local decision rules that minimize the error probability at the fusion center amount to a likelihood ratio test (LRT. Numerical analysis reveals that the detection performance appears to be more sensitive to the knowledge of CSI at the fusion center. The proposed design framework that utilizes only partial channel knowledge will enable distributed design of a decentralized detection wireless sensor system.

  3. 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.

  4. A Universal High-Performance Correlation Analysis Detection Model and Algorithm for Network Intrusion Detection System

    Directory of Open Access Journals (Sweden)

    Hongliang Zhu

    2017-01-01

    Full Text Available In big data era, the single detection techniques have already not met the demand of complex network attacks and advanced persistent threats, but there is no uniform standard to make different correlation analysis detection be performed efficiently and accurately. In this paper, we put forward a universal correlation analysis detection model and algorithm by introducing state transition diagram. Based on analyzing and comparing the current correlation detection modes, we formalize the correlation patterns and propose a framework according to data packet timing and behavior qualities and then design a new universal algorithm to implement the method. Finally, experiment, which sets up a lightweight intrusion detection system using KDD1999 dataset, shows that the correlation detection model and algorithm can improve the performance and guarantee high detection rates.

  5. Detection of Interphase Fault Zone in Overhead Power Distribution Networks

    Directory of Open Access Journals (Sweden)

    E. Kalentionok

    2013-01-01

    Full Text Available Parametric methods have been recommended on the basis of current and voltage value recording in normal and emergency modes at a sub-transmission substation in order to detect two- and three-phase short circuits in overhead power distribution networks. The paper proposes to detect an inspection zone in order to locate an interphase fault with the help of analytical calculation of distance up to the fault point using 3–4 expressions on the basis of data obtained as a result of multiple metering pertaining to emergency mode parameters  with their subsequent statistical processing.

  6. Differential Characteristics Based Iterative Multiuser Detection for Wireless Sensor Networks.

    Science.gov (United States)

    Chen, Xiaoguang; Jiang, Xu; Wu, Zhilu; Zhuang, Shufeng

    2017-02-16

    High throughput, low latency and reliable communication has always been a hot topic for wireless sensor networks (WSNs) in various applications. Multiuser detection is widely used to suppress the bad effect of multiple access interference in WSNs. In this paper, a novel multiuser detection method based on differential characteristics is proposed to suppress multiple access interference. The proposed iterative receive method consists of three stages. Firstly, a differential characteristics function is presented based on the optimal multiuser detection decision function; then on the basis of differential characteristics, a preliminary threshold detection is utilized to find the potential wrongly received bits; after that an error bit corrector is employed to correct the wrong bits. In order to further lower the bit error ratio (BER), the differential characteristics calculation, threshold detection and error bit correction process described above are iteratively executed. Simulation results show that after only a few iterations the proposed multiuser detection method can achieve satisfactory BER performance. Besides, BER and near far resistance performance are much better than traditional suboptimal multiuser detection methods. Furthermore, the proposed iterative multiuser detection method also has a large system capacity.

  7. 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.

  8. 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.

  9. Detection and classification of the breast abnormalities in digital mammograms via regional Convolutional Neural Network.

    Science.gov (United States)

    Al-Masni, M A; Al-Antari, M A; Park, J M; Gi, G; Kim, T Y; Rivera, P; Valarezo, E; Han, S-M; Kim, T-S

    2017-07-01

    Automatic detection and classification of the masses in mammograms are still a big challenge and play a crucial role to assist radiologists for accurate diagnosis. In this paper, we propose a novel computer-aided diagnose (CAD) system based on one of the regional deep learning techniques: a ROI-based Convolutional Neural Network (CNN) which is called You Only Look Once (YOLO). Our proposed YOLO-based CAD system contains four main stages: mammograms preprocessing, feature extraction utilizing multi convolutional deep layers, mass detection with confidence model, and finally mass classification using fully connected neural network (FC-NN). A set of training mammograms with the information of ROI masses and their types are used to train YOLO. The trained YOLO-based CAD system detects the masses and classifies their types into benign or malignant. Our results show that the proposed YOLO-based CAD system detects the mass location with an overall accuracy of 96.33%. The system also distinguishes between benign and malignant lesions with an overall accuracy of 85.52%. Our proposed system seems to be feasible as a CAD system capable of detection and classification at the same time. It also overcomes some challenging breast cancer cases such as the mass existing in the pectoral muscles or dense regions.

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

    OpenAIRE

    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 YouTube users‟ behaviour and their characteristics through expert knowledge. Based on experts‟ knowledge, the system assigns a score to the users, which represents their level of “bulliness‿ based on th...

  11. Detection of Cyberbullying Incidents on the Instagram Social Network

    OpenAIRE

    Hosseinmardi, Homa; Mattson, Sabrina Arredondo; Rafiq, Rahat Ibn; Han, Richard; Lv, Qin; Mishra, Shivakant

    2015-01-01

    Cyberbullying is a growing problem affecting more than half of all American teens. The main goal of this paper is to investigate fundamentally new approaches to understand and automatically detect incidents of cyberbullying over images in Instagram, a media-based mobile social network. To this end, we have collected a sample Instagram data set consisting of images and their associated comments, and designed a labeling study for cyberbullying as well as image content using human labelers at th...

  12. Machine learning for network-based malware detection

    DEFF Research Database (Denmark)

    Stevanovic, Matija

    and based on different, mutually complementary, principles of traffic analysis. The proposed approaches rely on machine learning algorithms (MLAs) for automated and resource-efficient identification of the patterns of malicious network traffic. We evaluated the proposed methods through extensive evaluations...... traffic that provides reliable and time-efficient labeling. Finally, the thesis outlines the opportunities for future work on realizing robust and effective detection solutions....

  13. Homodyne laser vibrometer capable of detecting nanometer displacements accurately by using optical shutters.

    Science.gov (United States)

    Zhu, JingHao; Hu, Pengcheng; Tan, JiuBin

    2015-12-01

    This paper describes a homodyne laser vibrometer with optical shutters. The parameters that define the nonlinearity of the quadrature signals in a vibrometer can be pre-extracted before the measurement, and can then be used to compensate for nonlinear errors, such as unequal AC amplitudes and DC offsets. The experimental results indicated that the homodyne laser vibrometer developed has the ability to accurately detect the vibration state of the object to be measured, even when the amplitude is ≤λ/4. The displacement residual error can be reduced to a value under 0.9 nm.

  14. 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...

  15. A source location algorithm of lightning detection networks in China

    Directory of Open Access Journals (Sweden)

    Z. X. Hu

    2010-10-01

    Full Text Available Fast and accurate retrieval of lightning sources is crucial to the early warning and quick repairs of lightning disaster. An algorithm for computing the location and onset time of cloud-to-ground lightning using the time-of-arrival (TOA and azimuth-of-arrival (AOA data is introduced in this paper. The algorithm can iteratively calculate the least-squares solution of a lightning source on an oblate spheroidal Earth. It contains a set of unique formulas to compute the geodesic distance and azimuth and an explicit method to compute the initial position using TOA data of only three sensors. Since the method accounts for the effects of the oblateness of the Earth, it would provide a more accurate solution than algorithms based on planar or spherical surface models. Numerical simulations are presented to test this algorithm and evaluate the performance of a lightning detection network in the Hubei province of China. Since 1990s, the proposed algorithm has been used in many regional lightning detection networks installed by the electric power system in China. It is expected that the proposed algorithm be used in more lightning detection networks and other location systems.

  16. MACD-Based Motion Detection Approach in Heterogeneous Networks

    Directory of Open Access Journals (Sweden)

    Chen Yung-Mu

    2008-01-01

    Full Text Available Abstract Optimizing the balance between handoff quality and power consumption is a great challenge for seamless mobile communications in wireless networks. Traditional proactive schemes continuously monitor available access networks and exercise handoff. Although such schemes achieve good handoff quality, they consume much power because all interfaces must remain on all the time. To save power, the reactive schemes use fixed RSS thresholds to determine when to search for a new available access network. However, since they do not consider user motion, these approaches require that all interfaces be turned on even when a user is stationary, and they tend initiate excessive unnecessary handoffs. To address this problem, this research presents a novel motion-aware scheme called network discovery with motion detection (NDMD to improve handoff quality and minimize power consumption. The NDMD first applies a moving average convergence divergence (MACD scheme to analyze received signal strength (RSS samples of the current active interface. These results are then used to estimate user's motion. The proposed NDMD scheme adds very little computing overhead to a mobile terminal (MT and can be easily incorporated into existing schemes. The simulation results in this study showed that NDMD can quickly track user motion state without a positioning system and perform network discovery rapidly enough to achieve a much lower handoff-dropping rate with less power consumption.

  17. MACD-Based Motion Detection Approach in Heterogeneous Networks

    Directory of Open Access Journals (Sweden)

    Chih-Hung Hsu

    2008-09-01

    Full Text Available Optimizing the balance between handoff quality and power consumption is a great challenge for seamless mobile communications in wireless networks. Traditional proactive schemes continuously monitor available access networks and exercise handoff. Although such schemes achieve good handoff quality, they consume much power because all interfaces must remain on all the time. To save power, the reactive schemes use fixed RSS thresholds to determine when to search for a new available access network. However, since they do not consider user motion, these approaches require that all interfaces be turned on even when a user is stationary, and they tend initiate excessive unnecessary handoffs. To address this problem, this research presents a novel motion-aware scheme called network discovery with motion detection (NDMD to improve handoff quality and minimize power consumption. The NDMD first applies a moving average convergence divergence (MACD scheme to analyze received signal strength (RSS samples of the current active interface. These results are then used to estimate user's motion. The proposed NDMD scheme adds very little computing overhead to a mobile terminal (MT and can be easily incorporated into existing schemes. The simulation results in this study showed that NDMD can quickly track user motion state without a positioning system and perform network discovery rapidly enough to achieve a much lower handoff-dropping rate with less power consumption.

  18. Detecting and analyzing research communities in longitudinal scientific networks.

    Science.gov (United States)

    Leone Sciabolazza, Valerio; Vacca, Raffaele; Kennelly Okraku, Therese; McCarty, Christopher

    2017-01-01

    A growing body of evidence shows that collaborative teams and communities tend to produce the highest-impact scientific work. This paper proposes a new method to (1) Identify collaborative communities in longitudinal scientific networks, and (2) Evaluate the impact of specific research institutes, services or policies on the interdisciplinary collaboration between these communities. First, we apply community-detection algorithms to cross-sectional scientific collaboration networks and analyze different types of co-membership in the resulting subgroups over time. This analysis summarizes large amounts of longitudinal network data to extract sets of research communities whose members have consistently collaborated or shared collaborators over time. Second, we construct networks of cross-community interactions and estimate Exponential Random Graph Models to predict the formation of interdisciplinary collaborations between different communities. The method is applied to longitudinal data on publication and grant collaborations at the University of Florida. Results show that similar institutional affiliation, spatial proximity, transitivity effects, and use of the same research services predict higher degree of interdisciplinary collaboration between research communities. Our application also illustrates how the identification of research communities in longitudinal data and the analysis of cross-community network formation can be used to measure the growth of interdisciplinary team science at a research university, and to evaluate its association with research policies, services or institutes.

  19. Detecting and analyzing research communities in longitudinal scientific networks.

    Directory of Open Access Journals (Sweden)

    Valerio Leone Sciabolazza

    Full Text Available A growing body of evidence shows that collaborative teams and communities tend to produce the highest-impact scientific work. This paper proposes a new method to (1 Identify collaborative communities in longitudinal scientific networks, and (2 Evaluate the impact of specific research institutes, services or policies on the interdisciplinary collaboration between these communities. First, we apply community-detection algorithms to cross-sectional scientific collaboration networks and analyze different types of co-membership in the resulting subgroups over time. This analysis summarizes large amounts of longitudinal network data to extract sets of research communities whose members have consistently collaborated or shared collaborators over time. Second, we construct networks of cross-community interactions and estimate Exponential Random Graph Models to predict the formation of interdisciplinary collaborations between different communities. The method is applied to longitudinal data on publication and grant collaborations at the University of Florida. Results show that similar institutional affiliation, spatial proximity, transitivity effects, and use of the same research services predict higher degree of interdisciplinary collaboration between research communities. Our application also illustrates how the identification of research communities in longitudinal data and the analysis of cross-community network formation can be used to measure the growth of interdisciplinary team science at a research university, and to evaluate its association with research policies, services or institutes.

  20. Anomaly detection using clustering for ad hoc networks -behavioral approach-

    Directory of Open Access Journals (Sweden)

    Belacel Madani

    2012-06-01

    Full Text Available Mobile   ad   hoc   networks   (MANETs   are   multi-hop   wireless   networks   ofautonomous  mobile  nodes  without  any  fixed  infrastructure.  In  MANETs,  it  isdifficult to detect malicious nodes because the network topology constantly changesdue  to  node  mobility.  Intrusion  detection  is  the  means  to  identify  the  intrusivebehaviors and provide useful information to intruded systems to respond fast and toavoid  or  reduce  damages.  The  anomaly  detection  algorithms  have  the  advantagebecause  they  can  detect  new  types  of  attacks  (zero-day  attacks.In  this  paper,  wepresent  a  Intrusion  Detection  System  clustering-based  (ID-Cluster  that  fits  therequirement of MANET. This dissertation addresses both routing layer misbehaviorsissues,  with  main  focuses  on  thwarting  routing  disruption  attack  Dynamic  SourceRouting  (DSR.  To  validate  the  research,  a  case  study  is  presented  using  thesimulation with GloMoSum at different mobility levels. Simulation results show thatour  proposed  system  can  achieve  desirable  performance  and  meet  the  securityrequirement of MANET.

  1. 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.

  2. Directed Design of Experiments for Validating Probability of Detection Capability of a Testing System

    Science.gov (United States)

    Generazio, Edward R. (Inventor)

    2012-01-01

    A method of validating a probability of detection (POD) testing system using directed design of experiments (DOE) includes recording an input data set of observed hit and miss or analog data for sample components as a function of size of a flaw in the components. The method also includes processing the input data set to generate an output data set having an optimal class width, assigning a case number to the output data set, and generating validation instructions based on the assigned case number. An apparatus includes a host machine for receiving the input data set from the testing system and an algorithm for executing DOE to validate the test system. The algorithm applies DOE to the input data set to determine a data set having an optimal class width, assigns a case number to that data set, and generates validation instructions based on the case number.

  3. Fall Detection on Ambient Assisted Living using a Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Miguel FELGUEIRAS

    2013-07-01

    Full Text Available In this work, a distributed system for fall detection is presented. The proposed system was designed to monitor activities of the daily living of elderly people and to inform the caregivers when a falls event occurs. This system uses a scalable wireless sensor networks to collect the data and transmit it to a control center. Also, an intelligent algorithm is used to process the data collected by the sensor networks and calculate if an event is, or not, a fall. A statistical method is used to improve this algorithm and to reduce false positives. The system presented has the capability to learn with past events and to adapt is behavior with new information collected from the monitored elders. The results obtained show that the system has an accuracy above 98%. 

  4. Fall Detection on Ambient Assisted Living using a Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    António PEREIRA

    2012-07-01

    Full Text Available In this work, a distributed system for fall detection is presented. The proposed system was designed to monitor activities of the daily living of elderly people and to inform the caregivers when a falls event occurs. This system uses a scalable wireless sensor networks to collect the data and transmit it to a control center. Also, an intelligent algorithm is used to process the data collected by the sensor networks and calculate if an event is, or not, a fall. A statistical method is used to improve this algorithm and to reduce false positives. The system presented has the capability to learn with past events and to adapt is behavior with new information collected from the monitored elders. The results obtained show that the system has an accuracy above 98%.  

  5. Determination of a Limited Scope Network's Lightning Detection Efficiency

    Science.gov (United States)

    Rompala, John T.; Blakeslee, R.

    2008-01-01

    This paper outlines a modeling technique to map lightning detection efficiency variations over a region surveyed by a sparse array of ground based detectors. A reliable flash peak current distribution (PCD) for the region serves as the technique's base. This distribution is recast as an event probability distribution function. The technique then uses the PCD together with information regarding: site signal detection thresholds, type of solution algorithm used, and range attenuation; to formulate the probability that a flash at a specified location will yield a solution. Applying this technique to the full region produces detection efficiency contour maps specific to the parameters employed. These contours facilitate a comparative analysis of each parameter's effect on the network's detection efficiency. In an alternate application, this modeling technique gives an estimate of the number, strength, and distribution of events going undetected. This approach leads to a variety of event density contour maps. This application is also illustrated. The technique's base PCD can be empirical or analytical. A process for formulating an empirical PCD specific to the region and network being studied is presented. A new method for producing an analytical representation of the empirical PCD is also introduced.

  6. Radiation detection and situation management by distributed sensor networks

    Energy Technology Data Exchange (ETDEWEB)

    Jan, Frigo [Los Alamos National Laboratory; Mielke, Angela [Los Alamos National Laboratory; Cai, D Michael [Los Alamos National Laboratory

    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.

  7. 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.

  8. 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.

  9. Automatic Detection of Welding Defects using Deep Neural Network

    Science.gov (United States)

    Hou, Wenhui; Wei, Ye; Guo, Jie; Jin, Yi; Zhu, Chang’an

    2018-01-01

    In this paper, we propose an automatic detection schema including three stages for weld defects in x-ray images. Firstly, the preprocessing procedure for the image is implemented to locate the weld region; Then a classification model which is trained and tested by the patches cropped from x-ray images is constructed based on deep neural network. And this model can learn the intrinsic feature of images without extra calculation; Finally, the sliding-window approach is utilized to detect the whole images based on the trained model. In order to evaluate the performance of the model, we carry out several experiments. The results demonstrate that the classification model we proposed is effective in the detection of welded joints quality.

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

    Science.gov (United States)

    Cho, Eung Jun; Hong, Choong Seon; Lee, Sungwon; Jeon, Seokhee

    2013-01-01

    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.

  11. 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.

  12. 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.

  13. Social Network Sensors for Early Detection of Contagious Outbreaks

    CERN Document Server

    Christakis, Nicholas A

    2010-01-01

    Current methods for the detection of contagious outbreaks give contemporaneous information about the course of an epidemic at best. Individuals at the center of a social network are likely to be infected sooner, on average, than those at the periphery. However, mapping a whole network to identify central individuals whom to monitor is typically very difficult. We propose an alternative strategy that does not require ascertainment of global network structure, namely, 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 divided between a random group and a friend group. Based on clinical diagnoses, the progression of the epidemic in the friend group occurred 14.7 days (95% C.I. 11.7-17.6) in advance of the randomly chosen group (i.e., the population as a whole). The friend group also showed a significa...

  14. Automatic detection and classification of leukocytes using convolutional neural networks.

    Science.gov (United States)

    Zhao, Jianwei; Zhang, Minshu; Zhou, Zhenghua; Chu, Jianjun; Cao, Feilong

    2017-08-01

    The detection and classification of white blood cells (WBCs, also known as Leukocytes) is a hot issue because of its important applications in disease diagnosis. Nowadays the morphological analysis of blood cells is operated manually by skilled operators, which results in some drawbacks such as slowness of the analysis, a non-standard accuracy, and the dependence on the operator's skills. Although there have been many papers studying the detection of WBCs or classification of WBCs independently, few papers consider them together. This paper proposes an automatic detection and classification system for WBCs from peripheral blood images. It firstly proposes an algorithm to detect WBCs from the microscope images based on the simple relation of colors R, B and morphological operation. Then a granularity feature (pairwise rotation invariant co-occurrence local binary pattern, PRICoLBP feature) and SVM are applied to classify eosinophil and basophil from other WBCs firstly. Lastly, convolution neural networks are used to extract features in high level from WBCs automatically, and a random forest is applied to these features to recognize the other three kinds of WBCs: neutrophil, monocyte and lymphocyte. Some detection experiments on Cellavison database and ALL-IDB database show that our proposed detection method has better effect almost than iterative threshold method with less cost time, and some classification experiments show that our proposed classification method has better accuracy almost than some other methods.

  15. Detection of exudates in fundus photographs using deep neural networks and anatomical landmark detection fusion.

    Science.gov (United States)

    Prentašić, Pavle; Lončarić, Sven

    2016-12-01

    Diabetic retinopathy is one of the leading disabling chronic diseases and one of the leading causes of preventable blindness in developed world. Early diagnosis of diabetic retinopathy enables timely treatment and in order to achieve it a major effort will have to be invested into automated population screening programs. Detection of exudates in color fundus photographs is very important for early diagnosis of diabetic retinopathy. We use deep convolutional neural networks for exudate detection. In order to incorporate high level anatomical knowledge about potential exudate locations, output of the convolutional neural network is combined with the output of the optic disc detection and vessel detection procedures. In the validation step using a manually segmented image database we obtain a maximum F1 measure of 0.78. As manually segmenting and counting exudate areas is a tedious task, having a reliable automated output, such as automated segmentation using convolutional neural networks in combination with other landmark detectors, is an important step in creating automated screening programs for early detection of diabetic retinopathy. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  16. Automated Detection of Classical Novae with Neural Networks

    CERN Document Server

    Feeney, S; Evans, N W; An, J; Hewett, P C; Bode, M; Darnley, M; Kerins, E; Baillon, Paul; Carr, B J; Paulin-Henriksson, S; Gould, A

    2005-01-01

    The POINT-AGAPE collaboration surveyed M31 with the primary goal of optical detection of microlensing events, yet its data catalogue is also a prime source of lightcurves of variable and transient objects, including classical novae (CNe). A reliable means of identification, combined with a thorough survey of the variable objects in M31, provides an excellent opportunity to locate and study an entire galactic population of CNe. This paper presents a set of 440 neural networks, working in 44 committees, designed specifically to identify fast CNe. The networks are developed using training sets consisting of simulated novae and POINT-AGAPE lightcurves, in a novel variation on K-fold cross-validation. They use the binned, normalised power spectra of the lightcurves as input units. The networks successfully identify 9 of the 13 previously identified M31 CNe within their optimal working range (and 11 out of 13 if the network error bars are taken into account). They provide a catalogue of 19 new candidate fast CNe, o...

  17. Analytical Validation and Capabilities of the Epic CTC Platform: Enrichment-Free Circulating Tumour Cell Detection and Characterization

    Directory of Open Access Journals (Sweden)

    Shannon L. Werner

    2015-05-01

    Full Text Available The Epic Platform was developed for the unbiased detection and molecular characterization of circulating tumour cells (CTCs. Here, we report assay performance data, including accuracy, linearity, specificity and intra/inter-assay precision of CTC enumeration in healthy donor (HD blood samples spiked with varying concentrations of cancer cell line controls (CLCs. Additionally, we demonstrate clinical feasibility for CTC detection in a small cohort of metastatic castrate-resistant prostate cancer (mCRPC patients. The Epic Platform demonstrated accuracy, linearity and sensitivity for the enumeration of all CLC concentrations tested. Furthermore, we established the precision between multiple operators and slide staining batches and assay specificity showing zero CTCs detected in 18 healthy donor samples. In a clinical feasibility study, at least one traditional CTC/mL (CK+, CD45-, and intact nuclei was detected in 89 % of 44 mCRPC samples, whereas 100 % of samples had CTCs enumerated if additional CTC subpopulations (CK-/CD45- and CK+ apoptotic CTCs were included in the analysis. In addition to presenting Epic Platform's performance with respect to CTC enumeration, we provide examples of its integrated downstream capabilities, including protein biomarker expression and downstream genomic analyses at single cell resolution.

  18. Design of Detection Engine for Wormhole Attack in Adhoc Network Environment

    OpenAIRE

    Husain Shahnawaz; Joshi R.C; Gupta S.C.

    2012-01-01

    Adhoc network is a collection of nodes that are capable to form dynamically atemporary network without the support of any centralized fixed infrastructure. There is no central controller to determine the reliable & secure communication paths in Mobile Adhoc network. Each node in the Adhoc 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 Adhoc network (MANE...

  19. Feature detection in satellite images using neural network technology

    Science.gov (United States)

    Augusteijn, Marijke F.; Dimalanta, Arturo S.

    1992-01-01

    A feasibility study of automated classification of satellite images is described. Satellite images were characterized by the textures they contain. In particular, the detection of cloud textures was investigated. The method of second-order gray level statistics, using co-occurrence matrices, was applied to extract feature vectors from image segments. Neural network technology was employed to classify these feature vectors. The cascade-correlation architecture was successfully used as a classifier. The use of a Kohonen network was also investigated but this architecture could not reliably classify the feature vectors due to the complicated structure of the classification problem. The best results were obtained when data from different spectral bands were fused.

  20. Network community-detection enhancement by proper weighting.

    Science.gov (United States)

    Khadivi, Alireza; Ajdari Rad, Ali; Hasler, Martin

    2011-04-01

    In this paper, we show how proper assignment of weights to the edges of a complex network can enhance the detection of communities and how it can circumvent the resolution limit and the extreme degeneracy problems associated with modularity. Our general weighting scheme takes advantage of graph theoretic measures and it introduces two heuristics for tuning its parameters. We use this weighting as a preprocessing step for the greedy modularity optimization algorithm of Newman to improve its performance. The result of the experiments of our approach on computer-generated and real-world data networks confirm that the proposed approach not only mitigates the problems of modularity but also improves the modularity optimization.

  1. Dimensionality reduction using Principal Component Analysis for network intrusion detection

    Directory of Open Access Journals (Sweden)

    K. Keerthi Vasan

    2016-09-01

    Full Text Available Intrusion detection is the identification of malicious activities in a given network by analyzing its traffic. Data mining techniques used for this analysis study the traffic traces and identify hostile flows in the traffic. Dimensionality reduction in data mining focuses on representing data with minimum number of dimensions such that its properties are not lost and hence reducing the underlying complexity in processing the data. Principal Component Analysis (PCA is one of the prominent dimensionality reduction techniques widely used in network traffic analysis. In this paper, we focus on the efficiency of PCA for intrusion detection and determine its Reduction Ratio (RR, ideal number of Principal Components needed for intrusion detection and the impact of noisy data on PCA. We carried out experiments with PCA using various classifier algorithms on two benchmark datasets namely, KDD CUP and UNB ISCX. Experiments show that the first 10 Principal Components are effective for classification. The classification accuracy for 10 Principal Components is about 99.7% and 98.8%, nearly same as the accuracy obtained using original 41 features for KDD and 28 features for ISCX, respectively.

  2. Combining Unsupervised Anomaly Detection and Neural Networks for Driver Identification

    Directory of Open Access Journals (Sweden)

    Thitaree Tanprasert

    2017-01-01

    Full Text Available This paper proposes an algorithm for real-time driver identification using the combination of unsupervised anomaly detection and neural networks. The proposed algorithm uses nonphysiological signals as input, namely, driving behavior signals from inertial sensors (e.g., accelerometers and geolocation signals from GPS sensors. First anomaly detection is performed to assess if the current driver is whom he/she claims to be. If an anomaly is detected, the algorithm proceeds to find relevant features in the input signals and use neural networks to identify drivers. To assess the proposed algorithm, real-world data are collected from ten drivers who drive different vehicles on several routes in real-world traffic conditions. Driver identification is performed on each of the seven-second-long driving behavior signals and geolocation signals in a streaming manner. It is shown that the proposed algorithm can achieve relatively high accuracy and identify drivers within 13 seconds. The proposed algorithm also outperforms the previously proposed driver identification algorithms. Furthermore, to demonstrate how the proposed algorithm can be deployed in real-world applications, results from real-world data associated with each operation of the proposed algorithm are shown step-by-step.

  3. Acquiring data in real time in Italy from the Antarctic Seismographic Argentinean Italian Network (ASAIN): testing the global capabilities of the EarthWorm and Antelope software suites.

    Science.gov (United States)

    Percy Plasencia Linares, Milton; Russi, Marino; Pesaresi, Damiano; Cravos, Claudio

    2010-05-01

    The Italian National Institute for Oceanography and Experimental Geophysics (Istituto Nazionale di Oceanografia e di Geofisica Sperimentale, OGS) is running the Antarctic Seismographic Argentinean Italian Network (ASAIN), made of 7 seismic stations located in the Scotia Sea region in Antarctica and in Tierra del Fuego - Argentina: data from these stations are transferred in real time to the OGS headquarters in Trieste (Italy) via satellite links provided by the Instituto Antártico Argentino (IAA). Data is collected and archived primarily in Güralp Compress Format (GCF) through the Scream! software at OGS and IAA, and transmitted also in real time to the Observatories and Research Facilities for European Seismology (ORFEUS). The main real time seismic data acquisition and processing system of the ASAIN network is based on the EarthWorm 7.3 (Open Source) software suite installed on a Linux server at the OGS headquarters in Trieste. It runs several software modules for data collection, data archiving, data publication on dedicated web servers: wave_serverV, Winston Wave Server, and data analysis and realtime monitoring through Swarm program. OGS is also running, in close cooperation with the Friuli-Venezia Giulia Civil Defense, the North East (NI) Italy seismic network, making use of the Antelope commercial software suite from BRTT as the main acquisition system. As a test to check the global capabilities of the Antelope software suite, we also set up an instance of Antelope acquiring data in real time from both the regional ASAIN seismic network in Antarctica and a subset of the Global Seismic Network (GSN) funded by the Incorporated Research Institution for Seismology (IRIS). The facilities of the IRIS Data Management System, and specifically the IRIS Data Management Center, were used for real time access to waveform required in this study. The first tests indicated that more than 80% of the earthquakes with magnitude M>5.0 listed in the Preliminary Determination

  4. Neural networks-based damage detection for bridges considering errors in baseline finite element models

    Science.gov (United States)

    Lee, Jong Jae; Lee, Jong Won; Yi, Jin Hak; Yun, Chung Bang; Jung, Hie Young

    2005-02-01

    Structural health monitoring has become an important research topic in conjunction with damage assessment and safety evaluation of structures. The use of system identification approaches for damage detection has been expanded in recent years owing to the advancements in signal analysis and information processing techniques. Soft computing techniques such as neural networks and genetic algorithm have been utilized increasingly for this end due to their excellent pattern recognition capability. In this study, a neural networks-based damage detection method using the modal properties is presented, which can effectively consider the modelling errors in the baseline finite element model from which the training patterns are to be generated. The differences or the ratios of the mode shape components between before and after damage are used as the input to the neural networks in this method, since they are found to be less sensitive to the modelling errors than the mode shapes themselves. Two numerical example analyses on a simple beam and a multi-girder bridge are presented to demonstrate the effectiveness of the proposed method. Results of laboratory test on a simply supported bridge model and field test on a bridge with multiple girders confirm the applicability of the present method.

  5. Clustering and community detection in directed networks: A survey

    Science.gov (United States)

    Malliaros, Fragkiskos D.; Vazirgiannis, Michalis

    2013-12-01

    Networks (or graphs) appear as dominant structures in diverse domains, including sociology, biology, neuroscience and computer science. In most of the aforementioned cases graphs are directed - in the sense that there is directionality on the edges, making the semantics of the edges nonsymmetric as the source node transmits some property to the target one but not vice versa. An interesting feature that real networks present is the clustering or community structure property, under which the graph topology is organized into modules commonly called communities or clusters. The essence here is that nodes of the same community are highly similar while on the contrary, nodes across communities present low similarity. Revealing the underlying community structure of directed complex networks has become a crucial and interdisciplinary topic with a plethora of relevant application domains. Therefore, naturally there is a recent wealth of research production in the area of mining directed graphs - with clustering being the primary method sought and the primary tool for community detection and evaluation. The goal of this paper is to offer an in-depth comparative review of the methods presented so far for clustering directed networks along with the relevant necessary methodological background and also related applications. The survey commences by offering a concise review of the fundamental concepts and methodological base on which graph clustering algorithms capitalize on. Then we present the relevant work along two orthogonal classifications. The first one is mostly concerned with the methodological principles of the clustering algorithms, while the second one approaches the methods from the viewpoint regarding the properties of a good cluster in a directed network. Further, we present methods and metrics for evaluating graph clustering results, demonstrate interesting application domains and provide promising future research directions.

  6. 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.

  7. EFFICIENT LANE DETECTION BASED ON ARTIFICIAL NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    F. Arce

    2017-09-01

    Full Text Available Lane detection is a problem that has attracted in the last years the attention of the computer vision community. Most of approaches used until now to face this problem combine conventional image processing, image analysis and pattern classification techniques. In this paper, we propose a methodology based on so-called Ellipsoidal Neural Networks with Dendritic Processing (ENNDPs as a new approach to provide a solution to this important problem. The functioning and performance of the proposed methodology is validated with a real video taken by a camera mounted on a car circulating on urban highway of Mexico City.

  8. Video Salient Object Detection via Fully Convolutional Networks.

    Science.gov (United States)

    Wang, Wenguan; Shen, Jianbing; Shao, Ling

    This paper proposes a deep learning model to efficiently detect salient regions in videos. It addresses two important issues: 1) deep video saliency model training with the absence of sufficiently large and pixel-wise annotated video data and 2) fast video saliency training and detection. The proposed deep video saliency network consists of two modules, for capturing the spatial and temporal saliency information, respectively. The dynamic saliency model, explicitly incorporating saliency estimates from the static saliency model, directly produces spatiotemporal saliency inference without time-consuming optical flow computation. We further propose a novel data augmentation technique that simulates video training data from existing annotated image data sets, which enables our network to learn diverse saliency information and prevents overfitting with the limited number of training videos. Leveraging our synthetic video data (150K video sequences) and real videos, our deep video saliency model successfully learns both spatial and temporal saliency cues, thus producing accurate spatiotemporal saliency estimate. We advance the state-of-the-art on the densely annotated video segmentation data set (MAE of .06) and the Freiburg-Berkeley Motion Segmentation data set (MAE of .07), and do so with much improved speed (2 fps with all steps).This paper proposes a deep learning model to efficiently detect salient regions in videos. It addresses two important issues: 1) deep video saliency model training with the absence of sufficiently large and pixel-wise annotated video data and 2) fast video saliency training and detection. The proposed deep video saliency network consists of two modules, for capturing the spatial and temporal saliency information, respectively. The dynamic saliency model, explicitly incorporating saliency estimates from the static saliency model, directly produces spatiotemporal saliency inference without time-consuming optical flow computation. We further

  9. Efficient Lane Detection Based on Artificial Neural Networks

    Science.gov (United States)

    Arce, F.; Zamora, E.; Hernández, G.; Sossa, H.

    2017-09-01

    Lane detection is a problem that has attracted in the last years the attention of the computer vision community. Most of approaches used until now to face this problem combine conventional image processing, image analysis and pattern classification techniques. In this paper, we propose a methodology based on so-called Ellipsoidal Neural Networks with Dendritic Processing (ENNDPs) as a new approach to provide a solution to this important problem. The functioning and performance of the proposed methodology is validated with a real video taken by a camera mounted on a car circulating on urban highway of Mexico City.

  10. Detection of phase transition via convolutional neural network

    CERN Document Server

    Tanaka, Akinori

    2016-01-01

    We design a Convolutional Neural Network (CNN) which studies correlation between discretized inverse temperature and spin configuration of 2D Ising model and show that it can find a feature of the phase transition without teaching any a priori information for it. We also define a new order parameter via the CNN and show that it provides well approximated critical inverse temperature. In addition, we compare the activation functions for convolution layer and find that the Rectified Linear Unit (ReLU) is important to detect the phase transition of 2D Ising model.

  11. 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.

  12. 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.

  13. Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks

    Science.gov (United States)

    Zhang, Kaipeng; Zhang, Zhanpeng; Li, Zhifeng; Qiao, Yu

    2016-10-01

    Face detection and alignment in unconstrained environment are challenging due to various poses, illuminations and occlusions. Recent studies show that deep learning approaches can achieve impressive performance on these two tasks. In this paper, we propose a deep cascaded multi-task framework which exploits the inherent correlation between them to boost up their performance. In particular, our framework adopts a cascaded structure with three stages of carefully designed deep convolutional networks that predict face and landmark location in a coarse-to-fine manner. In addition, in the learning process, we propose a new online hard sample mining strategy that can improve the performance automatically without manual sample selection. Our method achieves superior accuracy over the state-of-the-art techniques on the challenging FDDB and WIDER FACE benchmark for face detection, and AFLW benchmark for face alignment, while keeps real time performance.

  14. A polymer lab-on-a-chip for magnetic immunoassay with on-chip sampling and detection capabilities.

    Science.gov (United States)

    Do, Jaephil; Ahn, Chong H

    2008-04-01

    This paper presents a new polymer lab-on-a-chip for magnetic bead-based immunoassay with fully on-chip sampling and detection capabilities, which provides a smart platform of magnetic immunoassay-based lab-on-a-chip for point-of-care testing (POCT) toward biochemical hazardous agent detection, food inspection or clinical diagnostics. In this new approach, the polymer lab-on-a-chip for magnetic bead-based immunoassay consists of a magnetic bead-based separator, an interdigitated array (IDA) micro electrode, and a microfluidic system, which are fully incorporated into a lab-on-a-chip on cyclic olefin copolymer (COC). Since the polymer lab-on-a-chip was realized using low cost, high throughput polymer microfabrication techniques such as micro injection molding and hot embossing method, a disposable polymer lab-on-a-chip for the magnetic bead-based immunoassay can be successfully realized in a disposable platform. With this newly developed polymer lab-on-a-chip, an enzyme-labelled electrochemical immunoassay (ECIA) was performed using magnetic beads as the mobile solid support, and the final enzyme product produced from the ECIA was measured using chronoamperometry. A sampling and detection of as low as 16.4 ng mL(-1) of mouse IgG has been successfully performed in 35 min for the entire procedure.

  15. An investigation of the storage capability of district heating networks. Consequences of heat production; Untersuchung der Speicherfaehigkeit von Fernwaermenetzen. Auswirkungen auf die Waermeerzeugung

    Energy Technology Data Exchange (ETDEWEB)

    Gross, Sebastian; Felsmann, Clemens [Technische Univ. Dresden (Germany). Professur fuer Gebaeudeenergietechnik und Waermeversorgung

    2012-01-15

    The storage of energy is a key issue in terms of the energy policy turnaround and the concomitant increase in decentralized power generation. District heating networks can be used as a heat storage. But is this reasonable energetically and economically? And what is the situation with the storage capacity of district heating networks? How does this storage capability impact on the use of heat sources? The authors of the contribution under consideration try to give an answer to these questions.

  16. 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.

  17. Detecting anomalous traders using multi-slice network analysis

    Science.gov (United States)

    Sun, Xiao-Qian; Shen, Hua-Wei; Cheng, Xue-Qi; Zhang, Yuqing

    2017-05-01

    Manipulation is an important issue for both developed and emerging stock markets. Many efforts have been made to detect manipulation in stock market. However, it is still an open problem to identify the fraudulent traders, especially when they collude with each other. In this paper, we focus on the problem of identifying anomalous traders using the transaction data of 8 manipulated stocks and 42 non-manipulated stocks during a one-year period. For each stock, we construct a multi-slice trading network to characterize the daily trading behavior and the cross-day participation of each trader. Comparing the multi-slice trading network of manipulated stocks and non-manipulated stocks with their randomized version, we find that manipulated stocks exhibit high number of trader pairs that trade with each other in multiple days and high deviation from randomized network at correlation between trading frequency and trading activity. These findings are effective at distinguishing manipulated stocks from non-manipulated ones and at identifying anomalous traders.

  18. 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.

  19. Final report on NA22 Project: A Comprehensive Capability to Use Prompt Fission Signatures to Detect SNM

    Energy Technology Data Exchange (ETDEWEB)

    Vogt, R. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2014-01-21

    The objective of this project is to provide a general capability that will facilitate detection of high-sensitivity nuclear material for both active interrogation and passive systems. Currently these systems are able to use only a small fraction of the information carried by particles emitted during ssion. Our improvements in physics modeling and simulation tools would enable exhaustive use of prompt ssion signatures. In particular, this project will provide a computationally e cient event-by-event description of ssion that explicitly describes all of the correlations associated with the production and subsequent decay of fragments formed in ssion. We will use existing data to validate our model as far as possible, including trying to make use of data from current NA22 funded projects. Once complete, we will make our code, FREYA, publicly available either as a standalone code or as a module in Monte Carlo transport codes.

  20. WSN-DS: A Dataset for Intrusion Detection Systems in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Iman Almomani

    2016-01-01

    Full Text Available Wireless Sensor Networks (WSN have become increasingly one of the hottest research areas in computer science due to their wide range of applications including critical military and civilian applications. Such applications have created various security threats, especially in unattended environments. To ensure the security and dependability of WSN services, an Intrusion Detection System (IDS should be in place. This IDS has to be compatible with the characteristics of WSNs and capable of detecting the largest possible number of security threats. In this paper a specialized dataset for WSN is developed to help better detect and classify four types of Denial of Service (DoS attacks: Blackhole, Grayhole, Flooding, and Scheduling attacks. This paper considers the use of LEACH protocol which is one of the most popular hierarchical routing protocols in WSNs. A scheme has been defined to collect data from Network Simulator 2 (NS-2 and then processed to produce 23 features. The collected dataset is called WSN-DS. Artificial Neural Network (ANN has been trained on the dataset to detect and classify different DoS attacks. The results show that WSN-DS improved the ability of IDS to achieve higher classification accuracy rate. WEKA toolbox was used with holdout and 10-Fold Cross Validation methods. The best results were achieved with 10-Fold Cross Validation with one hidden layer. The classification accuracies of attacks were 92.8%, 99.4%, 92.2%, 75.6%, and 99.8% for Blackhole, Flooding, Scheduling, and Grayhole attacks, in addition to the normal case (without attacks, respectively.

  1. 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.

  2. 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.

  3. Video Salient Object Detection via Fully Convolutional Networks

    Science.gov (United States)

    Wang, Wenguan; Shen, Jianbing; Shao, Ling

    2018-01-01

    This paper proposes a deep learning model to efficiently detect salient regions in videos. It addresses two important issues: (1) deep video saliency model training with the absence of sufficiently large and pixel-wise annotated video data, and (2) fast video saliency training and detection. The proposed deep video saliency network consists of two modules, for capturing the spatial and temporal saliency information, respectively. The dynamic saliency model, explicitly incorporating saliency estimates from the static saliency model, directly produces spatiotemporal saliency inference without time-consuming optical flow computation. We further propose a novel data augmentation technique that simulates video training data from existing annotated image datasets, which enables our network to learn diverse saliency information and prevents overfitting with the limited number of training videos. Leveraging our synthetic video data (150K video sequences) and real videos, our deep video saliency model successfully learns both spatial and temporal saliency cues, thus producing accurate spatiotemporal saliency estimate. We advance the state-of-the-art on the DAVIS dataset (MAE of .06) and the FBMS dataset (MAE of .07), and do so with much improved speed (2fps with all steps).

  4. Congestion Detection and Alleviation in Multihop Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Omer Chughtai

    2017-01-01

    Full Text Available Multiple traffic flows in a dense environment of a mono-sink wireless sensor network (WSN experience congestion that leads to excessive energy consumption and severe packet loss. To address this problem, a Congestion Detection and Alleviation (CDA mechanism has been proposed. CDA exploits the features and the characteristics of the sensor nodes and the wireless links between them to detect and alleviate node- and link-level congestion. Node-level congestion is detected by examining the buffer utilisation and the interval between the consecutive data packets. However, link-level congestion is detected through a novel procedure by determining link utilisation using back-off stage of Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA. CDA alleviates congestion reactively by either rerouting the data traffic to a new less congested, more energy-efficient route or bypassing the affected node/link through ripple-based search. The simulation analysis performed in ns-2.35 evaluates CDA with Congestion Avoidance through Fairness (CAF and with No Congestion Control (NOCC protocols. The analysis shows that CDA improves packet delivery ratio by 33% as compared to CAF and 54% as compared to NOCC. CDA also shows an improvement in throughput by 16% as compared to CAF and 36% as compared to NOCC. Additionally, it reduces End-To-End delay by 17% as compared to CAF and 38% as compared to NOCC.

  5. Flash Detection Efficiencies of Long Range Lightning Detection Networks During GRIP

    Science.gov (United States)

    Mach, Douglas M.; Bateman, Monte G.; Blakeslee, Richard J.

    2012-01-01

    We flew our Lightning Instrument Package (LIP) on the NASA Global Hawk as a part of the Genesis and Rapid Intensification Processes (GRIP) field program. The GRIP program was a NASA Earth science field experiment during the months of August and September, 2010. During the program, the LIP detected lighting from 48 of the 213 of the storms overflown by the Global Hawk. The time and location of tagged LIP flashes can be used as a "ground truth" dataset for checking the detection efficiency of the various long or extended range ground-based lightning detection systems available during the GRIP program. The systems analyzed included Vaisala Long Range (LR), Vaisala GLD360, the World Wide Lightning Location Network (WWLLN), and the Earth Networks Total Lightning Network (ENTLN). The long term goal of our research is to help understand the advantages and limitations of these systems so that we can utilize them for both proxy data applications and cross sensor validation of the GOES-R Geostationary Lightning Mapper (GLM) sensor when it is launched in the 2015 timeframe.

  6. 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…

  7. Application of a Hidden Bayes Naive Multiclass Classifier in Network Intrusion Detection

    Science.gov (United States)

    Koc, Levent

    2013-01-01

    With increasing Internet connectivity and traffic volume, recent intrusion incidents have reemphasized the importance of network intrusion detection systems for combating increasingly sophisticated network attacks. Techniques such as pattern recognition and the data mining of network events are often used by intrusion detection systems to classify…

  8. The Rondonia Lightning Detection Network: Network Description, Science Objectives, Data Processing Archival/Methodology, and Results

    Science.gov (United States)

    Blakeslee, R. J.; Bailey, J. C.; Pinto, O.; Athayde, A.; Renno, N.; Weidman, C. D.

    2003-01-01

    A four station Advanced Lightning Direction Finder (ALDF) network was established in the state of Rondonia in western Brazil in 1999 through a collaboration of U.S. and Brazilian participants from NASA, INPE, INMET, and various universities. The network utilizes ALDF IMPACT (Improved Accuracy from Combined Technology) sensors to provide cloud-to-ground lightning observations (i.e., stroke/flash locations, signal amplitude, and polarity) using both time-of- arrival and magnetic direction finding techniques. The observations are collected, processed and archived at a central site in Brasilia and at the NASA/Marshall Space Flight Center in Huntsville, Alabama. Initial, non-quality assured quick-look results are made available in near real-time over the Internet. The network, which is still operational, was deployed to provide ground truth data for the Lightning Imaging Sensor (LIS) on the Tropical Rainfall Measuring Mission (TRMM) satellite that was launched in November 1997. The measurements are also being used to investigate the relationship between the electrical, microphysical and kinematic properties of tropical convection. In addition, the long-time series observations produced by this network will help establish a regional lightning climatological database, supplementing other databases in Brazil that already exist or may soon be implemented. Analytic inversion algorithms developed at the NASA/Marshall Space Flight Center have been applied to the Rondonian ALDF lightning observations to obtain site error corrections and improved location retrievals. The data will also be corrected for the network detection efficiency. The processing methodology and the results from the analysis of four years of network operations will be presented.

  9. Towards effective and robust list-based packet filter for signature-based network intrusion detection: an engineering approach

    DEFF Research Database (Denmark)

    Meng, Weizhi; Li, Wenjuan; Kwok, Lam For

    2017-01-01

    Network intrusion detection systems (NIDSs) which aim to identify various attacks, have become an essential part of current security infrastructure. In particular, signature-based NIDSs are being widely implemented in industry due to their low rate of false alarms. However, the signature matching...... process is a big challenge for these systems, in which the cost is at least linear to the size of an input string. As a result, overhead packets will be a major issue for practical usage, where the incoming packets exceed the maximum capability of an intrusion detection system (IDS). To mitigate...

  10. Capability of Ophthalmology Residents to Detect Glaucoma Using High-Dynamic-Range Concept versus Color Optic Disc Photography.

    Science.gov (United States)

    Ittarat, Mantapond; Itthipanichpong, Rath; Manassakorn, Anita; Tantisevi, Visanee; Chansangpetch, Sunee; Rojanapongpun, Prin

    2017-01-01

    Assessment of color disc photograph (C-DP) is affected by image quality, which decreases the ability to detect glaucoma. High-dynamic-range (HDR) imaging provides a greater range of luminosity. Therefore, the objective of this study was to evaluate the capability of ophthalmology residents to detect glaucoma using HDR-concept disc photography (HDR-DP) compared to C-DP. Cross-sectional study. Twenty subjects were classified by 3 glaucoma specialists as either glaucoma, glaucoma suspect, or control. All C-DPs were converted to HDR-DPs and randomly presented and assessed by 10 first-year ophthalmology residents. Sensitivity and specificity of glaucoma detection were compared. The mean ± SD of averaged retinal nerve fiber layer (RNFL) thickness was 74.0 ± 6.1 μm, 100.2 ± 9.6 μm, and 105.8 ± 17.2 μm for glaucoma, glaucoma suspect, and controls, respectively. The diagnostic sensitivity of HDR-DP was higher than that of C-DP (87% versus 68%, mean difference: 19.0, 95% CI: 4.91 to 33.1; p = 0.014). Regarding diagnostic specificity, HDR-DP and C-DP yielded 46% and 75% (mean difference: 29.0, 95% CI: 13.4 to 44.6; p = 0.002). HDR-DP statistically increased diagnostic sensitivity but not specificity. HDR-DP may be a screening tool for nonexpert ophthalmologists.

  11. 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.

  12. 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.

  13. The research of the evaluation system towards a core enterprise's network capability in the industrial technology alliance

    Science.gov (United States)

    Yan, Guangshi; Tian, Xuelian; Shen, Xue; You, Yue

    2017-05-01

    The social network theory is introduced for the industrial technology alliance based on the actual needs of the development of the industrial technology alliance. Through discussing the influence of the core enterprise network capacity on alliance performance, this article establishes evaluation system and index model of core enterprise network ability. We also evaluate and analyze the network capacity of core enterprise by fuzzy comprehensive evaluation method. So, the evaluation method is very important and full of practical value with a new research vision.

  14. 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.

  15. 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.

  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. Leveraging uncertainty information from deep neural networks for disease detection.

    Science.gov (United States)

    Leibig, Christian; Allken, Vaneeda; Ayhan, Murat Seçkin; Berens, Philipp; Wahl, Siegfried

    2017-12-19

    Deep learning (DL) has revolutionized the field of computer vision and image processing. In medical imaging, algorithmic solutions based on DL have been shown to achieve high performance on tasks that previously required medical experts. However, DL-based solutions for disease detection have been proposed without methods to quantify and control their uncertainty in a decision. In contrast, a physician knows whether she is uncertain about a case and will consult more experienced colleagues if needed. Here we evaluate drop-out based Bayesian uncertainty measures for DL in diagnosing diabetic retinopathy (DR) from fundus images and show that it captures uncertainty better than straightforward alternatives. Furthermore, we show that uncertainty informed decision referral can improve diagnostic performance. Experiments across different networks, tasks and datasets show robust generalization. Depending on network capacity and task/dataset difficulty, we surpass 85% sensitivity and 80% specificity as recommended by the NHS when referring 0-20% of the most uncertain decisions for further inspection. We analyse causes of uncertainty by relating intuitions from 2D visualizations to the high-dimensional image space. While uncertainty is sensitive to clinically relevant cases, sensitivity to unfamiliar data samples is task dependent, but can be rendered more robust.

  18. 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.

  19. 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.

  20. 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.

  1. Improved hyperspectral vegetation detection using neural networks with spectral angle mapper

    Science.gov (United States)

    Özdemir, Okan Bilge; Yardımcı ćetin, Yasemin

    2017-05-01

    Hyperspectral images have been used in many areas including city planning, mining and military decision support systems. Hyperspectral image analysis techniques have a great potential for vegetation detection and classification with their capability to identify the spectral differences across the electromagnetic spectrum due to their ability to provide information about the chemical compositions of materials. This study introduces a vegetation detection method employing Artificial Neural Network (ANN) over hyperspectral imaging. The algorithm employed backpropagation MLP algorithm for training neural networks. The performance of ANN is improved by the joint use with Spectral Angle Mapper(SAM). The algorithm first obtains the certainty measure from ANN, following the completion of this process, every pixels' angular distance is computed by SAM. The certainty measure is divided by angular distance. Results from ANN, SAM and Support Vector Machine (SVM) algorithms are compared and evaluated with the result of the algorithm. Limited number of training samples are used for training. The results demonstrate that joint use of ANN and SAM significantly improves classification accuracy for smaller training samples.

  2. Robust Fault Detection of Wind Energy Conversion Systems Based on Dynamic Neural Networks

    Directory of Open Access Journals (Sweden)

    Nasser Talebi

    2014-01-01

    Full Text Available 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.

  3. 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.

  4. 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.

  5. Neural network-based detection of pulmonary nodules on chest radiography; Identificazione mediante reti neurali dei noduli polmonari nel radiogramma del torace

    Energy Technology Data Exchange (ETDEWEB)

    Coppini, G. [Consiglio Nazionale delle Ricerche, Pisa (Italy). Ist. di Fisiologia Patologica; Valli, G. [Florence Univ., Florence (Italy). Dipt. di Ingegneria Elettronica; Falchini, M.; Stecco, A.; Bindi, A.; Carmignani, L. [Florence Univ., Florence (Italy). Dipt. di Fisiopatologia Clinica, Sezione di Radiodiagnostica

    1999-10-01

    In this report are investigated the capabilities of an artificial neural network-based Computer-Aided Diagnosis (CAD) system in improving early detection of pulmonary nodules on chest radiographs. [Italian] Valutazione di un sistema di riconoscimento automatico basato sulla tecnologia delle reti neruali artificiali per migliorare le possibilita' di rivelazione precoce dei noduli polmonari sul radiogramma toracico.

  6. 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.

  7. Combining MLP and Using Decision Tree in Order to Detect the Intrusion into Computer Networks

    OpenAIRE

    Saba Sedigh Rad; Alireza Zebarjad

    2013-01-01

    The security of computer networks has an important role in computer systems. The increasing use of computer networks results in penetration and destruction of systems by system operations. So, in order to keep the systems away from these hazards, it is essential to use the intrusion detection system (IDS). This intrusion detection is done in order to detect the illicit use and misuse and to avoid damages to the systems and computer networks by both the external and internal intruders. Intrusi...

  8. A novel approach for the fast detection of black holes in mobile ad hoc networks

    OpenAIRE

    SERRAT OLMOS, MANUEL DAVID; Hernández Orallo, Enrique; Cano Escribá, Juan Carlos; Tavares De Araujo Cesariny Calafate, Carlos Miguel; Manzoni, Pietro

    2013-01-01

    Mobile ad hoc networks are infrastructure-less wireless networks that rely on node cooperation to properly work. In this kind of networks, attack detection and reaction is a key issue to the whole network. The most common threat in mobile ad hoc network scenarios consists in the presence of a certain percentage of selfish nodes, which try to reduce the consumption of their own resources to prolong their battery lifetime. Those nodes do not collaborate on forwarding activities, therefore affec...

  9. Sensor anomaly detection in wireless sensor networks for healthcare.

    Science.gov (United States)

    Haque, Shah Ahsanul; Rahman, Mustafizur; Aziz, Syed Mahfuzul

    2015-04-15

    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).

  10. 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.

  11. Phase-Resolved Heterodyne-Detected Transient Grating Enhances the Capabilities of 2D IR Echo Spectroscopy.

    Science.gov (United States)

    Jin, Geun Young; Kim, Yung Sam

    2017-02-09

    2D IR echo spectroscopy, with high sensitivity and femtosecond time resolution, enables us to understand structure and ultrafast dynamics of molecular systems. Application of this experimental technique on weakly absorbing samples, however, had been limited by the precise and unambiguous phase determination of the echo signals. In this study, we propose a new experimental scheme that significantly increases the phase stability of the involved IR pulses. We have demonstrated that the incorporation of phase-resolved heterodyne-detected transient grating (PR-HDTG) spectroscopy greatly enhances the capabilities of 2D IR spectroscopy. The new experimental scheme has been used to obtain 2D IR spectra on weakly absorbing azide ions (N3-) in H2O (absorbance ∼0.025), free of phase ambiguity even at large waiting times. We report the estimated spectral diffusion time scale (1.056 ps) of azide ions in aqueous solution from the 2D IR spectra and the vibrational lifetime (750 ± 3 fs) and the reorientation time (1108 ± 24 fs) from the PR-HDTG spectra.

  12. 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.

  13. Social networking for older people’s well-being : An example of applying the capability approach

    NARCIS (Netherlands)

    Steen, M.G.D.; Aarts, O.A.J.; Broekman, C.C.M.T.; Prins, S.C.L.

    2011-01-01

    In this paper, we discuss several ways in which the capability approach offers a valuable and useful perspective to better understand and organize processes of designing ICT. First, we present the WeCare project, in which the authors work and which will serve as an example of applying the capability

  14. Energy-Efficient Fault-Tolerant Dynamic Event Region Detection in Wireless Sensor Networks

    DEFF Research Database (Denmark)

    Enemark, Hans-Jacob; Zhang, Yue; Dragoni, Nicola

    2015-01-01

    Fault-tolerant event detection is fundamental to wireless sensor network applications. Existing approaches usually adopt neighborhood collaboration for better detection accuracy, while need more energy consumption due to communication. Focusing on energy efficiency, this paper makes an improvement...

  15. An analysis of network traffic classification for botnet detection

    DEFF Research Database (Denmark)

    Stevanovic, Matija; Pedersen, Jens Myrup

    2015-01-01

    Botnets represent one of the most serious threats to the Internet security today. This paper explores how can network traffic classification be used for accurate and efficient identification of botnet network activity at local and enterprise networks. The paper examines the effectiveness of detec......Botnets represent one of the most serious threats to the Internet security today. This paper explores how can network traffic classification be used for accurate and efficient identification of botnet network activity at local and enterprise networks. The paper examines the effectiveness...

  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. 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.

  18. 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.

  19. 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

  20. An Approach for Detecting Attacks in Mobile Adhoc Networks

    OpenAIRE

    V. M. Viswanatham; A. A. Chari

    2008-01-01

    The security of data becomes more important with the increased use of commercial applications over wireless network environments. We presented an approach to handle various attacks for wireless networks. There were several problems of security in wireless networks due to intruders and different type of attacks such as Node Isolation, Route Disruption and Resource Consumption. There were better methods and intruder handling procedures available for fixed networks but it was difficult to analyz...

  1. EdgeCentric: Anomaly Detection in Edge-Attributed Networks

    OpenAIRE

    Shah, Neil; Beutel, Alex; Hooi, Bryan; Akoglu, Leman; Gunnemann, Stephan; Makhija, Disha; Kumar, Mohit; Faloutsos, Christos

    2015-01-01

    Given a network with attributed edges, how can we identify anomalous behavior? Networks with edge attributes are commonplace in the real world. For example, edges in e-commerce networks often indicate how users rated products and services in terms of number of stars, and edges in online social and phonecall networks contain temporal information about when friendships were formed and when users communicated with each other -- in such cases, edge attributes capture information about how the adj...

  2. Reliable epileptic seizure detection using an improved wavelet neural network

    Directory of Open Access Journals (Sweden)

    Zarita Zainuddin

    2013-05-01

    Full Text Available BackgroundElectroencephalogram (EEG signal analysis is indispensable in epilepsy diagnosis as it offers valuable insights for locating the abnormal distortions in the brain wave. However, visual interpretation of the massive amounts of EEG signals is time-consuming, and there is often inconsistent judgment between experts. AimsThis study proposes a novel and reliable seizure detection system, where the statistical features extracted from the discrete wavelet transform are used in conjunction with an improved wavelet neural network (WNN to identify the occurrence of seizures. Method Experimental simulations were carried out on a well-known publicly available dataset, which was kindly provided by the Epilepsy Center, University of Bonn, Germany. The normal and epileptic EEG signals were first pre-processed using the discrete wavelet transform. Subsequently, a set of statistical features was extracted to train a WNNs-based classifier. ResultsThe study has two key findings. First, simulation results showed that the proposed improved WNNs-based classifier gave excellent predictive ability, where an overall classification accuracy of 98.87% was obtained. Second, by using the 10th and 90th percentiles of the absolute values of the wavelet coefficients, a better set of EEG features can be identified from the data, as the outliers are removed before any further downstream analysis.ConclusionThe obtained high prediction accuracy demonstrated the feasibility of the proposed seizure detection scheme. It suggested the prospective implementation of the proposed method in developing a real time automated epileptic diagnostic system with fast and accurate response that could assist neurologists in the decision making process.

  3. 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...

  4. 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

  5. Detecting and Blocking Network Attacks at Ultra High Speeds

    Energy Technology Data Exchange (ETDEWEB)

    Paxson, Vern

    2010-11-29

    Stateful, in-depth, in-line traffic analysis for intrusion detection and prevention has grown increasingly more difficult as the data rates of modern networks rise. One point in the design space for high-performance network analysis - pursued by a number of commercial products - is the use of sophisticated custom hardware. For very high-speed processing, such systems often cast the entire analysis process in ASICs. This project pursued a different architectural approach, which we term Shunting. Shunting marries a conceptually quite simple hardware device with an Intrusion Prevention System (IPS) running on commodity PC hardware. The overall design goal is was to keep the hardware both cheap and readily scalable to future higher speeds, yet also retain the unparalleled flexibility that running the main IPS analysis in a full general-computing environment provides. The Shunting architecture we developed uses a simple in-line hardware element that maintains several large state tables indexed by packet header fields, including IP/TCP flags, source and destination IP addresses, and connection tuples. The tables yield decision values the element makes on a packet-by-packet basis: forward the packet, drop it, or divert ('shunt') it through the IPS (the default). By manipulating table entries, the IPS can, on a fine-grained basis: (i) specify the traffic it wishes to examine, (ii) directly block malicious traffic, and (iii) 'cut through' traffic streams once it has had an opportunity to 'vet' them, or (iv) skip over large items within a stream before proceeding to further analyze it. For the Shunting architecture to yield benefits, it needs to operate in an environment for which the monitored network traffic has the property that - after proper vetting - much of it can be safely skipped. This property does not universally hold. For example, if a bank needs to examine all Web traffic involving its servers for regulatory compliance, then a

  6. Research on artificial neural network intrusion detection photochemistry based on the improved wavelet analysis and transformation

    Science.gov (United States)

    Li, Hong; Ding, Xue

    2017-03-01

    This paper combines wavelet analysis and wavelet transform theory with artificial neural network, through the pretreatment on point feature attributes before in intrusion detection, to make them suitable for improvement of wavelet neural network. The whole intrusion classification model gets the better adaptability, self-learning ability, greatly enhances the wavelet neural network for solving the problem of field detection invasion, reduces storage space, contributes to improve the performance of the constructed neural network, and reduces the training time. Finally the results of the KDDCup99 data set simulation experiment shows that, this method reduces the complexity of constructing wavelet neural network, but also ensures the accuracy of the intrusion classification.

  7. 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.

  8. 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.

  9. Cross-Sensor Calibration of the GAI Long Range Detection Network

    Science.gov (United States)

    Boccippio, Dennis J.; Boeck, William; Goodman, Steven J.; Cummins, K.; Cramer, J.

    1999-01-01

    The long range component of the North American Lightning Detection Network has been providing experimental data products since July 1996, offering cloud-to-ground lightning coverage throughout the Atlantic and Western Pacific oceans, as well as south to the Intertropical Convergence Zone. The network experiences a strong decrease in detection efficiency with range, which is also significantly modulated by differential propagation under day, night and terminator-crossing conditions. A climatological comparison of total lightning data observed by the Optical Transient Detector (OTD) and CG lightning observed by the long range network is conducted, with strict quality control and allowance for differential network performance before and after the activation of the Canadian Lightning Detection Network. This yields a first-order geographic estimate of long range network detection efficiency and its spatial variability. Intercomparisons are also performed over the continental US, allowing large scale estimates of the midlatitude climatological IC:CG ratio and its possible dependence on latitude.

  10. PMFA: Toward Passive Message Fingerprint Attacks on Challenge-Based Collaborative Intrusion Detection Networks

    DEFF Research Database (Denmark)

    Li, Wenjuan; Meng, Weizhi; Kwok, Lam-For

    2016-01-01

    To enhance the performance of single intrusion detection systems (IDSs), collaborative intrusion detection networks (CIDNs) have been developed, which enable a set of IDS nodes to communicate with each other. In such a distributed network, insider attacks like collusion attacks are the main threat...

  11. 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.

  12. Wavelet neural networks initialization using hybridized clustering and harmony search algorithm: Application in epileptic seizure detection

    Science.gov (United States)

    Zainuddin, Zarita; Lai, Kee Huong; Ong, Pauline

    2013-04-01

    Artificial neural networks (ANNs) are powerful mathematical models that are used to solve complex real world problems. Wavelet neural networks (WNNs), which were developed based on the wavelet theory, are a variant of ANNs. During the training phase of WNNs, several parameters need to be initialized; including the type of wavelet activation functions, translation vectors, and dilation parameter. The conventional k-means and fuzzy c-means clustering algorithms have been used to select the translation vectors. However, the solution vectors might get trapped at local minima. In this regard, the evolutionary harmony search algorithm, which is capable of searching for near-optimum solution vectors, both locally and globally, is introduced to circumvent this problem. In this paper, the conventional k-means and fuzzy c-means clustering algorithms were hybridized with the metaheuristic harmony search algorithm. In addition to obtaining the estimation of the global minima accurately, these hybridized algorithms also offer more than one solution to a particular problem, since many possible solution vectors can be generated and stored in the harmony memory. To validate the robustness of the proposed WNNs, the real world problem of epileptic seizure detection was presented. The overall classification accuracy from the simulation showed that the hybridized metaheuristic algorithms outperformed the standard k-means and fuzzy c-means clustering algorithms.

  13. Detection of Informal Settlements from VHR Images Using Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Nicholus Mboga

    2017-10-01

    Full Text Available Information about the location and extent of informal settlements is necessary to guide decision making and resource allocation for their upgrading. Very high resolution (VHR satellite images can provide this useful information, however, different urban settlement types are hard to be automatically discriminated and extracted from VHR imagery, because of their abstract semantic class definition. State-of-the-art classification techniques rely on hand-engineering spatial-contextual features to improve the classification results of pixel-based methods. In this paper, we propose to use convolutional neural networks (CNNs for learning discriminative spatial features, and perform automatic detection of informal settlements. The experimental analysis is carried out on a QuickBird image acquired over Dar es Salaam, Tanzania. The proposed technique is compared against support vector machines (SVMs using texture features extracted from grey level co-occurrence matrix (GLCM and local binary patterns (LBP, which result in accuracies of 86.65% and 90.48%, respectively. CNN leads to better classification, resulting in an overall accuracy of 91.71%. A sensitivity analysis shows that deeper networks result in higher accuracies when large training sets are used. The study concludes that training CNN in an end-to-end fashion can automatically learn spatial features from the data that are capable of discriminating complex urban land use classes.

  14. 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

  15. 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)

  16. Multiagent Intrusion Detection Based on Neural Network Detectors and Artificial Immune System

    OpenAIRE

    Vaitsekhovich, L.; Golovko, V; Rubanau, V.

    2009-01-01

    In this article the artificial immune system and neural network techniques for intrusion detection have been addressed. The AIS allows detecting unknown samples of computer attacks. The integration of AIS and neural networks as detectors permits to increase performance of the system security. The detector structure is based on the integration of the different neural networks namely RNN and MLP. The KDD-99 dataset was used for experiments performing. The experimental results show that such int...

  17. Intrusion Detection Systems Based on Artificial Intelligence Techniques in Wireless Sensor Networks

    OpenAIRE

    Nabil Ali Alrajeh; Lloret, J.

    2013-01-01

    Intrusion detection system (IDS) is regarded as the second line of defense against network anomalies and threats. IDS plays an important role in network security. There are many techniques which are used to design IDSs for specific scenario and applications. Artificial intelligence techniques are widely used for threats detection. This paper presents a critical study on genetic algorithm, artificial immune, and artificial neural network (ANN) based IDSs techniques used in wireless sensor netw...

  18. 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 ...

  19. 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.

  20. 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. PMID:26447696

  1. 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.

  2. Active-Varying Sampling-Based Fault Detection Filter Design for Networked Control Systems

    Directory of Open Access Journals (Sweden)

    Yu-Long Wang

    2014-01-01

    Full Text Available This paper is concerned with fault detection filter design for continuous-time networked control systems considering packet dropouts and network-induced delays. The active-varying sampling period method is introduced to establish a new discretized model for the considered networked control systems. The mutually exclusive distribution characteristic of packet dropouts and network-induced delays is made full use of to derive less conservative fault detection filter design criteria. Compared with the fault detection filter design adopting a constant sampling period, the proposed active-varying sampling-based fault detection filter design can improve the sensitivity of the residual signal to faults and shorten the needed time for fault detection. The simulation results illustrate the merits and effectiveness of the proposed fault detection filter design.

  3. Intrusions Detection System Based on Ubiquitous Network Nodes

    OpenAIRE

    Sellami, Lynda; IDOUGHI, Djilali; Baadache, Abderahmane

    2014-01-01

    Ubiquitous computing allows to make data and services within the reach of users anytime and anywhere. This makes ubiquitous networks vulnerable to attacks coming from either inside or outside the network. To ensure and enhance networks security, several solutions have been implemented. These solutions are inefficient and or incomplete. Solving these challenges in security with new requirement of Ubicomp, could provide a potential future for such systems towards better mobility and higher conf...

  4. Volcanic ash and meteorological clouds detection by neural networks

    Science.gov (United States)

    Picchiani, Matteo; Del Frate, Fabio; Stefano, Corradini; Piscini, Alessandro; Merucci, Luca; Chini, Marco

    2014-05-01

    The recent eruptions of the Icelandic Eyjafjallajokull and Grímsvötn volcanoes occurred in 2010 and 2011 respectively have been highlighted the necessity to increase the accuracy of the ash detection and retrieval. Follow the evolution of the ash plume is crucial for aviation security. Indeed from the accuracy of the algorithms applied to identify the ash presence may depend the safety of the passengers. The difference between the brightness temperatures (BTD) of thermal infrared channels, centered around 11 µm and 12 µm, is suitable to distinguish the ash plume from the meteorological clouds [Prata, 1989] on satellite images. Anyway in some condition an accurate interpretation is essential to avoid false alarms. In particular Corradini et al. (2008) have developed a correction procedure aimed to avoid the atmospheric water vapour effect that tends to mask, or cancel-out, the ash plume effects on the BTD. Another relevant issue is due to the height of the meteorological clouds since their brightness temperatures is affected by this parameter. Moreover the overlapping of ash plume and meteorological clouds may affects the retrieval result since this latter is dependent by the physical temperature of the surface below the ash cloud. For this reason the correct identification of such condition, that can require a proper interpretation by the analyst, is crucial to address properly the inversion of ash parameters. In this work a fast and automatic procedure based on multispectral data from MODIS and a neural network algorithm is applied to the recent eruptions of Eyjafjallajokull and Grímsvötn volcanoes. A similar approach has been already tested with encouraging results in a previous work [Picchiani et al., 2011]. The algorithm is now improved in order to distinguish the meteorological clouds from the ash plume, dividing the latter between ash above sea and ash overlapped to meteorological clouds. The results have been compared to the BTD ones, properly

  5. 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

  6. 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.

  7. Genetic programming and cae neural networks approach for prediction of the bending capability of ZnTiCu sheets

    OpenAIRE

    Turk, R.; Peruš, I.; Kovačič, M.; Kugler, G.; Terčelj, M.

    2008-01-01

    Genetic programming (GP) and CAE NN analysis have been applied for the prediction of bending capability of rolled ZnTiCu alloy sheet. Investigation revealed that an analysis with CAE NN is faster than GP but less accurate for lower amount of data. Both methods enable good assessment of separate influencing parameters in the complex system.

  8. 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

  9. Using Active Networking to Detect and Troubleshoot Issues in Tactical Data Networks

    Science.gov (United States)

    2014-06-01

    team SDN software defined networking SIPRnet Secret Internet Protocol Router Network SSH secure shell xiv SVG Scalable Vector Graphics SNMP Simple...networking ( SDN ) paradigm, which has gained popularity in recent years, has its roots in the idea of programmable networks [6]. By extending the...addressed by SDN [6]. While there are simi- larities between SDN and active networking, SDN is primarily concerned with the idea of separating the control

  10. Detection of protein complex from protein-protein interaction network using Markov clustering

    Science.gov (United States)

    Ochieng, P. J.; Kusuma, W. A.; Haryanto, T.

    2017-05-01

    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.

  11. MULTI-LEVEL NETWORK RESILIENCE: TRAFFIC ANALYSIS, ANOMALY DETECTION AND SIMULATION

    Directory of Open Access Journals (Sweden)

    Angelos Marnerides

    2011-06-01

    Full Text Available Traffic analysis and anomaly detection have been extensively used to characterize network utilization as well as to identify abnormal network traffic such as malicious attacks. However, so far, techniques for traffic analysis and anomaly detection have been carried out independently, relying on mechanisms and algorithms either in edge or in core networks alone. In this paper we propose the notion of multi-level network resilience, in order to provide a more robust traffic analysis and anomaly detection architecture, combining mechanisms and algorithms operating in a coordinated fashion both in the edge and in the core networks. This work is motivated by the potential complementarities between the research being developed at IIT Madras and Lancaster University. In this paper we describe the current work being developed at IIT Madras and Lancaster on traffic analysis and anomaly detection, and outline the principles of a multi-level resilience architecture.

  12. An algorithm J-SC of detecting communities in complex networks

    Science.gov (United States)

    Hu, Fang; Wang, Mingzhu; Wang, Yanran; Hong, Zhehao; Zhu, Yanhui

    2017-11-01

    Currently, community detection in complex networks has become a hot-button topic. In this paper, based on the Spectral Clustering (SC) algorithm, we introduce the idea of Jacobi iteration, and then propose a novel algorithm J-SC for community detection in complex networks. Furthermore, the accuracy and efficiency of this algorithm are tested by some representative real-world networks and several computer-generated networks. The experimental results indicate that the J-SC algorithm can accurately and effectively detect the community structure in these networks. Meanwhile, compared with the state-of-the-art community detecting algorithms SC, SOM, K-means, Walktrap and Fastgreedy, the J-SC algorithm has better performance, reflecting that this new algorithm can acquire higher values of modularity and NMI. Moreover, this new algorithm has faster running time than SOM and Walktrap algorithms.

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

    Science.gov (United States)

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

    2017-06-01

    State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet [1] and Fast R-CNN [2] 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 simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features-using the recently popular terminology of neural networks with 'attention' mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model [3] , our detection system has a frame rate of 5 fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available.

  14. The harmonics detection method based on neural network applied ...

    African Journals Online (AJOL)

    user

    Consequently, many structures based on artificial neural network (ANN) have been developed in the literature, The most significant ... Keywords: Artificial Neural Networks (ANN), p-q theory, (SAPF), Harmonics, Total Harmonic Distortion. 1. ..... and pure shunt active fitters, IEEE 38th Conf on Industry Applications, Vol. 2, pp.

  15. 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).

  16. Detecting Hidden Hierarchy of Non Hierarchical Terrorist Networks

    DEFF Research Database (Denmark)

    Memon, Nasrullah

    players, characterize the structure, locate points of vulnerability, and find the efficiency of the network. To meet this challenge, we designed and developed a knowledge-base for storing and manipulating data collected from various authenticated websites. This paper applies several network centrality...

  17. 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

  18. 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.

  19. Detecting modules in biological networks by edge weight clustering and entropy significance

    Directory of Open Access Journals (Sweden)

    Paola eLecca

    2015-08-01

    Full Text Available Detection of the modular structure of biological networks is of interest to researchers adopting a systems perspective for the analysis of omics data. Computational systems biology has provided a rich array of methods for network clustering. To date, the majority of approaches address this task through a network node classification based on topological or external quantifiable properties of network nodes. Conversely, numerical properties of network edges are underused, even though the information content which can be associated with network edges has augmented due to steady advances in molecular biology technology over the last decade. Properly accounting for network edges in the development of clustering approaches can become crucial to improve quantitative interpretation of omics data. We present a novel technique for network module detection, named WG-Cluster (Weighted Graph CLUSTERing. WG-Cluster's notable features are the: (1 simultaneous exploitation of network node and edge weights to improve the biological interpretability of connected components detected, (2 assessment of their statistical significance, and (3 identification of emerging topological properties in the connected components. Applying WG-Cluster to a protein-protein network weighted by measurements of differential gene expression permitted to explore the changes in network topology under two distinct (normal vs tumour conditions.

  20. 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.

  1. 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.

  2. Fault detection and diagnosis of a gearbox in marine propulsion systems using bispectrum analysis and artificial neural networks

    Science.gov (United States)

    Li, Zhixiong; Yan, Xinping; Yuan, Chengqing; Zhao, Jiangbin; Peng, Zhongxiao

    2011-03-01

    A marine propulsion system is a very complicated system composed of many mechanical components. As a result, the vibration signal of a gearbox in the system is strongly coupled with the vibration signatures of other components including a diesel engine and main shaft. It is therefore imperative to assess the coupling effect on diagnostic reliability in the process of gear fault diagnosis. For this reason, a fault detection and diagnosis method based on bispectrum analysis and artificial neural networks (ANNs) was proposed for the gearbox with consideration given to the impact of the other components in marine propulsion systems. To monitor the gear conditions, the bispectrum analysis was first employed to detect gear faults. The amplitude-frequency plots containing gear characteristic signals were then attained based on the bispectrum technique, which could be regarded as an index actualizing forepart gear faults diagnosis. Both the back propagation neural network (BPNN) and the radial-basis function neural network (RBFNN) were applied to identify the states of the gearbox. The numeric and experimental test results show the bispectral patterns of varying gear fault severities are different so that distinct fault features of the vibrant signal of a marine gearbox can be extracted effectively using the bispectrum, and the ANN classification method has achieved high detection accuracy. Hence, the proposed diagnostic techniques have the capability of diagnosing marine gear faults in the earlier phases, and thus have application importance.

  3. An integrated PHY-MAC analytical model for IEEE 802.15.7 VLC network with MPR capability

    Science.gov (United States)

    Yu, Hai-feng; Chi, Xue-fen; Liu, Jian

    2014-09-01

    Considering that the collision caused by hidden terminal is particularly serious due to the narrow beams of optical devices, the multi-packet reception (MPR) is introduced to mitigate the collisions for IEEE 802.15.7 visible light communication (VLC) system. To explore the impact of MPR on system performance and investigate the interaction between physical (PHY) layer and media access control (MAC) layer, a three dimensional (3D) integrated PHY-MAC analytical model of carrier sense multiple access/collision avoidance (CSMA/CA) is established based on Markov chain theory for VLC system, in which MPR is implemented through the use of orthogonal code sequence. Throughput is derived to evaluate the performance of VLC system with MPR capability under imperfect optical channel. The results can be used for the performance optimization of a VLC system with MPR capability.

  4. Capability of the People’s Republic of China to Conduct Cyber Warfare and Computer Network Exploitation

    Science.gov (United States)

    2009-10-09

    capabilities or profile of virtually all organized cybercriminal enterprises and is difficult at best without some type of state-sponsorship. The type...of information often targeted for exfiltration has no inherent monetary value to cybercriminals like credit card numbers or bank account information...latitude for cybercriminal activities. The amendment also added a section criminalizing the creation and dissemination of malicious software.78

  5. 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.

  6. Developments in the use and capability of millimetre wave technologies for stand-off detection of threat items over the last decade

    Science.gov (United States)

    Ollett, E.; Clark, A.

    2017-05-01

    The Home Office Centre for Applied Science and Technology (CAST) has a longstanding history in the evaluation of passive and active millimetre wave (mmW) systems for stand-off detection. The requirements for stand-off detection have evolved greatly over the last decade due to changes in threat, as has the capability of technologies. CAST has worked with these changes to evaluate systems alongside other government departments, developing expertise in the standard of technology from low to high technology readiness level (TRL) as well as understanding the limitations in detection. In this paper I discuss the work that has been undertaken by CAST since 2007, exploring the developments in methodology that have become necessary for trials to capture the requirements successfully. This involves utilising aspects of test protocols to ensure consistency across testing between CAST and other organisations, allowing for a fair comparison of data. The trials undertaken vary from evaluating the system capability in a static setting to the capability in a crowded environment such as a shopping centre. Understanding the performance capability of passive and active (mmW) systems in crowded places is particularly important given the current threat status of the UK.

  7. Use of AI Techniques for Residential Fire Detection in Wireless Sensor Networks

    NARCIS (Netherlands)

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

    2009-01-01

    Early residential fire detection is important for prompt extinguishing and reducing damages and life losses. To detect fire, one or a combination of sensors and a detection algorithm are needed. The sensors might be part of a wireless sensor network (WSN) or work independently. The previous research

  8. Community detection based on "clumpiness" matrix in complex networks

    CERN Document Server

    Faqeeh, Ali

    2011-01-01

    The "clumpiness" matrix of a network is used to develop a method to identify its community structure. A "projection space" is constructed from the eigenvectors of the clumpiness matrix and a border line is defined using some kind of angular distance in this space. The community structure of the network is identified using this borderline and/or the hierarchical clustering method. The performance of our algorithm is tested on some computer-generated and real-world networks. The accuracy of the results is checked using normalized mutual information. The effect of community size heterogeneity on the accuracy of the method is also discussed.

  9. 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.

  10. A prototype implementation of a network-level intrusion detection system. Technical report number CS91-11

    Energy Technology Data Exchange (ETDEWEB)

    Heady, R.; Luger, G.F.; Maccabe, A.B.; Servilla, M.; Sturtevant, J. [New Mexico Univ., Albuquerque, NM (United States). Dept. of Computer Science

    1991-05-15

    This paper presents the implementation of a prototype network level intrusion detection system. The prototype system monitors base level information in network packets (source, destination, packet size, time, and network protocol), learning the normal patterns and announcing anomalies as they occur. The goal of this research is to determine the applicability of current intrusion detection technology to the detection of network level intrusions. In particular, the authors are investigating the possibility of using this technology to detect and react to worm programs.

  11. Neural networks for error detection and data aggregation in wireless sensor network

    OpenAIRE

    Saeid Bahanfar; Helia Kousha; Ladan Darougaran

    2011-01-01

    Correct information and data aggregation are very important in wireless sensor networks because sending incorrect information by fault sensors make to wrong decision about environment and increasing defective sensor during the time incorrect data decries reliability of wireless sensor networks. Previous methods have Problems such as there are fault sensors in wireless sensor network therefore wrong data are sent to CH by these sensors. In this paper apply the neural network within the sensors...

  12. 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.

  13. 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.

  14. A hybrid network intrusion detection framework based on random forests and weighted k-means

    Directory of Open Access Journals (Sweden)

    Reda M. Elbasiony

    2013-12-01

    Full Text Available Many current NIDSs are rule-based systems, which are very difficult in encoding rules, and cannot detect novel intrusions. Therefore, a hybrid detection framework that depends on data mining classification and clustering techniques is proposed. In misuse detection, random forests classification algorithm is used to build intrusion patterns automatically from a training dataset, and then matches network connections to these intrusion patterns to detect network intrusions. In anomaly detection, the k-means clustering algorithm is used to detect novel intrusions by clustering the network connections’ data to collect the most of intrusions together in one or more clusters. In the proposed hybrid framework, the anomaly part is improved by replacing the k-means algorithm with another one called weighted k-means algorithm, moreover, it uses a proposed method in choosing the anomalous clusters by injecting known attacks into uncertain connections data. Our approaches are evaluated over the Knowledge Discovery and Data Mining (KDD’99 datasets.

  15. Design of Hybrid Network Anomalies Detection System (H-NADS Using IP Gray Space Analysis

    Directory of Open Access Journals (Sweden)

    Yogendra Kumar JAIN

    2009-01-01

    Full Text Available In Network Security, there is a major issue to secure the public or private network from abnormal users. It is because each network is made up of users, services and computers with a specific behavior that is also called as heterogeneous system. To detect abnormal users, anomaly detection system (ADS is used. In this paper, we present a novel and hybrid Anomaly Detection System with the uses of IP gray space analysis and dominant scanning port identification heuristics used to detect various anomalous users with their potential behaviors. This methodology is the combination of both statistical and rule based anomaly detection which detects five types of anomalies with their three types of potential behaviors and generates respective alarm messages to GUI.

  16. 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.

  17. 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.

  18. 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.

  19. How modular structure can simplify tasks on networks: parameterizing graph optimization by fast local community detection.

    Science.gov (United States)

    Bui-Xuan, Binh-Minh; Jones, Nick S

    2014-10-08

    By considering the task of finding the shortest walk through a Network, we find an algorithm for which the run time is not as O(2 n ), with n being the number of nodes, but instead scales with the number of nodes in a coarsened network. This coarsened network has a number of nodes related to the number of dense regions in the original graph. Since we exploit a form of local community detection as a preprocessing, this work gives support to the project of developing heuristic algorithms for detecting dense regions in networks: preprocessing of this kind can accelerate optimization tasks on networks. Our work also suggests a class of empirical conjectures for how structural features of efficient networked systems might scale with system size.

  20. Detecting spatial ontogenetic niche shifts in complex dendritic ecological networks

    Science.gov (United States)

    Fields, William R.; Grant, Evan H. Campbell; Lowe, Winsor H.

    2017-01-01

    Ontogenetic niche shifts (ONS) are important drivers of population and community dynamics, but they can be difficult to identify for species with prolonged larval or juvenile stages, or for species that inhabit continuous habitats. Most studies of ONS focus on single transitions among discrete habitat patches at local scales. However, for species with long larval or juvenile periods, affinity for particular locations within connected habitat networks may differ among cohorts. The resulting spatial patterns of distribution can result from a combination of landscape-scale habitat structure, position of a habitat patch within a network, and local habitat characteristics—all of which may interact and change as individuals grow. We estimated such spatial ONS for spring salamanders (Gyrinophilus porphyriticus), which have a larval period that can last 4 years or more. Using mixture models to identify larval cohorts from size frequency data, we fit occupancy models for each age class using two measures of the branching structure of stream networks and three measures of stream network position. Larval salamander cohorts showed different preferences for the position of a site within the stream network, and the strength of these responses depended on the basin-wide spatial structure of the stream network. The isolation of a site had a stronger effect on occupancy in watersheds with more isolated headwater streams, while the catchment area, which is associated with gradients in stream habitat, had a stronger effect on occupancy in watersheds with more paired headwater streams. Our results show that considering the spatial structure of habitat networks can provide new insights on ONS in long-lived species.

  1. A Network Intrusions Detection System based on a Quantum Bio Inspired Algorithm

    OpenAIRE

    Soliman, Omar S.; Rassem, Aliaa

    2014-01-01

    Network intrusion detection systems (NIDSs) have a role of identifying malicious activities by monitoring the behavior of networks. Due to the currently high volume of networks trafic in addition to the increased number of attacks and their dynamic properties, NIDSs have the challenge of improving their classification performance. Bio-Inspired Optimization Algorithms (BIOs) are used to automatically extract the the discrimination rules of normal or abnormal behavior to improve the classificat...

  2. Intrusion Prevention/Intrusion Detection System (IPS/IDS) for Wifi Networks

    OpenAIRE

    Michal Korcak; Jaroslav Lamer; Frantisek Jakab

    2014-01-01

    The nature of wireless networks itself created new vulnerabilities that in the classical wired network s do not exist. This results in an evolutional requireme nt to implement new sophisticated security mechanis m in form of Intrusion Detection and Prevention Systems. This paper deals with security issues of small off ice and home office wireless networks. The goal of our work is to design and evaluate wireless IDPS with u se of packet injection method. Dec...

  3. 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

  4. Game theory and extremal optimization for community detection in complex dynamic networks.

    Directory of Open Access Journals (Sweden)

    Rodica Ioana Lung

    Full Text Available 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.

  5. 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.

  6. An Agent-Based Intrusion Detection System for Local Area Networks

    National Research Council Canada - National Science Library

    Jaydip Sen

    2010-01-01

    Since it is impossible to predict and identify all the vulnerabilities of a network beforehand, and penetration into a system by malicious intruders cannot always be prevented, intrusion detection systems (IDSs...

  7. 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...

  8. 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.

  9. A scanning method for detecting clustering pattern of both attribute and structure in social networks

    Science.gov (United States)

    Wang, Tai-Chi; Phoa, Frederick Kin Hing

    2016-03-01

    Community/cluster is one of the most important features in social networks. Many cluster detection methods were proposed to identify such an important pattern, but few were able to identify the statistical significance of the clusters by considering the likelihood of network structure and its attributes. Based on the definition of clustering, we propose a scanning method, originated from analyzing spatial data, for identifying clusters in social networks. Since the properties of network data are more complicated than those of spatial data, we verify our method's feasibility via simulation studies. The results show that the detection powers are affected by cluster sizes and connection probabilities. According to our simulation results, the detection accuracy of structure clusters and both structure and attribute clusters detected by our proposed method is better than that of other methods in most of our simulation cases. In addition, we apply our proposed method to some empirical data to identify statistically significant clusters.

  10. 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...

  11. 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.

  12. Node ID based detection of Sybil attack in mobile wireless sensor network

    Science.gov (United States)

    Sharmila, S.; Umamaheswari, G.

    2013-10-01

    Security is the major issue in wireless sensor networks and many defence mechanisms have been developed to secure the network from these alarming attacks by detecting the malicious nodes which hinder the performance of the network. Sybil attack can make the network vulnerable. Sybil attack means a node which illegitimately claims multiple identities. This attack threatens wireless sensor network in routing, voting system, fair resource allocation, data aggregation and misbehaviour detection. Hence, the research is carried out to prevent the Sybil attack and improve the network performance. The node ID-based scheme is proposed, where the detection is based on node registration, consisting of two phases and the assignment of ID to the node is done dynamically. The ID's corresponding to the nodes registered is at the base station and the node active time is monitored, any abnormalities in the above phases confirm the presence of Sybil nodes in the network. The scheme is simulated using NS2. The energy consumed for this algorithm is 2.3 J. The proposed detection scheme is analysed based on the network's PDR and found that the throughput has improved, which prove that this scheme may be used in the environment where security is needed.

  13. 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

  14. Weighted modularity optimization for crisp and fuzzy community detection in large-scale networks

    Science.gov (United States)

    Cao, Jie; Bu, Zhan; Gao, Guangliang; Tao, Haicheng

    2016-11-01

    Community detection is a classic and very difficult task in the field of complex network analysis, principally for its applications in domains such as social or biological networks analysis. One of the most widely used technologies for community detection in networks is the maximization of the quality function known as modularity. However, existing work has proved that modularity maximization algorithms for community detection may fail to resolve communities in small size. Here we present a new community detection method, which is able to find crisp and fuzzy communities in undirected and unweighted networks by maximizing weighted modularity. The algorithm derives new edge weights using the cosine similarity in order to go around the resolution limit problem. Then a new local moving heuristic based on weighted modularity optimization is proposed to cluster the updated network. Finally, the set of potentially attractive clusters for each node is computed, to further uncover the crisply fuzzy partition of the network. We give demonstrative applications of the algorithm to a set of synthetic benchmark networks and six real-world networks and find that it outperforms the current state of the art proposals (even those aimed at finding overlapping communities) in terms of quality and scalability.

  15. ATLANTIDES: An Architecture for Alert Verification in Network Intrusion Detection Systems

    NARCIS (Netherlands)

    Bolzoni, D.; Crispo, Bruno; Etalle, Sandro

    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

  16. ATLANTIDES: Automatic Configuration for Alert Verification in Network Intrusion Detection Systems

    NARCIS (Netherlands)

    Bolzoni, D.; Crispo, B.; Etalle, Sandro

    2008-01-01

    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

  17. Progress towards design elements for a Great Lakes-wide aquatic invasive species early detection network

    Science.gov (United States)

    Great Lakes coastal systems are vulnerable to introduction of a wide variety of non-indigenous species (NIS), and the desire to effectively respond to future invaders is prompting efforts towards establishing a broad early-detection network. Such a network requires statistically...

  18. Usage of Modified Holt-Winters Method in the Anomaly Detection of Network Traffic: Case Studies

    Directory of Open Access Journals (Sweden)

    Maciej Szmit

    2012-01-01

    Full Text Available The traditional Holt-Winters method is used, among others, in behavioural analysis of network traffic for development of adaptive models for various types of traffic in sample computer networks. This paper is devoted to the application of extended versions of these models for development of predicted templates and intruder detection.

  19. Usage of Modified Holt-Winters Method in the Anomaly Detection of Network Traffic: Case Studies

    OpenAIRE

    Maciej Szmit; Anna Szmit

    2012-01-01

    The traditional Holt-Winters method is used, among others, in behavioural analysis of network traffic for development of adaptive models for various types of traffic in sample computer networks. This paper is devoted to the application of extended versions of these models for development of predicted templates and intruder detection.

  20. Positive cloud-to-ground lightning detection by a direction-finder network

    Science.gov (United States)

    Macgorman, Donald R.; Taylor, William L.

    1989-01-01

    Consideration is given to the ability of an automatic direction-finder network to identify cloud-to-ground flashes that effectively lower positive charge to the ground (+CG flashes). Records from an extremely low frequency system are examined to determine whether or not 340 +CG flashes detected by the network have coincident waveforms characteristic of +CG flashes. It is found that false detection in the system is negligible for +CG flashes with range-normalized amplitudes of at least 50 direction-finder units. Also, it is shown that no more than about 15 percent of the +CG flashes detected by the system at smaller amplitudes are false detections.