An Intrusion Detection System for the Protection of Railway Assets Using Fiber Bragg Grating Sensors

Directory of Open Access Journals (Sweden)

Angelo Catalano

2014-09-01

Full Text Available We demonstrate the ability of Fiber Bragg Gratings (FBGs sensors to protect large areas from unauthorized activities in railway scenarios such as stations or tunnels. We report on the technological strategy adopted to protect a specific depot, representative of a common scenario for security applications in the railway environment. One of the concerns in the protection of a railway area centers on the presence of rail-tracks, which cannot be obstructed with physical barriers. We propose an integrated optical fiber system composed of FBG strain sensors that can detect human intrusion for protection of the perimeter combined with FBG accelerometer sensors for protection of rail-track access. Several trials were carried out in indoor and outdoor environments. The results demonstrate that FBG strain sensors bonded under a ribbed rubber mat enable the detection of intruder break-in via the pressure induced on the mat, whereas the FBG accelerometers installed under the rails enable the detection of intruders walking close to the railroad tracks via the acoustic surface waves generated by footsteps. Based on a single enabling technology, this integrated system represents a valuable intrusion detection system for railway security and could be integrated with other sensing functionalities in the railway field using fiber optic technology.

The evolution of Interior Intrusion Detection Technology at Sandia National Laboratories

International Nuclear Information System (INIS)

Graham, R.H.; Workhoven, R.M.

1987-07-01

Interior Intrusion Detection Technology began at Sandia National Laboratories (SNL) in 1975 as part of the Fixed Facilities Physical Protection Research and Development program sponsored by the US Department of Energy in connection with their nuclear safeguards effort. This paper describes the evolution of Interior Intrusion Detection Technology at Sandia National Laboratories from the beginning of the Interior Sensor Laboratory to the present. This Laboratory was established in 1976 to evaluate commercial interior intrusion sensors and to assist in site-specific intrusion detection system designs. Examples of special test techniques and new test equipment that were developed at the Lab are presented, including the Sandia Intruder Motion Simulator (SIMS), the Sensor and Environment Monitor (SEM), and the Sandia Interior Robot (SIR). We also discuss new sensors and unique sensor combinations developed when commercial sensors were unavailable and the future application of expert systems

The evolution of interior intrusion detection technology at Sandia National Laboratories

International Nuclear Information System (INIS)

Graham, R.H.; Workhoven, R.M.

1987-07-01

Interior Intrusion Detection Technology began at Sandia National Laboratories (SNL) in 1975 as part of the Fixed Facilities Physical Protection Research and Development program sponsored by the US Department of Energy in connection with their nuclear safeguards effort. This paper describes the evolution of Interior Intrusion Detection Technology at Sandia National Laboratories from the beginning of the Interior Sensor Laboratory to the present. This Laboratory was established in 1976 to evaluate commercial interior intrusion sensors and to assist in site-specific intrusion detection system designs. Examples of special test techniques and new test equipment that were developed at the Lab are presented, including the Sandia Intruder Motion Simulator (SIMS), the Sensor and Environment Monitor (SEM), and the Sandia Interior Robot (SIR). We also discuss new sensors and unique sensor combination developed when commercial sensors were unavailable and the future application of expert systems. 5 refs

The evolution of interior intrusion detection technology at Sandia National Laboratories

International Nuclear Information System (INIS)

Graham, R.H.; Workhoven, R.M.

1987-01-01

Interior Intrusion Detection Technology began at Sandia National Laboratories (SNL) in 1975 as part of the Fixed Facilities Physical Protection Research and Development program sponsored by the U.S. Department of Energy in connection with their nuclear safeguards effort. This paper describes the evolution of Interior Intrusion Detection Technology at Sandia National Laboratories from the beginning of the Interior Sensor Laboratory to the present. This Laboratory was established in 1976 to evaluate commercial interior intrusion sensors and to assist in site-specific intrusion detection system designs. Examples of special test techniques and new test equipment that were developed at the Lab are presented, including the Sandia Intruder Motion Simulator (SIMS), the Sensor and Environment Monitor (SEM), and the Sandia Interior Robot (SIR). The authors also discuss new sensors and unique sensor combinations developed when commercial sensors were unavailable and the future application of expert systems

Evaluation of intrusion sensors and video assessment in areas of restricted passage

International Nuclear Information System (INIS)

Hoover, C.E.; Ringler, C.E.

1996-04-01

This report discusses an evaluation of intrusion sensors and video assessment in areas of restricted passage. The discussion focuses on applications of sensors and video assessment in suspended ceilings and air ducts. It also includes current and proposed requirements for intrusion detection and assessment. Detection and nuisance alarm characteristics of selected sensors as well as assessment capabilities of low-cost board cameras were included in the evaluation

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

Smart container UWB sensor system for situational awareness of intrusion alarms

Science.gov (United States)

Romero, Carlos E.; Haugen, Peter C.; Zumstein, James M.; Leach, Jr., Richard R.; Vigars, Mark L.

2013-06-11

An in-container monitoring sensor system is based on an UWB radar intrusion detector positioned in a container and having a range gate set to the farthest wall of the container from the detector. Multipath reflections within the container make every point on or in the container appear to be at the range gate, allowing intrusion detection anywhere in the container. The system also includes other sensors to provide false alarm discrimination, and may include other sensors to monitor other parameters, e.g. radiation. The sensor system also includes a control subsystem for controlling system operation. Communications and information extraction capability may also be included. A method of detecting intrusion into a container uses UWB radar, and may also include false alarm discrimination. A secure container has an UWB based monitoring system

Intrusion detection system elements

International Nuclear Information System (INIS)

Eaton, M.J.; Mangan, D.L.

1980-09-01

This report highlights elements required for an intrusion detection system and discusses problems which can be encountered in attempting to make the elements effective. Topics discussed include: sensors, both for exterior detection and interior detection; alarm assessment systems, with the discussion focused on video assessment; and alarm reporting systems, including alarm communication systems and dislay/console considerations. Guidance on careful planning and design of a new or to-be-improved system is presented

Reduction of Motion Artifacts and Improvement of R Peak Detecting Accuracy Using Adjacent Non-Intrusive ECG Sensors

Directory of Open Access Journals (Sweden)

Minho Choi

2016-05-01

Full Text Available Non-intrusive electrocardiogram (ECG monitoring has many advantages: easy to measure and apply in daily life. However, motion noise in the measured signal is the major problem of non-intrusive measurement. This paper proposes a method to reduce the noise and to detect the R peaks of ECG in a stable manner in a sitting arrangement using non-intrusive sensors. The method utilizes two capacitive ECG sensors (cECGs to measure ECG, and another two cECGs located adjacent to the sensors for ECG are added to obtain the information on motion. Then, active noise cancellation technique and the motion information are used to reduce motion noise. To verify the proposed method, ECG was measured indoors and during driving, and the accuracy of the detected R peaks was compared. After applying the method, the sum of sensitivity and positive predictivity increased 8.39% on average and 26.26% maximally in the data. Based on the results, it was confirmed that the motion noise was reduced and that more reliable R peak positions could be obtained by the proposed method. The robustness of the new ECG measurement method will elicit benefits to various health care systems that require noninvasive heart rate or heart rate variability measurements.

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.

Cross-layer design for intrusion detection and data security in wireless ad hoc sensor networks

Science.gov (United States)

Hortos, William S.

2007-09-01

A wireless ad hoc sensor network is a configuration for area surveillance that affords rapid, flexible deployment in arbitrary threat environments. There is no infrastructure support and sensor nodes communicate with each other only when they are in transmission range. The nodes are severely resource-constrained, with limited processing, memory and power capacities and must operate cooperatively to fulfill a common mission in typically unattended modes. In a wireless sensor network (WSN), each sensor at a node can observe locally some underlying physical phenomenon and sends a quantized version of the observation to sink (destination) nodes via wireless links. Since the wireless medium can be easily eavesdropped, links can be compromised by intrusion attacks from nodes that may mount denial-of-service attacks or insert spurious information into routing packets, leading to routing loops, long timeouts, impersonation, and node exhaustion. A cross-layer design based on protocol-layer interactions is proposed for detection and identification of various intrusion attacks on WSN operation. A feature set is formed from selected cross-layer parameters of the WSN protocol to detect and identify security threats due to intrusion attacks. A separate protocol is not constructed from the cross-layer design; instead, security attributes and quantified trust levels at and among nodes established during data exchanges complement customary WSN metrics of energy usage, reliability, route availability, and end-to-end quality-of-service (QoS) provisioning. Statistical pattern recognition algorithms are applied that use observed feature-set patterns observed during network operations, viewed as security audit logs. These algorithms provide the "best" network global performance in the presence of various intrusion attacks. A set of mobile (software) agents distributed at the nodes implement the algorithms, by moving among the layers involved in the network response at each active node

A subtractive approach to interior intrusion detection system design

International Nuclear Information System (INIS)

Sons, R.J.; Graham, R.H. Jr.

1986-01-01

This paper discusses the subtractive approach to interior intrusion detection system design which assumes that all sensors are viable candidates until they are subjected to the constraints imposed by a particular facility. The constraints are determined by a sequence of questions concerning parameters such as threat definition, facility description and operation, environment, assets to be protected, security system capabilities, and cost. As a result of the questioning, some sensors will be eliminated from the candidate list, and the ''best'' set of sensors for the facility will remain. This form of questioning could be incorporated into an expert system aiding future intrusion detection system designs

Experimental Study of Nuclear Security System Components for Achieving the Intrusion Process via Sensor's Network System

International Nuclear Information System (INIS)

EL-Kafas, A.A.

2011-01-01

Cluster sensors are one of nuclear security system components which are used to detect any intrusion process of the nuclear sites. In this work, an experimental measuring test for sensor performance and procedures are presented. Sensor performance testing performed to determine whether a particular sensor will be acceptable in a proposed design. We have access to a sensors test field in which the sensor of interest is already properly installed and the parameters have been set to optimal levels by preliminary testing. The glass-breakage (G.B) and open door (O.D) sensors construction, operation and design for the investigated nuclear site are explained. Intrusion tests were carried out inside the field areas of the sensors to evaluate the sensor performance during the intrusion process. Experimental trials were performed for achieving the intrusion process via sensor network system. The performance and intrusion senses of cluster sensors inside the internal zones was recorded and evaluated. The obtained results explained that the tested and experimented G.B sensors have a probability of detection P (D) value 65% founded, and 80% P (D) of Open-door sensor

Multisensor Fusion for Intrusion Detection and Situational Awareness

OpenAIRE

Hallstensen, Christoffer V

2017-01-01

Cybercrime damage costs the world several trillion dollars annually. And al-though technical solutions to protect organizations from hackers are being con-tinuously developed, criminals learn fast to circumvent them. The question is,therefore, how to create leverage to protect an organization by improving in-trusion detection and situational awareness? This thesis seeks to contribute tothe prior art in intrusion detection and situational awareness by using a multi-sensor data fusion...

Perimeter intrusion detection and assessment system

International Nuclear Information System (INIS)

Eaton, M.J.; Jacobs, J.; McGovern, D.E.

1977-11-01

To obtain an effective perimeter intrusion detection system requires careful sensor selection, procurement, and installation. The selection process involves a thorough understanding of the unique site features and how these features affect the performance of each type of sensor. It is necessary to develop procurement specifications to establish acceptable sensor performance limits. Careful explanation and inspection of critical installation dimensions is required during on-site construction. The implementation of these activities at a particular site is discussed

A Survey on Cross-Layer Intrusion Detection System for Wireless ...

African Journals Online (AJOL)

pc

2018-03-05

Mar 5, 2018 ... forwarding, and open wireless medium are the factors that make ... Wireless Sensor Network (WSN) is a kind of network that ... These tiny sensors are mainly small sized and have low ..... they were integrated to WSN for intrusion detection in ..... Anomaly Detection Techniques for Smart City Wireless Sensor.

Nuisance alarm suppression techniques for fibre-optic intrusion detection systems

Science.gov (United States)

Mahmoud, Seedahmed S.; Visagathilagar, Yuvaraja; Katsifolis, Jim

2012-02-01

The suppression of nuisance alarms without degrading sensitivity in fibre-optic intrusion detection systems is important for maintaining acceptable performance. Signal processing algorithms that maintain the POD and minimize nuisance alarms are crucial for achieving this. A level crossings algorithm is presented for suppressing torrential rain-induced nuisance alarms in a fibre-optic fence-based perimeter intrusion detection system. Results show that rain-induced nuisance alarms can be suppressed for rainfall rates in excess of 100 mm/hr, and intrusion events can be detected simultaneously during rain periods. The use of a level crossing based detection and novel classification algorithm is also presented demonstrating the suppression of nuisance events and discrimination of nuisance and intrusion events in a buried pipeline fibre-optic intrusion detection system. The sensor employed for both types of systems is a distributed bidirectional fibre-optic Mach Zehnder interferometer.

An Estimation of a Passive Infra-Red Sensor Probability of Detection

International Nuclear Information System (INIS)

Osman, E.A.; El-Gazar, M.I.; Shaat, M.K.; El-Kafas, A.A.; Zidan, W.I.; Wadoud, A.A.

2009-01-01

Passive Infera-Red (PIR) sensors are one of many detection sensors are used to detect any intrusion process of the nuclear sites. In this work, an estimation of a PIR Sensor's Probability of Detection of a hypothetical facility is presented. sensor performance testing performed to determine whether a particular sensor will be acceptable in a proposed design. We have access to a sensor test field in which the sensor of interest is already properly installed and the parameters have been set to optimal levels by preliminary testing. The PIR sensor construction, operation and design for the investigated nuclear site are explained. Walking and running intrusion tests were carried out inside the field areas of the PIR sensor to evaluate the sensor performance during the intrusion process. 10 trials experimentally performed for achieving the intrusion process via a passive infra-red sensor's network system. The performance and intrusion senses of PIR sensors inside the internal zones was recorded and evaluated.

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.

Capability for intrusion detection at nuclear fuel sites

International Nuclear Information System (INIS)

1978-03-01

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

Intrusion recognition for optic fiber vibration sensor based on the selective attention mechanism

Science.gov (United States)

Xu, Haiyan; Xie, Yingjuan; Li, Min; Zhang, Zhuo; Zhang, Xuewu

2017-11-01

Distributed fiber-optic vibration sensors receive extensive investigation and play a significant role in the sensor panorama. A fiber optic perimeter detection system based on all-fiber interferometric sensor is proposed, through the back-end analysis, processing and intelligent identification, which can distinguish effects of different intrusion activities. In this paper, an intrusion recognition based on the auditory selective attention mechanism is proposed. Firstly, considering the time-frequency of vibration, the spectrogram is calculated. Secondly, imitating the selective attention mechanism, the color, direction and brightness map of the spectrogram is computed. Based on these maps, the feature matrix is formed after normalization. The system could recognize the intrusion activities occurred along the perimeter sensors. Experiment results show that the proposed method for the perimeter is able to differentiate intrusion signals from ambient noises. What's more, the recognition rate of the system is improved while deduced the false alarm rate, the approach is proved by large practical experiment and project.

Wireless sensing without sensors—an experimental study of motion/intrusion detection using RF irregularity

International Nuclear Information System (INIS)

Lee, Pius W Q; Tan, Hwee-Pink; Seah, Winston K G; Yao, Zexi

2010-01-01

Motion and intrusion detection are often cited as wireless sensor network (WSN) applications with typical configurations comprising clusters of wireless nodes equipped with motion sensors to detect human motion. Currently, WSN performance is subjected to several constraints, namely radio irregularity and finite on-board computation/energy resources. Radio irregularity in radio frequency (RF) propagation rises to a higher level in the presence of human activity due to the absorption effect of the human body. In this paper, we investigate the feasibility of monitoring RF transmission for the purpose of intrusion detection through experimentation. With empirical data obtained from the Crossbow TelosB platform in several different environments, the impact of human activity on the signal strength of RF signals in a WSN is evaluated. We then propose a novel approach to intrusion detection by turning a constraint in WSN, namely radio irregularity, into an advantage for the purpose of intrusion detection, using signal fluctuations to detect the presence of human activity within the WSN. Unlike RF fingerprinting, the 'intruders' here neither transmit nor receive any RF signals. By enabling existing wireless infrastructures to serve as intrusion detectors instead of deploying numerous costly sensors, this approach shows great promise for providing novel solutions

In-situ trainable intrusion detection system

Energy Technology Data Exchange (ETDEWEB)

Symons, Christopher T.; Beaver, Justin M.; Gillen, Rob; Potok, Thomas E.

2016-11-15

A computer implemented method detects intrusions using a computer by analyzing network traffic. The method includes a semi-supervised learning module connected to a network node. The learning module uses labeled and unlabeled data to train a semi-supervised machine learning sensor. The method records events that include a feature set made up of unauthorized intrusions and benign computer requests. The method identifies at least some of the benign computer requests that occur during the recording of the events while treating the remainder of the data as unlabeled. The method trains the semi-supervised learning module at the network node in-situ, such that the semi-supervised learning modules may identify malicious traffic without relying on specific rules, signatures, or anomaly detection.

Distributed Intrusion Sensor Using DFB Laser with Optical Feedback and Saturable Absorber

Directory of Open Access Journals (Sweden)

Kyoo Nam Choi

2018-01-01

Full Text Available Characteristics of a distributed intrusion sensor using a coherent DFB laser diode with an external optical feedback and saturable absorber were experimentally investigated. The stimulus at a location of 2 km using a PZT transducer placed the location of a simulated intruder in Φ-OTDR trace after averaging 32 times. Field trials demonstrated the detection of a vehicle and a pedestrian crossing above the sensing line and a loop in a burial depth of 50 cm. This distributed intrusion sensor using a coherent DFB laser diode as the light source had the advantages of a simple structure and intruder detection capability at the underground burial location.

Enhancing collaborative intrusion detection networks against insider attacks using supervised intrusion sensitivity-based trust management model

DEFF Research Database (Denmark)

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

2017-01-01

To defend against complex attacks, collaborative intrusion detection networks (CIDNs) have been developed to enhance the detection accuracy, which enable an IDS to collect information and learn experience from others. However, this kind of networks is vulnerable to malicious nodes which are utili......To defend against complex attacks, collaborative intrusion detection networks (CIDNs) have been developed to enhance the detection accuracy, which enable an IDS to collect information and learn experience from others. However, this kind of networks is vulnerable to malicious nodes which...... are utilized by insider attacks (e.g., betrayal attacks). In our previous research, we developed a notion of intrusion sensitivity and identified that it can help improve the detection of insider attacks, whereas it is still a challenge for these nodes to automatically assign the values. In this article, we...... of intrusion sensitivity based on expert knowledge. In the evaluation, we compare the performance of three different supervised classifiers in assigning sensitivity values and investigate our trust model under different attack scenarios and in a real wireless sensor network. Experimental results indicate...

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%.

Quality-of-service sensitivity to bio-inspired/evolutionary computational methods for intrusion detection in wireless ad hoc multimedia sensor networks

Science.gov (United States)

Hortos, William S.

2012-06-01

In the author's previous work, a cross-layer protocol approach to wireless sensor network (WSN) intrusion detection an identification is created with multiple bio-inspired/evolutionary computational methods applied to the functions of the protocol layers, a single method to each layer, to improve the intrusion-detection performance of the protocol over that of one method applied to only a single layer's functions. The WSN cross-layer protocol design embeds GAs, anti-phase synchronization, ACO, and a trust model based on quantized data reputation at the physical, MAC, network, and application layer, respectively. The construct neglects to assess the net effect of the combined bioinspired methods on the quality-of-service (QoS) performance for "normal" data streams, that is, streams without intrusions. Analytic expressions of throughput, delay, and jitter, coupled with simulation results for WSNs free of intrusion attacks, are the basis for sensitivity analyses of QoS metrics for normal traffic to the bio-inspired methods.

State of the Practice of Intrusion Detection Technologies

Science.gov (United States)

2000-01-01

functions, procedures, and scripts, an Oracle database structure, Borne shell scripts, and configuration files which together communicate with ASIM Sensor...34Plugging the Holes in eCommerce Leads to 135% Growth in the Intrusion Detection and Vulnerability Assessment Software Market," PRNewswire. August

Typed Linear Chain Conditional Random Fields and Their Application to Intrusion Detection

Science.gov (United States)

Elfers, Carsten; Horstmann, Mirko; Sohr, Karsten; Herzog, Otthein

Intrusion detection in computer networks faces the problem of a large number of both false alarms and unrecognized attacks. To improve the precision of detection, various machine learning techniques have been proposed. However, one critical issue is that the amount of reference data that contains serious intrusions is very sparse. In this paper we present an inference process with linear chain conditional random fields that aims to solve this problem by using domain knowledge about the alerts of different intrusion sensors represented in an ontology.

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

Science.gov (United States)

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

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

Non-intrusive refractometer sensor

Indian Academy of Sciences (India)

An experimental realization of a simple non-intrusive refractometer sensor .... and after amplification is finally read by a digital multimeter (Fluke make: 179 true ... To study the response of the present FO refractometer, propylene glycol has been ... values of all the samples were initially measured by Abbe's refractometer.

Rapid deployment intrusion detection system

International Nuclear Information System (INIS)

Graham, R.H.

1997-01-01

A rapidly deployable security system is one that provides intrusion detection, assessment, communications, and annunciation capabilities; is easy to install and configure; can be rapidly deployed, and is reusable. A rapidly deployable intrusion detection system (RADIDS) has many potential applications within the DOE Complex: back-up protection for failed zones in a perimeter intrusion detection and assessment system, intrusion detection and assessment capabilities in temporary locations, protection of assets during Complex reconfiguration, and protection in hazardous locations, protection of assets during Complex reconfiguration, and protection in hazardous locations. Many DOE user-need documents have indicated an interest in a rapidly deployable intrusion detection system. The purpose of the RADIDS project is to design, develop, and implement such a system. 2 figs

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.

Active Low Intrusion Hybrid Monitor for Wireless Sensor Networks.

Science.gov (United States)

Navia, Marlon; Campelo, Jose C; Bonastre, Alberto; Ors, Rafael; Capella, Juan V; Serrano, Juan J

2015-09-18

Several systems have been proposed to monitor wireless sensor networks (WSN). These systems may be active (causing a high degree of intrusion) or passive (low observability inside the nodes). This paper presents the implementation of an active hybrid (hardware and software) monitor with low intrusion. It is based on the addition to the sensor node of a monitor node (hardware part) which, through a standard interface, is able to receive the monitoring information sent by a piece of software executed in the sensor node. The intrusion on time, code, and energy caused in the sensor nodes by the monitor is evaluated as a function of data size and the interface used. Then different interfaces, commonly available in sensor nodes, are evaluated: serial transmission (USART), serial peripheral interface (SPI), and parallel. The proposed hybrid monitor provides highly detailed information, barely disturbed by the measurement tool (interference), about the behavior of the WSN that may be used to evaluate many properties such as performance, dependability, security, etc. Monitor nodes are self-powered and may be removed after the monitoring campaign to be reused in other campaigns and/or WSNs. No other hardware-independent monitoring platforms with such low interference have been found in the literature.

A Frequency-Based Approach to Intrusion Detection

Directory of Open Access Journals (Sweden)

Mian Zhou

2004-06-01

Full Text Available Research on network security and intrusion detection strategies presents many challenging issues to both theoreticians and practitioners. Hackers apply an array of intrusion and exploit techniques to cause disruption of normal system operations, but on the defense, firewalls and intrusion detection systems (IDS are typically only effective in defending known intrusion types using their signatures, and are far less than mature when faced with novel attacks. In this paper, we adapt the frequency analysis techniques such as the Discrete Fourier Transform (DFT used in signal processing to the design of intrusion detection algorithms. We demonstrate the effectiveness of the frequency-based detection strategy by running synthetic network intrusion data in simulated networks using the OPNET software. The simulation results indicate that the proposed intrusion detection strategy is effective in detecting anomalous traffic data that exhibit patterns over time, which include several types of DOS and probe attacks. The significance of this new strategy is that it does not depend on the prior knowledge of attack signatures, thus it has the potential to be a useful supplement to existing signature-based IDS and firewalls.

Multi-User Low Intrusive Occupancy Detection.

Science.gov (United States)

Pratama, Azkario Rizky; Widyawan, Widyawan; Lazovik, Alexander; Aiello, Marco

2018-03-06

Smart spaces are those that are aware of their state and can act accordingly. Among the central elements of such a state is the presence of humans and their number. For a smart office building, such information can be used for saving energy and safety purposes. While acquiring presence information is crucial, using sensing techniques that are highly intrusive, such as cameras, is often not acceptable for the building occupants. In this paper, we illustrate a proposal for occupancy detection which is low intrusive; it is based on equipment typically available in modern offices such as room-level power-metering and an app running on workers' mobile phones. For power metering, we collect the aggregated power consumption and disaggregate the load of each device. For the mobile phone, we use the Received Signal Strength (RSS) of BLE (Bluetooth Low Energy) nodes deployed around workspaces to localize the phone in a room. We test the system in our offices. The experiments show that sensor fusion of the two sensing modalities gives 87-90% accuracy, demonstrating the effectiveness of the proposed approach.

Multi-User Low Intrusive Occupancy Detection

Science.gov (United States)

Widyawan, Widyawan; Lazovik, Alexander

2018-01-01

Smart spaces are those that are aware of their state and can act accordingly. Among the central elements of such a state is the presence of humans and their number. For a smart office building, such information can be used for saving energy and safety purposes. While acquiring presence information is crucial, using sensing techniques that are highly intrusive, such as cameras, is often not acceptable for the building occupants. In this paper, we illustrate a proposal for occupancy detection which is low intrusive; it is based on equipment typically available in modern offices such as room-level power-metering and an app running on workers’ mobile phones. For power metering, we collect the aggregated power consumption and disaggregate the load of each device. For the mobile phone, we use the Received Signal Strength (RSS) of BLE (Bluetooth Low Energy) nodes deployed around workspaces to localize the phone in a room. We test the system in our offices. The experiments show that sensor fusion of the two sensing modalities gives 87–90% accuracy, demonstrating the effectiveness of the proposed approach. PMID:29509693

An intrusion detection system based on fiber hydrophone

Science.gov (United States)

Liu, Junrong; Qiu, Xiufen; Shen, Heping

2017-10-01

This paper provides a new intrusion detection system based on fiber hydrophone, focusing beam forming figure positioning according to the near field and high precision sound source location algorithm which can accurately position the intrusion; obtaining its behavior path , obtaining the intrusion events related information such as speed form tracking intrusion trace; And analyze identification the detected intrusion behavior. If the monitor area is larger, the algorithm will take too much time once, and influence the system response time, for reduce the calculating time. This paper provides way that coarse location first, and then scanned for accuracy, so as to realize the intrusion events (such as car, etc.) the remote monitoring of positioning. The system makes up the blank in process capture of the fiber optic intrusion detection technology, and improves the understanding of the invasion. Through the capture of the process of intrusion behavior, and the fusion detection of intrusion behavior itself, thus analysis, judgment, identification of the intrusion information can greatly reduce the rate of false positives, greatly improved the reliability and practicability of the perimeter security system.

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.

Research on IPv6 intrusion detection system Snort-based

Science.gov (United States)

Shen, Zihao; Wang, Hui

2010-07-01

This paper introduces the common intrusion detection technologies, discusses the work flow of Snort intrusion detection system, and analyzes IPv6 data packet encapsulation and protocol decoding technology. We propose the expanding Snort architecture to support IPv6 intrusion detection in accordance with CIDF standard combined with protocol analysis technology and pattern matching technology, and present its composition. The research indicates that the expanding Snort system can effectively detect various intrusion attacks; it is high in detection efficiency and detection accuracy and reduces false alarm and omission report, which effectively solves the problem of IPv6 intrusion detection.

Network Intrusion Forensic Analysis Using Intrusion Detection System

OpenAIRE

Manish Kumar; Dr. M. Hanumanthappa; Dr. T.V. Suresh Kumar

2011-01-01

The need for computer intrusion forensics arises from the alarming increase in the number of computer crimes that are committed annually. After a computer system has been breached and an intrusion has been detected, there is a need for a computer forensics investigation to follow. Computer forensics is used to bring to justice, those responsible for conducting attacks on computer systems throughout the world. Because of this the law must be follow precisely when conducting a forensics investi...

Multi-User Low Intrusive Occupancy Detection

Directory of Open Access Journals (Sweden)

Azkario Rizky Pratama

2018-03-01

Full Text Available Smart spaces are those that are aware of their state and can act accordingly. Among the central elements of such a state is the presence of humans and their number. For a smart office building, such information can be used for saving energy and safety purposes. While acquiring presence information is crucial, using sensing techniques that are highly intrusive, such as cameras, is often not acceptable for the building occupants. In this paper, we illustrate a proposal for occupancy detection which is low intrusive; it is based on equipment typically available in modern offices such as room-level power-metering and an app running on workers’ mobile phones. For power metering, we collect the aggregated power consumption and disaggregate the load of each device. For the mobile phone, we use the Received Signal Strength (RSS of BLE (Bluetooth Low Energy nodes deployed around workspaces to localize the phone in a room. We test the system in our offices. The experiments show that sensor fusion of the two sensing modalities gives 87–90% accuracy, demonstrating the effectiveness of the proposed approach.

Enhancing Trust Management for Wireless Intrusion Detection via Traffic Sampling in the Era of Big Data

DEFF Research Database (Denmark)

Meng, Weizhi; Li, Wenjuan; Su, Chunhua

2017-01-01

many kinds of information among sensors, whereas such network is vulnerable to a wide range of attacks, especially insider attacks, due to its natural environment and inherent unreliable transmission. To safeguard its security, intrusion detection systems (IDSs) are widely adopted in a WSN to defend...... against insider attacks through implementing proper trustbased mechanisms. However, in the era of big data, sensors may generate excessive information and data, which could degrade the effectiveness of trust computation. In this paper, we focus on this challenge and propose a way of combining Bayesian......-based trust management with traffic sampling for wireless intrusion detection under a hierarchical structure. In the evaluation, we investigate the performance of our approach in both a simulated and a real network environment. Experimental results demonstrate that packet-based trust management would become...

A Survey on Anomaly Based Host Intrusion Detection System

Science.gov (United States)

Jose, Shijoe; Malathi, D.; Reddy, Bharath; Jayaseeli, Dorathi

2018-04-01

An intrusion detection system (IDS) is hardware, software or a combination of two, for monitoring network or system activities to detect malicious signs. In computer security, designing a robust intrusion detection system is one of the most fundamental and important problems. The primary function of system is detecting intrusion and gives alerts when user tries to intrusion on timely manner. In these techniques when IDS find out intrusion it will send alert massage to the system administrator. Anomaly detection is an important problem that has been researched within diverse research areas and application domains. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. From the existing anomaly detection techniques, each technique has relative strengths and weaknesses. The current state of the experiment practice in the field of anomaly-based intrusion detection is reviewed and survey recent studies in this. This survey provides a study of existing anomaly detection techniques, and how the techniques used in one area can be applied in another application domain.

Evidential reasoning research on intrusion detection

Science.gov (United States)

Wang, Xianpei; Xu, Hua; Zheng, Sheng; Cheng, Anyu

2003-09-01

In this paper, we mainly aim at D-S theory of evidence and the network intrusion detection these two fields. It discusses the method how to apply this probable reasoning as an AI technology to the Intrusion Detection System (IDS). This paper establishes the application model, describes the new mechanism of reasoning and decision-making and analyses how to implement the model based on the synscan activities detection on the network. The results suggest that if only rational probability values were assigned at the beginning, the engine can, according to the rules of evidence combination and hierarchical reasoning, compute the values of belief and finally inform the administrators of the qualities of the traced activities -- intrusions, normal activities or abnormal activities.

The state of the art in intrusion prevention and detection

CERN Document Server

Pathan, Al-Sakib Khan

2013-01-01

The State of the Art in Intrusion Prevention and Detection analyzes the latest trends and issues surrounding intrusion detection systems in computer networks, especially in communications networks. Its broad scope of coverage includes wired, wireless, and mobile networks; next-generation converged networks; and intrusion in social networks.Presenting cutting-edge research, the book presents novel schemes for intrusion detection and prevention. It discusses tracing back mobile attackers, secure routing with intrusion prevention, anomaly detection, and AI-based techniques. It also includes infor

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.

Autonomous Rule Creation for Intrusion Detection

Energy Technology Data Exchange (ETDEWEB)

Todd Vollmer; Jim Alves-Foss; Milos Manic

2011-04-01

Many computational intelligence techniques for anomaly based network intrusion detection can be found in literature. Translating a newly discovered intrusion recognition criteria into a distributable rule can be a human intensive effort. This paper explores a multi-modal genetic algorithm solution for autonomous rule creation. This algorithm focuses on the process of creating rules once an intrusion has been identified, rather than the evolution of rules to provide a solution for intrusion detection. The algorithm was demonstrated on anomalous ICMP network packets (input) and Snort rules (output of the algorithm). Output rules were sorted according to a fitness value and any duplicates were removed. The experimental results on ten test cases demonstrated a 100 percent rule alert rate. Out of 33,804 test packets 3 produced false positives. Each test case produced a minimum of three rule variations that could be used as candidates for a production system.

Security Enrichment in Intrusion Detection System Using Classifier Ensemble

Directory of Open Access Journals (Sweden)

Uma R. Salunkhe

2017-01-01

Full Text Available In the era of Internet and with increasing number of people as its end users, a large number of attack categories are introduced daily. Hence, effective detection of various attacks with the help of Intrusion Detection Systems is an emerging trend in research these days. Existing studies show effectiveness of machine learning approaches in handling Intrusion Detection Systems. In this work, we aim to enhance detection rate of Intrusion Detection System by using machine learning technique. We propose a novel classifier ensemble based IDS that is constructed using hybrid approach which combines data level and feature level approach. Classifier ensembles combine the opinions of different experts and improve the intrusion detection rate. Experimental results show the improved detection rates of our system compared to reference technique.

A comparative performance evaluation of intrusion detection techniques for hierarchical wireless sensor networks

Directory of Open Access Journals (Sweden)

H.H. Soliman

2012-11-01

Full Text Available An explosive growth in the field of wireless sensor networks (WSNs has been achieved in the past few years. Due to its important wide range of applications especially military applications, environments monitoring, health care application, home automation, etc., they are exposed to security threats. Intrusion detection system (IDS is one of the major and efficient defensive methods against attacks in WSN. Therefore, developing IDS for WSN have attracted much attention recently and thus, there are many publications proposing new IDS techniques or enhancement to the existing ones. This paper evaluates and compares the most prominent anomaly-based IDS systems for hierarchical WSNs and identifying their strengths and weaknesses. For each IDS, the architecture and the related functionality are briefly introduced, discussed, and compared, focusing on both the operational strengths and weakness. In addition, a comparison of the studied IDSs is carried out using a set of critical evaluation metrics that are divided into two groups; the first one related to performance and the second related to security. Finally based on the carried evaluation and comparison, a set of design principles are concluded, which have to be addressed and satisfied in future research of designing and implementing IDS for WSNs.

Unique Challenges in WiFi Intrusion Detection

OpenAIRE

Milliken, Jonny

2014-01-01

The Intrusion Detection System (IDS) is a common means of protecting networked systems from attack or malicious misuse. The deployment of an IDS can take many different forms dependent on protocols, usage and cost. This is particularly true of Wireless Intrusion Detection Systems (WIDS) which have many detection challenges associated with data transmission through an open, shared medium, facilitated by fundamental changes at the Physical and MAC layers. WIDS need to be considered in more deta...

Semantic intrusion detection with multisensor data fusion using ...

Indian Academy of Sciences (India)

spatiotemporal relations to form complex events which model the intrusion patterns. ... Wireless sensor networks; complex event processing; event stream; ...... of the 2006 ACM SIGMOD International Conference on Management of Data, 407– ...

Intrusion Detection System In IoT

OpenAIRE

Nygaard, Frederik

2017-01-01

Intrusion detection detects misbehaving nodes in a network. In Internet of Things(IoT), IPv6 Routing for Low-Power and Lossy Networks (RPL) is the standard routing protocol. In IoT, devices commonly have low energy, storage and memory, which is why the implemented intrusion algorithm in this thesis will try to minimize the usage of these resources. IDS for RPL-networks have been implemented before, but the use of resources or the number of packets sent was too high to be successful when findi...

When Intrusion Detection Meets Blockchain Technology: A Review

DEFF Research Database (Denmark)

Meng, Weizhi; Tischhauser, Elmar Wolfgang; Wang, Qingju

2018-01-01

developed, which allow IDS nodes to exchange data with each other. However, data and trust management still remain two challenges for current detection architectures, which may degrade the effectiveness of such detection systems. In recent years, blockchain technology has shown its adaptability in many...... fields such as supply chain management, international payment, interbanking and so on. As blockchain can protect the integrity of data storage and ensure process transparency, it has a potential to be applied to intrusion detection domain. Motivated by this, this work provides a review regarding...... the intersection of IDSs and blockchains. In particular, we introduce the background of intrusion detection and blockchain, discuss the applicability of blockchain to intrusion detection, and identify open challenges in this direction....

When Intrusion Detection Meets Blockchain Technology: A Review

OpenAIRE

Meng, Weizhi; Tischhauser, Elmar Wolfgang; Wang, Qingju; Wang, Yu; Han, Jinguang

2018-01-01

With the purpose of identifying cyber threats and possible incidents, intrusion detection systems (IDSs) are widely deployed in various computer networks. In order to enhance the detection capability of a single IDS, collaborative intrusion detection networks (or collaborative IDSs) have been developed, which allow IDS nodes to exchange data with each other. However, data and trust management still remain two challenges for current detection architectures, which may degrade the effectiveness ...

Anomaly-based intrusion detection for SCADA systems

International Nuclear Information System (INIS)

Yang, D.; Usynin, A.; Hines, J. W.

2006-01-01

Most critical infrastructure such as chemical processing plants, electrical generation and distribution networks, and gas distribution is monitored and controlled by Supervisory Control and Data Acquisition Systems (SCADA. These systems have been the focus of increased security and there are concerns that they could be the target of international terrorists. With the constantly growing number of internet related computer attacks, there is evidence that our critical infrastructure may also be vulnerable. Researchers estimate that malicious online actions may cause $75 billion at 2007. One of the interesting countermeasures for enhancing information system security is called intrusion detection. This paper will briefly discuss the history of research in intrusion detection techniques and introduce the two basic detection approaches: signature detection and anomaly detection. Finally, it presents the application of techniques developed for monitoring critical process systems, such as nuclear power plants, to anomaly intrusion detection. The method uses an auto-associative kernel regression (AAKR) model coupled with the statistical probability ratio test (SPRT) and applied to a simulated SCADA system. The results show that these methods can be generally used to detect a variety of common attacks. (authors)

Data Fusion for Network Intrusion Detection: A Review

Directory of Open Access Journals (Sweden)

Guoquan Li

2018-01-01

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

Online Adaboost-Based Parameterized Methods for Dynamic Distributed Network Intrusion Detection.

Science.gov (United States)

Hu, Weiming; Gao, Jun; Wang, Yanguo; Wu, Ou; Maybank, Stephen

2014-01-01

Current network intrusion detection systems lack adaptability to the frequently changing network environments. Furthermore, intrusion detection in the new distributed architectures is now a major requirement. In this paper, we propose two online Adaboost-based intrusion detection algorithms. In the first algorithm, a traditional online Adaboost process is used where decision stumps are used as weak classifiers. In the second algorithm, an improved online Adaboost process is proposed, and online Gaussian mixture models (GMMs) are used as weak classifiers. We further propose a distributed intrusion detection framework, in which a local parameterized detection model is constructed in each node using the online Adaboost algorithm. A global detection model is constructed in each node by combining the local parametric models using a small number of samples in the node. This combination is achieved using an algorithm based on particle swarm optimization (PSO) and support vector machines. The global model in each node is used to detect intrusions. Experimental results show that the improved online Adaboost process with GMMs obtains a higher detection rate and a lower false alarm rate than the traditional online Adaboost process that uses decision stumps. Both the algorithms outperform existing intrusion detection algorithms. It is also shown that our PSO, and SVM-based algorithm effectively combines the local detection models into the global model in each node; the global model in a node can handle the intrusion types that are found in other nodes, without sharing the samples of these intrusion types.

Design of an Acoustic Target Intrusion Detection System Based on Small-Aperture Microphone Array

Science.gov (United States)

Zu, Xingshui; Guo, Feng; Huang, Jingchang; Zhao, Qin; Liu, Huawei; Li, Baoqing; Yuan, Xiaobing

2017-01-01

Automated surveillance of remote locations in a wireless sensor network is dominated by the detection algorithm because actual intrusions in such locations are a rare event. Therefore, a detection method with low power consumption is crucial for persistent surveillance to ensure longevity of the sensor networks. A simple and effective two-stage algorithm composed of energy detector (ED) and delay detector (DD) with all its operations in time-domain using small-aperture microphone array (SAMA) is proposed. The algorithm analyzes the quite different velocities between wind noise and sound waves to improve the detection capability of ED in the surveillance area. Experiments in four different fields with three types of vehicles show that the algorithm is robust to wind noise and the probability of detection and false alarm are 96.67% and 2.857%, respectively. PMID:28273838

Intrusion Detection amp Prevention Systems - Sourcefire Snort

Directory of Open Access Journals (Sweden)

Rajesh Vuppala

2015-08-01

Full Text Available Information security is a challenging issue for all business organizations today amidst increasing cyber threats. While there are many alternative intrusion detection amp prevention systems available to choose from selecting the best solution to implement to detect amp prevent cyber-attacks is a difficult task. The best solution is of the one that gets the best reviews and suits the organizations needs amp budget. In this review paper we summarize various classes of intrusion detection and prevention systems compare features of alternative solutions and make recommendation for implementation of one as the best solution for business organization in Fiji.

Medication Adherence using Non-intrusive Wearable Sensors

Directory of Open Access Journals (Sweden)

T. H. Lim

2017-12-01

Full Text Available Activity recognition approaches have been applied in home ambient systems to monitor the status and well- being of occupant especially for home care systems. With the advancement of embedded wireless sensing devices, various applications have been proposed to monitor user´s activities and maintain a healthy lifestyle. In this paper, we propose and evaluate a Smart Medication Alert and Treatment Electronic Systems (SmartMATES using a non-intrusive wearable activity recognition sensing system to monitor and alert an user for missing medication prescription. Two sensors are used to collect data from the accelerometer and radio transceiver. Based on the data collected, SmartMATES processes the data and generate a model for the various actions including taking medication. We have evaluated the SmartMATES on 9 participants. The results show that the SmartMATES can identify and prevent missing dosage in a less intrusive way than existing mobile application and traditional approaches.

Ultrasonic intrusion sensor using the Doppler effect; Choonpa Doppler hoshiki shinnyu sensor

Energy Technology Data Exchange (ETDEWEB)

Kani, H; Iwasaki, N; Goto, M [Nippon Soken, Inc., Tokyo (Japan); Tsuzuki, T; Nakamura, T [Denso Corp., Aichi (Japan)

1997-10-01

For vehicle anti-theft alarm systems which cope with vehicle and car component theft, EU initiated vehicle security regulations from Jan 1997. Also, the insurance industry has instituted the insurance certification of vehicle anti-theft alarm systems. We have developed an ultrasonic intrusion sensor using the doppler effect for vehicle anti-theft alarm systems specifically for these EU regulations and insurance certification. 2 refs., 7 figs., 1 tab.

Data Mining for Intrusion Detection

Science.gov (United States)

Singhal, Anoop; Jajodia, Sushil

Data Mining Techniques have been successfully applied in many different fields including marketing, manufacturing, fraud detection and network management. Over the past years there is a lot of interest in security technologies such as intrusion detection, cryptography, authentication and firewalls. This chapter discusses the application of Data Mining techniques to computer security. Conclusions are drawn and directions for future research are suggested.

Resilient Control and Intrusion Detection for SCADA Systems

Science.gov (United States)

2014-05-01

Lowe. The myths and facts behind cyber security risks for industrial control systems . VDE Congress, 2004. [45] I. S. C37.1-1994. Ieee standard...Resilient Control and Intrusion Detection for SCADA Systems Bonnie Xia Zhu Electrical Engineering and Computer Sciences University of California at...3. DATES COVERED 00-00-2014 to 00-00-2014 4. TITLE AND SUBTITLE Resilient Control and Intrusion Detection for SCADA Systems 5a. CONTRACT

An evaluation of classification algorithms for intrusion detection ...

African Journals Online (AJOL)

An evaluation of classification algorithms for intrusion detection. ... Log in or Register to get access to full text downloads. ... Most of the available IDSs use all the 41 features in the network to evaluate and search for intrusive pattern in which ...

Railway clearance intrusion detection method with binocular stereo vision

Science.gov (United States)

Zhou, Xingfang; Guo, Baoqing; Wei, Wei

2018-03-01

In the stage of railway construction and operation, objects intruding railway clearance greatly threaten the safety of railway operation. Real-time intrusion detection is of great importance. For the shortcomings of depth insensitive and shadow interference of single image method, an intrusion detection method with binocular stereo vision is proposed to reconstruct the 3D scene for locating the objects and judging clearance intrusion. The binocular cameras are calibrated with Zhang Zhengyou's method. In order to improve the 3D reconstruction speed, a suspicious region is firstly determined by background difference method of a single camera's image sequences. The image rectification, stereo matching and 3D reconstruction process are only executed when there is a suspicious region. A transformation matrix from Camera Coordinate System(CCS) to Track Coordinate System(TCS) is computed with gauge constant and used to transfer the 3D point clouds into the TCS, then the 3D point clouds are used to calculate the object position and intrusion in TCS. The experiments in railway scene show that the position precision is better than 10mm. It is an effective way for clearance intrusion detection and can satisfy the requirement of railway application.

Data mining approach to web application intrusions detection

Science.gov (United States)

Kalicki, Arkadiusz

2011-10-01

Web applications became most popular medium in the Internet. Popularity, easiness of web application script languages and frameworks together with careless development results in high number of web application vulnerabilities and high number of attacks performed. There are several types of attacks possible because of improper input validation: SQL injection Cross-site scripting, Cross-Site Request Forgery (CSRF), web spam in blogs and others. In order to secure web applications intrusion detection (IDS) and intrusion prevention systems (IPS) are being used. Intrusion detection systems are divided in two groups: misuse detection (traditional IDS) and anomaly detection. This paper presents data mining based algorithm for anomaly detection. The principle of this method is the comparison of the incoming HTTP traffic with a previously built profile that contains a representation of the "normal" or expected web application usage sequence patterns. The frequent sequence patterns are found with GSP algorithm. Previously presented detection method was rewritten and improved. Some tests show that the software catches malicious requests, especially long attack sequences, results quite good with medium length sequences, for short length sequences must be complemented with other methods.

A framework for implementing a Distributed Intrusion Detection System (DIDS) with interoperabilty and information analysis

OpenAIRE

Davicino, Pablo; Echaiz, Javier; Ardenghi, Jorge Raúl

2011-01-01

Computer Intrusion Detection Systems (IDS) are primarily designed to protect availability, condentiality and integrity of critical information infrastructures. A Distributed IDS (DIDS) consists of several IDS over a large network(s), all of which communicate with each other, with a central server or with a cluster of servers that facilitates advanced network monitoring. In a distributed environment, DIDS are implemented using cooperative intelligent sensors distributed across the network(s). ...

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.

Intelligent Agent-Based Intrusion Detection System Using Enhanced Multiclass SVM

Science.gov (United States)

Ganapathy, S.; Yogesh, P.; Kannan, A.

2012-01-01

Intrusion detection systems were used in the past along with various techniques to detect intrusions in networks effectively. However, most of these systems are able to detect the intruders only with high false alarm rate. In this paper, we propose a new intelligent agent-based intrusion detection model for mobile ad hoc networks using a combination of attribute selection, outlier detection, and enhanced multiclass SVM classification methods. For this purpose, an effective preprocessing technique is proposed that improves the detection accuracy and reduces the processing time. Moreover, two new algorithms, namely, an Intelligent Agent Weighted Distance Outlier Detection algorithm and an Intelligent Agent-based Enhanced Multiclass Support Vector Machine algorithm are proposed for detecting the intruders in a distributed database environment that uses intelligent agents for trust management and coordination in transaction processing. The experimental results of the proposed model show that this system detects anomalies with low false alarm rate and high-detection rate when tested with KDD Cup 99 data set. PMID:23056036

On-line detection of Escherichia coli intrusion in a pilot-scale drinking water distribution system.

Science.gov (United States)

Ikonen, Jenni; Pitkänen, Tarja; Kosse, Pascal; Ciszek, Robert; Kolehmainen, Mikko; Miettinen, Ilkka T

2017-08-01

Improvements in microbial drinking water quality monitoring are needed for the better control of drinking water distribution systems and for public health protection. Conventional water quality monitoring programmes are not always able to detect a microbial contamination of drinking water. In the drinking water production chain, in addition to the vulnerability of source waters, the distribution networks are prone to contamination. In this study, a pilot-scale drinking-water distribution network with an on-line monitoring system was utilized for detecting bacterial intrusion. During the experimental Escherichia coli intrusions, the contaminant was measured by applying a set of on-line sensors for electric conductivity (EC), pH, temperature (T), turbidity, UV-absorbance at 254 nm (UVAS SC) and with a device for particle counting. Monitored parameters were compared with the measured E. coli counts using the integral calculations of the detected peaks. EC measurement gave the strongest signal compared with the measured baseline during the E. coli intrusion. Integral calculations showed that the peaks in the EC, pH, T, turbidity and UVAS SC data were detected corresponding to the time predicted. However, the pH and temperature peaks detected were barely above the measured baseline and could easily be mixed with the background noise. The results indicate that on-line monitoring can be utilized for the rapid detection of microbial contaminants in the drinking water distribution system although the peak interpretation has to be performed carefully to avoid being mixed up with normal variations in the measurement data. Copyright © 2017 Elsevier Ltd. All rights reserved.

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.

On emulation-based network intrusion detection systems

NARCIS (Netherlands)

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

2014-01-01

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

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.

Anomaly-based online intrusion detection system as a sensor for cyber security situational awareness system

OpenAIRE

Kokkonen, Tero

2016-01-01

Almost all the organisations and even individuals rely on complex structures of data networks and networked computer systems. That complex data ensemble, the cyber domain, provides great opportunities, but at the same time it offers many possible attack vectors that can be abused for cyber vandalism, cyber crime, cyber espionage or cyber terrorism. Those threats produce requirements for cyber security situational awareness and intrusion detection capability. This dissertation conc...

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

Computational neural network regression model for Host based Intrusion Detection System

Directory of Open Access Journals (Sweden)

Sunil Kumar Gautam

2016-09-01

Full Text Available The current scenario of information gathering and storing in secure system is a challenging task due to increasing cyber-attacks. There exists computational neural network techniques designed for intrusion detection system, which provide security to single machine and entire network's machine. In this paper, we have used two types of computational neural network models, namely, Generalized Regression Neural Network (GRNN model and Multilayer Perceptron Neural Network (MPNN model for Host based Intrusion Detection System using log files that are generated by a single personal computer. The simulation results show correctly classified percentage of normal and abnormal (intrusion class using confusion matrix. On the basis of results and discussion, we found that the Host based Intrusion Systems Model (HISM significantly improved the detection accuracy while retaining minimum false alarm rate.

NIST Special Publication on Intrusion Detection Systems

National Research Council Canada - National Science Library

Bace, Rebecca Gurley

2001-01-01

Intrusion detection systems (IDSs) are software or hardware systems that automate the process of monitoring the events occurring in a computer system or network, analyzing them for signs of security problems...

Intrusion detection in Mobile Ad-hoc Networks: Bayesian game formulation

Directory of Open Access Journals (Sweden)

Basant Subba

2016-06-01

Full Text Available Present Intrusion Detection Systems (IDSs for MANETs require continuous monitoring which leads to rapid depletion of a node's battery life. To address this issue, we propose a new IDS scheme comprising a novel cluster leader election process and a hybrid IDS. The cluster leader election process uses the Vickrey–Clarke–Groves mechanism to elect the cluster leader which provides the intrusion detection service. The hybrid IDS comprises a threshold based lightweight module and a powerful anomaly based heavyweight module. Initially, only the lightweight module is activated. The decision to activate the heavyweight module is taken by modeling the intrusion detection process as an incomplete information non-cooperative game between the elected leader node and the potential malicious node. Simulation results show that the proposed scheme significantly reduces the IDS traffic and overall power consumption in addition to maintaining a high detection rate and accuracy.

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.

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.

An Adaptive Database Intrusion Detection System

Science.gov (United States)

Barrios, Rita M.

2011-01-01

Intrusion detection is difficult to accomplish when attempting to employ current methodologies when considering the database and the authorized entity. It is a common understanding that current methodologies focus on the network architecture rather than the database, which is not an adequate solution when considering the insider threat. Recent…

Multilayer Statistical Intrusion Detection in Wireless Networks

Science.gov (United States)

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

2008-12-01

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

Apriori-based network intrusion detection system

International Nuclear Information System (INIS)

Wang Wenjin; Liu Junrong; Liu Baoxu

2012-01-01

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

Approaches in anomaly-based network intrusion detection systems

NARCIS (Netherlands)

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

2008-01-01

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

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

Power-Aware Intrusion Detection in Mobile Ad Hoc Networks

Science.gov (United States)

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

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

Alerts Visualization and Clustering in Network-based Intrusion Detection

Energy Technology Data Exchange (ETDEWEB)

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

2010-04-01

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

An Automata Based Intrusion Detection Method for Internet of Things

Directory of Open Access Journals (Sweden)

Yulong Fu

2017-01-01

Full Text Available Internet of Things (IoT transforms network communication to Machine-to-Machine (M2M basis and provides open access and new services to citizens and companies. It extends the border of Internet and will be developed as one part of the future 5G networks. However, as the resources of IoT’s front devices are constrained, many security mechanisms are hard to be implemented to protect the IoT networks. Intrusion detection system (IDS is an efficient technique that can be used to detect the attackers when cryptography is broken, and it can be used to enforce the security of IoT networks. In this article, we analyzed the intrusion detection requirements of IoT networks and then proposed a uniform intrusion detection method for the vast heterogeneous IoT networks based on an automata model. The proposed method can detect and report the possible IoT attacks with three types: jam-attack, false-attack, and reply-attack automatically. We also design an experiment to verify the proposed IDS method and examine the attack of RADIUS application.

Exploring machine-learning-based control plane intrusion detection techniques in software defined optical networks

Science.gov (United States)

Zhang, Huibin; Wang, Yuqiao; Chen, Haoran; Zhao, Yongli; Zhang, Jie

2017-12-01

In software defined optical networks (SDON), the centralized control plane may encounter numerous intrusion threatens which compromise the security level of provisioned services. In this paper, the issue of control plane security is studied and two machine-learning-based control plane intrusion detection techniques are proposed for SDON with properly selected features such as bandwidth, route length, etc. We validate the feasibility and efficiency of the proposed techniques by simulations. Results show an accuracy of 83% for intrusion detection can be achieved with the proposed machine-learning-based control plane intrusion detection techniques.

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.

Intrusion detection techniques for plant-wide network in a nuclear power plant

International Nuclear Information System (INIS)

Rajasekhar, P.; Shrikhande, S.V.; Biswas, B.B.; Patil, R.K.

2012-01-01

Nuclear power plants have a lot of critical data to be sent to the operator workstations. A plant wide integrated communication network, with high throughput, determinism and redundancy, is required between the workstations and the field. Switched Ethernet network is a promising prospect for such an integrated communication network. But for such an integrated system, intrusion is a major issue. Hence the network should have an intrusion detection system to make the network data secure and enhance the network availability. Intrusion detection is the process of monitoring the events occurring in a network and analyzing them for signs of possible incidents, which are violations or imminent threats of violation of network security policies, acceptable user policies, or standard security practices. This paper states the various intrusion detection techniques and approaches which are applicable for analysis of a plant wide network. (author)

Abstracting massive data for lightweight intrusion detection in computer networks

KAUST Repository

Wang, Wei

2016-10-15

Anomaly intrusion detection in big data environments calls for lightweight models that are able to achieve real-time performance during detection. Abstracting audit data provides a solution to improve the efficiency of data processing in intrusion detection. Data abstraction refers to abstract or extract the most relevant information from the massive dataset. In this work, we propose three strategies of data abstraction, namely, exemplar extraction, attribute selection and attribute abstraction. We first propose an effective method called exemplar extraction to extract representative subsets from the original massive data prior to building the detection models. Two clustering algorithms, Affinity Propagation (AP) and traditional . k-means, are employed to find the exemplars from the audit data. . k-Nearest Neighbor (k-NN), Principal Component Analysis (PCA) and one-class Support Vector Machine (SVM) are used for the detection. We then employ another two strategies, attribute selection and attribute extraction, to abstract audit data for anomaly intrusion detection. Two http streams collected from a real computing environment as well as the KDD\\'99 benchmark data set are used to validate these three strategies of data abstraction. The comprehensive experimental results show that while all the three strategies improve the detection efficiency, the AP-based exemplar extraction achieves the best performance of data abstraction.

Access Control from an Intrusion Detection Perspective

NARCIS (Netherlands)

Nunes Leal Franqueira, V.

Access control and intrusion detection are essential components for securing an organization's information assets. In practice, these components are used in isolation, while their fusion would contribute to increase the range and accuracy of both. One approach to accomplish this fusion is the

How Intrusion Detection Can Improve Software Decoy Applications

National Research Council Canada - National Science Library

Monteiro, Valter

2003-01-01

This research concerns information security and computer-network defense. It addresses how to handle the information of log files and intrusion-detection systems to recognize when a system is under attack...

Technologies, Methodologies and Challenges in Network Intrusion Detection and Prevention Systems

Directory of Open Access Journals (Sweden)

Nicoleta STANCIU

2013-01-01

Full Text Available This paper presents an overview of the technologies and the methodologies used in Network Intrusion Detection and Prevention Systems (NIDPS. Intrusion Detection and Prevention System (IDPS technologies are differentiated by types of events that IDPSs can recognize, by types of devices that IDPSs monitor and by activity. NIDPSs monitor and analyze the streams of network packets in order to detect security incidents. The main methodology used by NIDPSs is protocol analysis. Protocol analysis requires good knowledge of the theory of the main protocols, their definition, how each protocol works.

Context-aware local Intrusion Detection in SCADA systems : a testbed and two showcases

NARCIS (Netherlands)

Chromik, Justyna Joanna; Haverkort, Boudewijn R.H.M.; Remke, Anne Katharina Ingrid; Pilch, Carina; Brackmann, Pascal; Duhme, Christof; Everinghoff, Franziska; Giberlein, Artur; Teodorowicz, Thomas; Wieland, Julian

2017-01-01

This paper illustrates the use of a testbed that we have developed for context-aware local intrusion detection. This testbed is based on the co-simulation framework Mosaik and allows for the validation of local intrusion detection mechanisms at field stations in power distribution networks. For two

Ant colony induced decision trees for intrusion detection

CSIR Research Space (South Africa)

Botes, FH

2017-06-01

Full Text Available platform. Intrusion Detection Systems (IDS) analyse network traffic to identify suspicious patterns with the intention to compromise the system. Practitioners train classifiers to classify the data within different categories e.g. malicious or normal...

Misuse and intrusion detection at Los Alamos National Laboratory

Energy Technology Data Exchange (ETDEWEB)

Jackson, K.A.; Neuman, M.C.; Simmonds, D.D.; Stallings, C.A.; Thompson, J.L.; Christoph, G.G.

1995-04-01

An effective method for detecting computer misuse is the automatic auditing and analysis of on-line user activity. This activity is reflected in system audit records, in system vulnerability postures, and in other evidence found through active system testing. Since 1989 we have implemented a misuse and intrusion detection system at Los Alamos. This is the Network Anomaly Detection and Intrusion Reporter, or NADIR. NADIR currently audits a Kerberos distributed authentication system, file activity on a mass, storage system, and four Cray supercomputers that run the UNICOS operating system. NADIR summarizes user activity and system configuration in statistical profiles. It compares these profiles to expert rules that define security policy and improper or suspicious behavior. It reports suspicious behavior to security auditors and provides tools to aid in follow-up investigations, As NADIR is constantly evolving, this paper reports its development to date.

Boosting Web Intrusion Detection Systems by Inferring Positive Signatures

NARCIS (Netherlands)

Bolzoni, D.; Etalle, Sandro

2008-01-01

We present a new approach to anomaly-based network intrusion detection for web applications. This approach is based on dividing the input parameters of the monitored web application in two groups: the "regular" and the "irregular" ones, and applying a new method for anomaly detection on the

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.

A Novel Algorithm for Intrusion Detection Based on RASL Model Checking

Directory of Open Access Journals (Sweden)

Weijun Zhu

2013-01-01

Full Text Available The interval temporal logic (ITL model checking (MC technique enhances the power of intrusion detection systems (IDSs to detect concurrent attacks due to the strong expressive power of ITL. However, an ITL formula suffers from difficulty in the description of the time constraints between different actions in the same attack. To address this problem, we formalize a novel real-time interval temporal logic—real-time attack signature logic (RASL. Based on such a new logic, we put forward a RASL model checking algorithm. Furthermore, we use RASL formulas to describe attack signatures and employ discrete timed automata to create an audit log. As a result, RASL model checking algorithm can be used to automatically verify whether the automata satisfy the formulas, that is, whether the audit log coincides with the attack signatures. The simulation experiments show that the new approach effectively enhances the detection power of the MC-based intrusion detection methods for a number of telnet attacks, p-trace attacks, and the other sixteen types of attacks. And these experiments indicate that the new algorithm can find several types of real-time attacks, whereas the existing MC-based intrusion detection approaches cannot do that.

Research on intrusion detection based on Kohonen network and support vector machine

Science.gov (United States)

Shuai, Chunyan; Yang, Hengcheng; Gong, Zeweiyi

2018-05-01

In view of the problem of low detection accuracy and the long detection time of support vector machine, which directly applied to the network intrusion detection system. Optimization of SVM parameters can greatly improve the detection accuracy, but it can not be applied to high-speed network because of the long detection time. a method based on Kohonen neural network feature selection is proposed to reduce the optimization time of support vector machine parameters. Firstly, this paper is to calculate the weights of the KDD99 network intrusion data by Kohonen network and select feature by weight. Then, after the feature selection is completed, genetic algorithm (GA) and grid search method are used for parameter optimization to find the appropriate parameters and classify them by support vector machines. By comparing experiments, it is concluded that feature selection can reduce the time of parameter optimization, which has little influence on the accuracy of classification. The experiments suggest that the support vector machine can be used in the network intrusion detection system and reduce the missing rate.

Robust site security using smart seismic array technology and multi-sensor data fusion

Science.gov (United States)

Hellickson, Dean; Richards, Paul; Reynolds, Zane; Keener, Joshua

2010-04-01

Traditional site security systems are susceptible to high individual sensor nuisance alarm rates that reduce the overall system effectiveness. Visual assessment of intrusions can be intensive and manually difficult as cameras are slewed by the system to non intrusion areas or as operators respond to nuisance alarms. Very little system intrusion performance data are available other than discrete sensor alarm indications that provide no real value. This paper discusses the system architecture, integration and display of a multi-sensor data fused system for wide area surveillance, local site intrusion detection and intrusion classification. The incorporation of a novel seismic array of smart sensors using FK Beamforming processing that greatly enhances the overall system detection and classification performance of the system is discussed. Recent test data demonstrates the performance of the seismic array within several different installations and its ability to classify and track moving targets at significant standoff distances with exceptional immunity to background clutter and noise. Multi-sensor data fusion is applied across a suite of complimentary sensors eliminating almost all nuisance alarms while integrating within a geographical information system to feed a visual-fusion display of the area being secured. Real-time sensor detection and intrusion classification data is presented within a visual-fusion display providing greatly enhanced situational awareness, system performance information and real-time assessment of intrusions and situations of interest with limited security operator involvement. This approach scales from a small local perimeter to very large geographical area and can be used across multiple sites controlled at a single command and control station.

Adaptive intrusion data system

International Nuclear Information System (INIS)

Johnson, C.S.

1976-01-01

An Adaptive Intrusion Data System (AIDS) was developed to collect data from intrusion alarm sensors as part of an evaluation system to improve sensor performance. AIDS is a unique digital data compression, storage, and formatting system. It also incorporates capability for video selection and recording for assessment of the sensors monitored by the system. The system is software reprogrammable to numerous configurations that may be utilized for the collection of environmental, bi-level, analog and video data. The output of the system is digital tapes formatted for direct data reduction on a CDC 6400 computer, and video tapes containing timed tagged information that can be correlated with the digital data

A Privacy-Preserving Framework for Collaborative Intrusion Detection Networks Through Fog Computing

DEFF Research Database (Denmark)

Wang, Yu; Xie, Lin; Li, Wenjuan

2017-01-01

Nowadays, cyber threats (e.g., intrusions) are distributed across various networks with the dispersed networking resources. Intrusion detection systems (IDSs) have already become an essential solution to defend against a large amount of attacks. With the development of cloud computing, a modern IDS...

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.

Automatic Extraction and Coordination of Audit Data and Features for Intrusion and Damage Assessment

National Research Council Canada - National Science Library

Ye, Nong

2006-01-01

.... We create a new attack-norm separation approach to developing detection models for building cyber sensors monitoring and identifying intrusion data characteristics at various points along the path...

Implementing an Intrusion Detection System in the Mysea Architecture

National Research Council Canada - National Science Library

Tenhunen, Thomas

2008-01-01

.... The objective of this thesis is to design an intrusion detection system (IDS) architecture that permits administrators operating on MYSEA client machines to conveniently view and analyze IDS alerts from the single level networks...

Improving Accuracy of Intrusion Detection Model Using PCA and optimized SVM

Directory of Open Access Journals (Sweden)

Sumaiya Thaseen Ikram

2016-06-01

Full Text Available Intrusion detection is very essential for providing security to different network domains and is mostly used for locating and tracing the intruders. There are many problems with traditional intrusion detection models (IDS such as low detection capability against unknown network attack, high false alarm rate and insufficient analysis capability. Hence the major scope of the research in this domain is to develop an intrusion detection model with improved accuracy and reduced training time. This paper proposes a hybrid intrusiondetection model by integrating the principal component analysis (PCA and support vector machine (SVM. The novelty of the paper is the optimization of kernel parameters of the SVM classifier using automatic parameter selection technique. This technique optimizes the punishment factor (C and kernel parameter gamma (γ, thereby improving the accuracy of the classifier and reducing the training and testing time. The experimental results obtained on the NSL KDD and gurekddcup dataset show that the proposed technique performs better with higher accuracy, faster convergence speed and better generalization. Minimum resources are consumed as the classifier input requires reduced feature set for optimum classification. A comparative analysis of hybrid models with the proposed model is also performed.

Hybrid feature selection for supporting lightweight intrusion detection systems

Science.gov (United States)

Song, Jianglong; Zhao, Wentao; Liu, Qiang; Wang, Xin

2017-08-01

Redundant and irrelevant features not only cause high resource consumption but also degrade the performance of Intrusion Detection Systems (IDS), especially when coping with big data. These features slow down the process of training and testing in network traffic classification. Therefore, a hybrid feature selection approach in combination with wrapper and filter selection is designed in this paper to build a lightweight intrusion detection system. Two main phases are involved in this method. The first phase conducts a preliminary search for an optimal subset of features, in which the chi-square feature selection is utilized. The selected set of features from the previous phase is further refined in the second phase in a wrapper manner, in which the Random Forest(RF) is used to guide the selection process and retain an optimized set of features. After that, we build an RF-based detection model and make a fair comparison with other approaches. The experimental results on NSL-KDD datasets show that our approach results are in higher detection accuracy as well as faster training and testing processes.

A Hybrid Swarm Intelligence Algorithm for Intrusion Detection Using Significant Features

Directory of Open Access Journals (Sweden)

P. Amudha

2015-01-01

Full Text Available Intrusion detection has become a main part of network security due to the huge number of attacks which affects the computers. This is due to the extensive growth of internet connectivity and accessibility to information systems worldwide. To deal with this problem, in this paper a hybrid algorithm is proposed to integrate Modified Artificial Bee Colony (MABC with Enhanced Particle Swarm Optimization (EPSO to predict the intrusion detection problem. The algorithms are combined together to find out better optimization results and the classification accuracies are obtained by 10-fold cross-validation method. The purpose of this paper is to select the most relevant features that can represent the pattern of the network traffic and test its effect on the success of the proposed hybrid classification algorithm. To investigate the performance of the proposed method, intrusion detection KDDCup’99 benchmark dataset from the UCI Machine Learning repository is used. The performance of the proposed method is compared with the other machine learning algorithms and found to be significantly different.

Adaptive Intrusion Data System (AIDS)

International Nuclear Information System (INIS)

Corlis, N.E.

1980-05-01

The adaptive intrusion data system (AIDS) was developed to collect data from intrusion alarm sensors as part of an evaluation system to improve sensor performance. AIDS is a unique data system which uses computer controlled data systems, video cameras and recorders, analog-to-digital conversion, environmental sensors, and digital recorders to collect sensor data. The data can be viewed either manually or with a special computerized data-reduction system which adds new data to a data base stored on a magnetic disc recorder. This report provides a synoptic account of the AIDS as it presently exists. Modifications to the purchased subsystems are described, and references are made to publications which describe the Sandia-designed subsystems

Towards real-time intrusion detection for NetFlow and IPFIX

NARCIS (Netherlands)

Hofstede, R.J.; Bartos, Vaclav; Sperotto, Anna; Pras, Aiko

2013-01-01

DDoS attacks bring serious economic and technical damage to networks and enterprises. Timely detection and mitigation are therefore of great importance. However, when flow monitoring systems are used for intrusion detection, as it is often the case in campus, enterprise and backbone networks, timely

Electric field sensor studies

International Nuclear Information System (INIS)

Griffith, R.D.; Parks, S.

1977-01-01

Above-ground intrusion sensors are reviewed briefly. Buried wire sensors are next considered; feasibility studies were conducted. A triangular system of an overhead transmitter wire exciting two buried sensor wires was developed and tested. It failed sometimes to detect a man making a broad jump. A differential receiver was developed to solve this problem

Intrusion detection system using Online Sequence Extreme Learning Machine (OS-ELM) in advanced metering infrastructure of smart grid.

Science.gov (United States)

Li, Yuancheng; Qiu, Rixuan; Jing, Sitong

2018-01-01

Advanced Metering Infrastructure (AMI) realizes a two-way communication of electricity data through by interconnecting with a computer network as the core component of the smart grid. Meanwhile, it brings many new security threats and the traditional intrusion detection method can't satisfy the security requirements of AMI. In this paper, an intrusion detection system based on Online Sequence Extreme Learning Machine (OS-ELM) is established, which is used to detecting the attack in AMI and carrying out the comparative analysis with other algorithms. Simulation results show that, compared with other intrusion detection methods, intrusion detection method based on OS-ELM is more superior in detection speed and accuracy.

Design and implementation of an intrusion detection system based on IPv6 protocol

Science.gov (United States)

Liu, Bin; Li, Zhitang; Li, Yao; Li, Zhanchun

2005-11-01

Network intrusion detection systems (NIDS) are important parts of network security architecture. Although many NIDS have been proposed, there is little effort to expand the current set of NIDS to support IPv6 protocol. This paper presents the design and implementation of a Network-based Intrusion Detection System that supports both IPv6 protocol and IPv4 protocol. It characters rules based logging to perform content pattern matching and detect a variety of attacks and probes from IPv4 and IPv6.There are four primary subsystems to make it up: packet capture, packet decoder, detection engine, and logging and alerting subsystem. A new approach to packet capture that combined NAPI with MMAP is proposed in this paper. The test results show that the efficiency of packet capture can be improved significantly by this method. Several new attack tools for IPv6 have been developed for intrusion detection evaluation. Test shows that more than 20 kinds of IPv6 attacks can be detected by this system and it also has a good performance under heavy traffic load.

A Comprehensive Review and meta-analysis on Applications of Machine Learning Techniques in Intrusion Detection

Directory of Open Access Journals (Sweden)

Manojit Chattopadhyay

2018-05-01

Full Text Available Securing a machine from various cyber-attacks has been of serious concern for researchers, statutory bodies such as governments, business organizations and users in both wired and wireless media. However, during the last decade, the amount of data handling by any device, particularly servers, has increased exponentially and hence the security of these devices has become a matter of utmost concern. This paper attempts to examine the challenges in the application of machine learning techniques to intrusion detection. We review different inherent issues in defining and applying the machine learning techniques to intrusion detection. We also attempt to identify the best technological solution for changing usage pattern by comparing different machine learning techniques on different datasets and summarizing their performance using various performance metrics. This paper highlights the research challenges and future trends of intrusion detection in dynamic scenarios of intrusion detection problems in diverse network technologies.

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...

DAVID: A new video motion sensor for outdoor perimeter applications

International Nuclear Information System (INIS)

Alexander, J.C.

1986-01-01

To be effective, a perimeter intrusion detection system must comprise both sensor and rapid assessment components. The use of closed circuit television (CCTV) to provide the rapid assessment capability, makes possible the use of video motion detection (VMD) processing as a system sensor component. Despite it's conceptual appeal, video motion detection has not been widely used in outdoor perimeter systems because of an inability to discriminate between genuine intrusions and numerous environmental effects such as cloud shadows, wind motion, reflections, precipitation, etc. The result has been an unacceptably high false alarm rate and operator work-load. DAVID (Digital Automatic Video Intrusion Detector) utilizes new digital signal processing techniques to achieve a dramatic improvement in discrimination performance thereby making video motion detection practical for outdoor applications. This paper begins with a discussion of the key considerations in implementing an outdoor video intrusion detection system, followed by a description of the DAVID design in light of these considerations

Hybrid Intrusion Detection System for DDoS Attacks

Directory of Open Access Journals (Sweden)

Özge Cepheli

2016-01-01

Full Text Available Distributed denial-of-service (DDoS attacks are one of the major threats and possibly the hardest security problem for today’s Internet. In this paper we propose a hybrid detection system, referred to as hybrid intrusion detection system (H-IDS, for detection of DDoS attacks. Our proposed detection system makes use of both anomaly-based and signature-based detection methods separately but in an integrated fashion and combines the outcomes of both detectors to enhance the overall detection accuracy. We apply two distinct datasets to our proposed system in order to test the detection performance of H-IDS and conclude that the proposed hybrid system gives better results than the systems based on nonhybrid detection.

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

KAUST Repository

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

2014-01-01

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

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.

A novel intrusion detection method based on OCSVM and K-means recursive clustering

Directory of Open Access Journals (Sweden)

Leandros A. Maglaras

2015-01-01

Full Text Available In this paper we present an intrusion detection module capable of detecting malicious network traffic in a SCADA (Supervisory Control and Data Acquisition system, based on the combination of One-Class Support Vector Machine (OCSVM with RBF kernel and recursive k-means clustering. Important parameters of OCSVM, such as Gaussian width o and parameter v affect the performance of the classifier. Tuning of these parameters is of great importance in order to avoid false positives and over fitting. The combination of OCSVM with recursive k- means clustering leads the proposed intrusion detection module to distinguish real alarms from possible attacks regardless of the values of parameters o and v, making it ideal for real-time intrusion detection mechanisms for SCADA systems. Extensive simulations have been conducted with datasets extracted from small and medium sized HTB SCADA testbeds, in order to compare the accuracy, false alarm rate and execution time against the base line OCSVM method.

Low-Cost Ground Sensor Network for Intrusion Detection

Science.gov (United States)

2017-09-01

their suitability to our research. 1. Wireless Sensor Networks The backend network infrastructure forms the communication links for the network...were not ideal as they were perpetually turned on. Our research considered the backend communication infrastructure and its power requirements when...7 3. Border Patrol— Mobile Situation Awareness Tool (MSAT

Intrusion detection system using Online Sequence Extreme Learning Machine (OS-ELM in advanced metering infrastructure of smart grid.

Directory of Open Access Journals (Sweden)

Yuancheng Li

Full Text Available Advanced Metering Infrastructure (AMI realizes a two-way communication of electricity data through by interconnecting with a computer network as the core component of the smart grid. Meanwhile, it brings many new security threats and the traditional intrusion detection method can't satisfy the security requirements of AMI. In this paper, an intrusion detection system based on Online Sequence Extreme Learning Machine (OS-ELM is established, which is used to detecting the attack in AMI and carrying out the comparative analysis with other algorithms. Simulation results show that, compared with other intrusion detection methods, intrusion detection method based on OS-ELM is more superior in detection speed and accuracy.

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.

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...

Panacea: Automating Attack Classification for Anomaly-based Network Intrusion Detection Systems

NARCIS (Netherlands)

Bolzoni, D.; Etalle, Sandro; Hartel, Pieter H.; Kirda, E.; Jha, S.; Balzarotti, D.

Anomaly-based intrusion detection systems are usually criticized because they lack a classication of attack, thus security teams have to manually inspect any raised alert to classify it. We present a new approach, Panacea, to automatically and systematically classify attacks detected by an

Panacea : Automating attack classification for anomaly-based network intrusion detection systems

NARCIS (Netherlands)

Bolzoni, D.; Etalle, S.; Hartel, P.H.; Kirda, E.; Jha, S.; Balzarotti, D.

2009-01-01

Anomaly-based intrusion detection systems are usually criticized because they lack a classification of attacks, thus security teams have to manually inspect any raised alert to classify it. We present a new approach, Panacea, to automatically and systematically classify attacks detected by an

Panacea : Automating attack classification for anomaly-based network intrusion detection systems

NARCIS (Netherlands)

Bolzoni, D.; Etalle, S.; Hartel, P.H.

2009-01-01

Anomaly-based intrusion detection systems are usually criticized because they lack a classification of attack, thus security teams have to manually inspect any raised alert to classify it. We present a new approach, Panacea, to automatically and systematically classify attacks detected by an

Panacea: Automating Attack Classification for Anomaly-based Network Intrusion Detection Systems

NARCIS (Netherlands)

Bolzoni, D.; Etalle, Sandro; Hartel, Pieter H.

2009-01-01

Anomaly-based intrusion detection systems are usually criticized because they lack a classication of attack, thus security teams have to manually inspect any raised alert to classify it. We present a new approach, Panacea, to automatically and systematically classify attacks detected by an

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.

Optical sensor for real-time weld defect detection

Science.gov (United States)

Ancona, Antonio; Maggipinto, Tommaso; Spagnolo, Vincenzo; Ferrara, Michele; Lugara, Pietro M.

2002-04-01

In this work we present an innovative optical sensor for on- line and non-intrusive welding process monitoring. It is based on the spectroscopic analysis of the optical VIS emission of the welding plasma plume generated in the laser- metal interaction zone. Plasma electron temperature has been measured for different chemical species composing the plume. Temperature signal evolution has been recorded and analyzed during several CO2-laser welding processes, under variable operating conditions. We have developed a suitable software able to real time detect a wide range of weld defects like crater formation, lack of fusion, excessive penetration, seam oxidation. The same spectroscopic approach has been applied for electric arc welding process monitoring. We assembled our optical sensor in a torch for manual Gas Tungsten Arc Welding procedures and tested the prototype in a manufacturing industry production line. Even in this case we found a clear correlation between the signal behavior and the welded joint quality.

Use of behavioral biometrics in intrusion detection and online gaming

Science.gov (United States)

Yampolskiy, Roman V.; Govindaraju, Venu

2006-04-01

Behavior based intrusion detection is a frequently used approach for insuring network security. We expend behavior based intrusion detection approach to a new domain of game networks. Specifically, our research shows that a unique behavioral biometric can be generated based on the strategy used by an individual to play a game. We wrote software capable of automatically extracting behavioral profiles for each player in a game of Poker. Once a behavioral signature is generated for a player, it is continuously compared against player's current actions. Any significant deviations in behavior are reported to the game server administrator as potential security breaches. Our algorithm addresses a well-known problem of user verification and can be re-applied to the fields beyond game networks, such as operating systems and non-game networks security.

Scalable High-Performance Parallel Design for Network Intrusion Detection Systems on Many-Core Processors

OpenAIRE

Jiang, Hayang; Xie, Gaogang; Salamatian, Kavé; Mathy, Laurent

2013-01-01

Network Intrusion Detection Systems (NIDSes) face significant challenges coming from the relentless network link speed growth and increasing complexity of threats. Both hardware accelerated and parallel software-based NIDS solutions, based on commodity multi-core and GPU processors, have been proposed to overcome these challenges. Network Intrusion Detection Systems (NIDSes) face significant challenges coming from the relentless network link speed growth and increasing complexity of threats. ...

Prototype of Intrusion Detection Model using UML 5.0 and Forward Engineering

Directory of Open Access Journals (Sweden)

Muthaiyan MADIAJAGAN,

2011-01-01

Full Text Available In this paper we are using UML (Unified Modeling Language which is the blueprint language between the programmers, analysts, and designer’s for easy representation of pictures or diagrammatic notation with some textual data. Here we are using UML 5.0 to show “prototype of the Intrusion Detection Model” and by explaining it by combining various parts by drawing various UML diagrams such as Use cases and Activity diagrams and Class Diagram using which we show forward engineering using the class diagram of the IDM( Intrusion Detection Model. IDM is a device or software that works on detecting malicious activities by unauthorized users that can cause breach to the security policy within a network.

State of the art on defenses against wormhole attacks in wireless sensor networks

DEFF Research Database (Denmark)

Prasad, Neeli R.; Giannetsos, T.; Dimitriou, T.

2009-01-01

describe the wormhole attack, a severe routing attack against sensor networks that is particularly challenging to defend against. We detail its characteristics and study its effects on the successful operation of a sensor network. We present state-of-the-art research for addressing wormhole related...... the possibility of using more sophisticated methods, like intrusion detection systems, to achieve a more complete and autonomic defense mechanism against wormhole attackers. We present our work on intrusion detection and introduce a lightweight IDS framework, called LIDeA, designed for wireless sensor networks....... LIDeA is based on a distributed architecture, in which nodes overhear their neighboring nodes and collaborate with each other in order to successfully detect an intrusion. We conclude by highlighting how such a system can be used for defending against wormhole attackers....

A Labeled Data Set For Flow-based Intrusion Detection

NARCIS (Netherlands)

Sperotto, Anna; Sadre, R.; van Vliet, Frank; Pras, Aiko; Nunzi, Giorgio; Scoglio, Caterina; Li, Xing

2009-01-01

Flow-based intrusion detection has recently become a promising security mechanism in high speed networks (1-10 Gbps). Despite the richness in contributions in this field, benchmarking of flow-based IDS is still an open issue. In this paper, we propose the first publicly available, labeled data set

AANtID: an alternative approach to network intrusion detection ...

African Journals Online (AJOL)

Journal of Computer Science and Its Application ... Security has become not just a feature of an information system, but the core and a necessity especially the systems that communicate and transmit data over the Internet for they are more ... Keywords: Intrusion, Genetic Algorithm, detection, Security, DARPA dataset ...

Reading between the fields: practical, effective intrusion detection for industrial control systems

NARCIS (Netherlands)

Yüksel, Ömer; den Hartog, Jeremy; Etalle, Sandro

2016-01-01

Detection of previously unknown attacks and malicious messages is a challenging problem faced by modern network intrusion detection systems. Anomaly-based solutions, despite being able to detect unknown attacks, have not been used often in practice due to their high false positive rate, and because

Intrusion detection in cloud computing based attack patterns and risk assessment

Directory of Open Access Journals (Sweden)

Ben Charhi Youssef

2017-05-01

Full Text Available This paper is an extension of work originally presented in SYSCO CONF.We extend our previous work by presenting the initial results of the implementation of intrusion detection based on risk assessment on cloud computing. The idea focuses on a novel approach for detecting cyber-attacks on the cloud environment by analyzing attacks pattern using risk assessment methodologies. The aim of our solution is to combine evidences obtained from Intrusion Detection Systems (IDS deployed in a cloud with risk assessment related to each attack pattern. Our approach presents a new qualitative solution for analyzing each symptom, indicator and vulnerability analyzing impact and likelihood of distributed and multi-steps attacks directed to cloud environments. The implementation of this approach will reduce the number of false alerts and will improve the performance of the IDS.

Nuclear-power-plant perimeter-intrusion alarm systems

International Nuclear Information System (INIS)

Halsey, D.J.

1982-04-01

Timely intercept of an intruder requires the examination of perimeter barriers and sensors in terms of reliable detection, immediate assessment and prompt response provisions. Perimeter security equipment and operations must at the same time meet the requirements of the Code of Federal Regulations, 10 CFR 73.55 with some attention to the performance and testing figures of Nuclear Regulatory Guide 5.44, Revision 2, May 1980. A baseline system is defined which recommends a general approach to implementing perimeter security elements: barriers, lighting, intrusion detection, alarm assessment. The baseline approach emphasizes cost/effectiveness achieved by detector layering and logic processing of alarm signals to produce reliable alarms and low nuisance alarm rates. A cost benefit of layering along with video assessment is reduction in operating expense. The concept of layering is also shown to minimize testing costs where detectability performance as suggested by Regulatory Guide 5.44 is to be performed. Synthesis of the perimeter intrusion alarm system and limited testing of CCTV and Video Motion Detectors (VMD), were performed at E-Systems, Greenville Division, Greenville, Texas during 1981

Ensemble of classifiers based network intrusion detection system performance bound

CSIR Research Space (South Africa)

Mkuzangwe, Nenekazi NP

2017-11-01

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

Abstracting audit data for lightweight intrusion detection

KAUST Repository

Wang, Wei

2010-01-01

High speed of processing massive audit data is crucial for an anomaly Intrusion Detection System (IDS) to achieve real-time performance during the detection. Abstracting audit data is a potential solution to improve the efficiency of data processing. In this work, we propose two strategies of data abstraction in order to build a lightweight detection model. The first strategy is exemplar extraction and the second is attribute abstraction. Two clustering algorithms, Affinity Propagation (AP) as well as traditional k-means, are employed to extract the exemplars, and Principal Component Analysis (PCA) is employed to abstract important attributes (a.k.a. features) from the audit data. Real HTTP traffic data collected in our institute as well as KDD 1999 data are used to validate the two strategies of data abstraction. The extensive test results show that the process of exemplar extraction significantly improves the detection efficiency and has a better detection performance than PCA in data abstraction. © 2010 Springer-Verlag.

Attacks and Intrusion Detection in Cloud Computing Using Neural Networks and Particle Swarm Optimization Algorithms

Directory of Open Access Journals (Sweden)

Ahmad Shokuh Saljoughi

2018-01-01

Full Text Available Today, cloud computing has become popular among users in organizations and companies. Security and efficiency are the two major issues facing cloud service providers and their customers. Since cloud computing is a virtual pool of resources provided in an open environment (Internet, cloud-based services entail security risks. Detection of intrusions and attacks through unauthorized users is one of the biggest challenges for both cloud service providers and cloud users. In the present study, artificial intelligence techniques, e.g. MLP Neural Network sand particle swarm optimization algorithm, were used to detect intrusion and attacks. The methods were tested for NSL-KDD, KDD-CUP datasets. The results showed improved accuracy in detecting attacks and intrusions by unauthorized users.

Investigating the Influence of Special On–Off Attacks on Challenge-Based Collaborative Intrusion Detection Networks

Directory of Open Access Journals (Sweden)

Wenjuan Li

2018-01-01

Full Text Available Intrusions are becoming more complicated with the recent development of adversarial techniques. To boost the detection accuracy of a separate intrusion detector, the collaborative intrusion detection network (CIDN has thus been developed by allowing intrusion detection system (IDS nodes to exchange data with each other. Insider attacks are a great threat for such types of collaborative networks, where an attacker has the authorized access within the network. In literature, a challenge-based trust mechanism is effective at identifying malicious nodes by sending challenges. However, such mechanisms are heavily dependent on two assumptions, which would cause CIDNs to be vulnerable to advanced insider attacks in practice. In this work, we investigate the influence of advanced on–off attacks on challenge-based CIDNs, which can respond truthfully to one IDS node but behave maliciously to another IDS node. To evaluate the attack performance, we have conducted two experiments under a simulated and a real CIDN environment. The obtained results demonstrate that our designed attack is able to compromise the robustness of challenge-based CIDNs in practice; that is, some malicious nodes can behave untruthfully without a timely detection.

A Fusion of Multiagent Functionalities for Effective Intrusion Detection System

OpenAIRE

Dhanalakshmi Krishnan Sadhasivan; Kannapiran Balasubramanian

2017-01-01

Provision of high security is one of the active research areas in the network applications. The failure in the centralized system based on the attacks provides less protection. Besides, the lack of update of new attacks arrival leads to the minimum accuracy of detection. The major focus of this paper is to improve the detection performance through the adaptive update of attacking information to the database. We propose an Adaptive Rule-Based Multiagent Intrusion Detection System (ARMA-IDS) to...

Real-Time and Resilient Intrusion Detection: A Flow-Based Approach

NARCIS (Netherlands)

Hofstede, R.J.; Pras, Aiko

Due to the demanding performance requirements of packet-based monitoring solutions on network equipment, flow-based intrusion detection systems will play an increasingly important role in current high-speed networks. The required technologies are already available and widely deployed: NetFlow and

Securing Cloud Computing from Different Attacks Using Intrusion Detection Systems

Directory of Open Access Journals (Sweden)

Omar Achbarou

2017-03-01

Full Text Available Cloud computing is a new way of integrating a set of old technologies to implement a new paradigm that creates an avenue for users to have access to shared and configurable resources through internet on-demand. This system has many common characteristics with distributed systems, hence, the cloud computing also uses the features of networking. Thus the security is the biggest issue of this system, because the services of cloud computing is based on the sharing. Thus, a cloud computing environment requires some intrusion detection systems (IDSs for protecting each machine against attacks. The aim of this work is to present a classification of attacks threatening the availability, confidentiality and integrity of cloud resources and services. Furthermore, we provide literature review of attacks related to the identified categories. Additionally, this paper also introduces related intrusion detection models to identify and prevent these types of attacks.

Sensor for metal detection

KAUST Repository

Kodzius, Rimantas

2014-06-26

NOVELTY - The sensor has a microfluidic flow channel that is provided with an inlet port, an outlet port, and a detection chamber. The detection chamber is provided with a group of sensing electrodes (4) having a working electrode (8), a counter electrode (9), and a reference electrode (10). A flow sensor is configured to measure flow in the channel. A temperature sensor (6) is configured to measure temperature in the channel (3). An electrical connection is configured to connect the sensor to a sensing device. USE - Sensor for detecting metal such as toxic metal in sample such as clinical sample such as stool, saliva, sputum, bronchial lavage, urine, vaginal swab, nasal swab, biopsy, tissue, tears, breath, blood, serum, plasma, cerebrospinal fluid, peritoneal fluid, pleural fluid, pericardial fluid, joint fluid, and amniotic fluid, water sample, food sample, air sample, and soil sample (all claimed). ADVANTAGE - The sensor for use with the portable analytical instrument is configured for detection of metalsin samples. The sensor can provide the excellent solution for on-site metal detection, including heavy metal detection. The sensors can provide significant advantages in higher throughput, lower cost, at the same time being less labor intensive and less dependent on individual skills. The disposable design of the sensor, the enhanced reliability and repeatability of measurements can be obtained. The sensors can be widely applied in various industries. DETAILED DESCRIPTION - INDEPENDENT CLAIMS are included for the following: (1) a system for detecting metal in sample; and (2) a method for using sensor for detecting metal in sample. DESCRIPTION OF DRAWING(S) - The drawing shows a schematic view of the sensor prototype. Channel (3) Sensing electrodes (4) Temperature sensor (6) Working electrode (8) Counter electrode (9) Reference electrode (10)

Developing advanced fingerprint attacks on challenge-based collaborative intrusion detection networks

DEFF Research Database (Denmark)

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

2017-01-01

Traditionally, an isolated intrusion detection system (IDS) is vulnerable to various types of attacks. In order to enhance IDS performance, collaborative intrusion detection networks (CIDNs) are developed through enabling a set of IDS nodes to communicate with each other. Due to the distributed...... network architecture, insider attacks are one of the major threats. In the literature, challenge-based trust mechanisms have been built to identify malicious nodes by evaluating the satisfaction levels between challenges and responses. However, such mechanisms rely on two major assumptions, which may...... result in a weak threat model. In this case, CIDNs may be still vulnerable to advanced insider attacks in real-world deployment. In this paper, we propose a novel collusion attack, called passive message fingerprint attack (PMFA), which can collect messages and identify normal requests in a passive way...

Adaptive intrusion data system (AIDS) software routines

International Nuclear Information System (INIS)

Corlis, N.E.

1980-07-01

An Adaptive Intrusion Data System (AIDS) was developed to collect information from intrusion alarm sensors as part of an evaluation system to improve sensor performance. AIDS is a unique digital data-compression, storage, and formatting system; it also incorporates a capability for video selection and recording for assessment of the sensors monitored by the system. The system is software reprogrammable to numerous configurations that may be used for the collection of environmental, bilevel, analog, and video data. This report describes the software routines that control the different AIDS data-collection modes, the diagnostic programs to test the operating hardware, and the data format. Sample data printouts are also included

Real-time distributed fiber optic sensor for security systems: Performance, event classification and nuisance mitigation

Science.gov (United States)

Mahmoud, Seedahmed S.; Visagathilagar, Yuvaraja; Katsifolis, Jim

2012-09-01

The success of any perimeter intrusion detection system depends on three important performance parameters: the probability of detection (POD), the nuisance alarm rate (NAR), and the false alarm rate (FAR). The most fundamental parameter, POD, is normally related to a number of factors such as the event of interest, the sensitivity of the sensor, the installation quality of the system, and the reliability of the sensing equipment. The suppression of nuisance alarms without degrading sensitivity in fiber optic intrusion detection systems is key to maintaining acceptable performance. Signal processing algorithms that maintain the POD and eliminate nuisance alarms are crucial for achieving this. In this paper, a robust event classification system using supervised neural networks together with a level crossings (LCs) based feature extraction algorithm is presented for the detection and recognition of intrusion and non-intrusion events in a fence-based fiber-optic intrusion detection system. A level crossings algorithm is also used with a dynamic threshold to suppress torrential rain-induced nuisance alarms in a fence system. Results show that rain-induced nuisance alarms can be suppressed for rainfall rates in excess of 100 mm/hr with the simultaneous detection of intrusion events. The use of a level crossing based detection and novel classification algorithm is also presented for a buried pipeline fiber optic intrusion detection system for the suppression of nuisance events and discrimination of intrusion events. The sensor employed for both types of systems is a distributed bidirectional fiber-optic Mach-Zehnder (MZ) interferometer.

Attack Pattern Analysis Framework for a Multiagent Intrusion Detection System

Directory of Open Access Journals (Sweden)

Krzysztof Juszczyszyn

2008-08-01

Full Text Available The paper proposes the use of attack pattern ontology and formal framework for network traffic anomalies detection within a distributed multi-agent Intrusion Detection System architecture. Our framework assumes ontology-based attack definition and distributed processing scheme with exchange of communicates between agents. The role of traffic anomalies detection was presented then it has been discussed how some specific values characterizing network communication can be used to detect network anomalies caused by security incidents (worm attack, virus spreading. Finally, it has been defined how to use the proposed techniques in distributed IDS using attack pattern ontology.

Towards software-based signature detection for intrusion prevention on the network card

NARCIS (Netherlands)

Bos, H.; Huang, Kaiming

2006-01-01

CardGuard is a signature detection system for intrusion detection and prevention that scans the entire payload of packets for suspicious patterns and is implemented in software on a network card equiped with an Intel IXP1200 network processor. One card can be used to protect either a single host, or

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

NARCIS (Netherlands)

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

2007-01-01

We present an architecture designed for alert verification (i.e., to reduce false positives) in network intrusion-detection systems. Our technique is based on a systematic (and automatic) anomaly-based analysis of the system output, which provides useful context information regarding the network

Using discriminant analysis to detect intrusions in external communication for self-driving vehicles

Directory of Open Access Journals (Sweden)

Khattab M.Ali Alheeti

2017-08-01

Full Text Available Security systems are a necessity for the deployment of smart vehicles in our society. Security in vehicular ad hoc networks is crucial to the reliable exchange of information and control data. In this paper, we propose an intelligent Intrusion Detection System (IDS to protect the external communication of self-driving and semi self-driving vehicles. This technology has the ability to detect Denial of Service (DoS and black hole attacks on vehicular ad hoc networks (VANETs. The advantage of the proposed IDS over existing security systems is that it detects attacks before they causes significant damage. The intrusion prediction technique is based on Linear Discriminant Analysis (LDA and Quadratic Discriminant Analysis (QDA which are used to predict attacks based on observed vehicle behavior. We perform simulations using Network Simulator 2 to demonstrate that the IDS achieves a low rate of false alarms and high accuracy in detection.

Effective approach toward Intrusion Detection System using data mining techniques

Directory of Open Access Journals (Sweden)

G.V. Nadiammai

2014-03-01

Full Text Available With the tremendous growth of the usage of computers over network and development in application running on various platform captures the attention toward network security. This paradigm exploits security vulnerabilities on all computer systems that are technically difficult and expensive to solve. Hence intrusion is used as a key to compromise the integrity, availability and confidentiality of a computer resource. The Intrusion Detection System (IDS plays a vital role in detecting anomalies and attacks in the network. In this work, data mining concept is integrated with an IDS to identify the relevant, hidden data of interest for the user effectively and with less execution time. Four issues such as Classification of Data, High Level of Human Interaction, Lack of Labeled Data, and Effectiveness of Distributed Denial of Service Attack are being solved using the proposed algorithms like EDADT algorithm, Hybrid IDS model, Semi-Supervised Approach and Varying HOPERAA Algorithm respectively. Our proposed algorithm has been tested using KDD Cup dataset. All the proposed algorithm shows better accuracy and reduced false alarm rate when compared with existing algorithms.

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.

Energy Efficient Monitoring for Intrusion Detection in Battery-Powered Wireless Mesh Networks

KAUST Repository

Hassanzadeh, Amin

2011-07-18

Wireless Mesh Networks (WMN) are easy-to-deploy, low cost solutions for providing networking and internet services in environments with no network infrastructure, e.g., disaster areas and battlefields. Since electric power is not readily available in such environments battery-powered mesh routers, operating in an energy efficient manner, are required. To the best of our knowledge, the impact of energy efficient solutions, e.g., involving duty-cycling, on WMN intrusion detection systems, which require continuous monitoring, remains an open research problem. In this paper we propose that carefully chosen monitoring mesh nodes ensure continuous and complete detection coverage, while allowing non-monitoring mesh nodes to save energy through duty-cycling. We formulate the monitoring node selection problem as an optimization problem and propose distributed and centralized solutions for it, with different tradeoffs. Through extensive simulations and a proof-of-concept hardware/software implementation we demonstrate that our solutions extend the WMN lifetime by 8%, while ensuring, at the minimum, a 97% intrusion detection rate.

Performance Analysis of Hierarchical Group Key Management Integrated with Adaptive Intrusion Detection in Mobile ad hoc Networks

Science.gov (United States)

2016-04-05

applications in wireless networks such as military battlefields, emergency response, mobile commerce , online gaming, and collaborative work are based on the...www.elsevier.com/locate/peva Performance analysis of hierarchical group key management integrated with adaptive intrusion detection in mobile ad hoc...Accepted 19 September 2010 Available online 26 September 2010 Keywords: Mobile ad hoc networks Intrusion detection Group communication systems Group

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...... to advanced insider attacks in practical deployment. In this paper, we design a novel type of collusion attack, called passive message fingerprint attack (PMFA), which can collect messages and identify normal requests in a passive way. In the evaluation, we explore the attack performance under both simulated...... and real network environments. Experimental results indicate that under our attack, malicious nodes can send malicious responses to normal requests while maintaining their trust values....

Ensemble regression model-based anomaly detection for cyber-physical intrusion detection in smart grids

DEFF Research Database (Denmark)

Kosek, Anna Magdalena; Gehrke, Oliver

2016-01-01

The shift from centralised large production to distributed energy production has several consequences for current power system operation. The replacement of large power plants by growing numbers of distributed energy resources (DERs) increases the dependency of the power system on small scale......, distributed production. Many of these DERs can be accessed and controlled remotely, posing a cybersecurity risk. This paper investigates an intrusion detection system which evaluates the DER operation in order to discover unauthorized control actions. The proposed anomaly detection method is based...

Messaging Attacks on Android: Vulnerabilities and Intrusion Detection

Directory of Open Access Journals (Sweden)

Khodor Hamandi

2015-01-01

Full Text Available Currently, Android is the leading mobile operating system in number of users worldwide. On the security side, Android has had significant challenges despite the efforts of the Android designers to provide a secure environment for apps. In this paper, we present numerous attacks targeting the messaging framework of the Android system. Our focus is on SMS, USSD, and the evolution of their associated security in Android and accordingly the development of related attacks. Also, we shed light on the Android elements that are responsible for these attacks. Furthermore, we present the architecture of an intrusion detection system (IDS that promises to thwart SMS messaging attacks. Our IDS shows a detection rate of 87.50% with zero false positives.

Industrial Control System Process-Oriented Intrusion Detection (iPoid) Algorithm

Science.gov (United States)

2016-08-01

SUBJECT TERMS supervisory control and data acquisition (SCADA), Modbus, industrial control system, intrusion detection system 16. SECURITY...List of Tables iv Acknowledgments v 1. Background 1 2. iPoid Modbus Packet-Inspection Capability 2 2.1 Software Requirements 2 2.2 Startup ...Mr Curtis Arnold’s support of Industrial Control Systems–Supervisory Control and Data Acquisition research at the US Army Research Laboratory

SOOA: Exploring Special On-Off Attacks on Challenge-Based Collaborative Intrusion Detection Networks

DEFF Research Database (Denmark)

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

2017-01-01

The development of collaborative intrusion detection networks (CIDNs) aims to enhance the performance of a single intrusion detection system (IDS), through communicating and collecting information from other IDS nodes. To defend CIDNs against insider attacks, trust-based mechanisms are crucial...... and render CIDNs still vulnerable to advanced insider attacks in a practical deployment. In this paper, our motivation is to investigate the effect of On-Off attacks on challenge-based CIDNs. In particular, as a study, we explore a special On-Off attack (called SOOA), which can keep responding normally...... to one node while acting abnormally to another node. In the evaluation, we explore the attack performance under simulated CIDN environments. Experimental results indicate that our attack can interfere the effectiveness of trust computation for CIDN nodes....

Energy Efficient Monitoring for Intrusion Detection in Battery-Powered Wireless Mesh Networks

KAUST Repository

Hassanzadeh, Amin; Stoleru, Radu; Shihada, Basem

2011-01-01

in such environments battery-powered mesh routers, operating in an energy efficient manner, are required. To the best of our knowledge, the impact of energy efficient solutions, e.g., involving duty-cycling, on WMN intrusion detection systems, which require continuous

Feature selection for anomaly–based network intrusion detection using cluster validity indices

CSIR Research Space (South Africa)

Naidoo, T

2015-09-01

Full Text Available for Anomaly–Based Network Intrusion Detection Using Cluster Validity Indices Tyrone Naidoo_, Jules–Raymond Tapamoy, Andre McDonald_ Modelling and Digital Science, Council for Scientific and Industrial Research, South Africa 1tnaidoo2@csir.co.za 3...

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.

On Cyber Attacks and Signature Based Intrusion Detection for MODBUS Based Industrial Control Systems

Directory of Open Access Journals (Sweden)

Wei Gao

2014-03-01

Full Text Available Industrial control system communication networks are vulnerable to reconnaissance, response injection, command injection, and denial of service attacks.  Such attacks can lead to an inability to monitor and control industrial control systems and can ultimately lead to system failure. This can result in financial loss for control system operators and economic and safety issues for the citizens who use these services.  This paper describes a set of 28 cyber attacks against industrial control systems which use the MODBUS application layer network protocol. The paper also describes a set of standalone and state based intrusion detection system rules which can be used to detect cyber attacks and to store evidence of attacks for post incident analysis. All attacks described in this paper were validated in a laboratory environment. The detection rate of the intrusion detection system rules presented by attack class is also presented.

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...

Trouble Brewing: Using Observations of Invariant Behavior to Detect Malicious Agency in Distributed Control Systems

Science.gov (United States)

McEvoy, Thomas Richard; Wolthusen, Stephen D.

Recent research on intrusion detection in supervisory data acquisition and control (SCADA) and DCS systems has focused on anomaly detection at protocol level based on the well-defined nature of traffic on such networks. Here, we consider attacks which compromise sensors or actuators (including physical manipulation), where intrusion may not be readily apparent as data and computational states can be controlled to give an appearance of normality, and sensor and control systems have limited accuracy. To counter these, we propose to consider indirect relations between sensor readings to detect such attacks through concurrent observations as determined by control laws and constraints.

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.

A Multiagent-based Intrusion Detection System with the Support of Multi-Class Supervised Classification

Science.gov (United States)

Shyu, Mei-Ling; Sainani, Varsha

The increasing number of network security related incidents have made it necessary for the organizations to actively protect their sensitive data with network intrusion detection systems (IDSs). IDSs are expected to analyze a large volume of data while not placing a significantly added load on the monitoring systems and networks. This requires good data mining strategies which take less time and give accurate results. In this study, a novel data mining assisted multiagent-based intrusion detection system (DMAS-IDS) is proposed, particularly with the support of multiclass supervised classification. These agents can detect and take predefined actions against malicious activities, and data mining techniques can help detect them. Our proposed DMAS-IDS shows superior performance compared to central sniffing IDS techniques, and saves network resources compared to other distributed IDS with mobile agents that activate too many sniffers causing bottlenecks in the network. This is one of the major motivations to use a distributed model based on multiagent platform along with a supervised classification technique.

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

A Database of Computer Attacks for the Evaluation of Intrusion Detection Systems

Science.gov (United States)

1999-06-01

administrator whenever a system binary file (such as the ps, login , or ls program) is modified. Normal users have no legitimate reason to alter these files...development of EMERALD [46], which combines statistical anomaly detection from NIDES with signature verification. Specification-based intrusion detection...the creation of a single host that can act as many hosts. Daemons that provide network services—including telnetd, ftpd, and login — display banners

A Distributed Intrusion Detection Scheme about Communication Optimization in Smart Grid

Directory of Open Access Journals (Sweden)

Yunfa Li

2013-01-01

Full Text Available We first propose an efficient communication optimization algorithm in smart grid. Based on the optimization algorithm, we propose an intrusion detection algorithm to detect malicious data and possible cyberattacks. In this scheme, each node acts independently when it processes communication flows or cybersecurity threats. And neither special hardware nor nodes cooperation is needed. In order to justify the feasibility and the availability of this scheme, a series of experiments have been done. The results show that it is feasible and efficient to detect malicious data and possible cyberattacks with less computation and communication cost.

Towards Effective Network Intrusion Detection: A Hybrid Model Integrating Gini Index and GBDT with PSO

Directory of Open Access Journals (Sweden)

Longjie Li

2018-01-01

Full Text Available In order to protect computing systems from malicious attacks, network intrusion detection systems have become an important part in the security infrastructure. Recently, hybrid models that integrating several machine learning techniques have captured more attention of researchers. In this paper, a novel hybrid model was proposed with the purpose of detecting network intrusion effectively. In the proposed model, Gini index is used to select the optimal subset of features, the gradient boosted decision tree (GBDT algorithm is adopted to detect network attacks, and the particle swarm optimization (PSO algorithm is utilized to optimize the parameters of GBDT. The performance of the proposed model is experimentally evaluated in terms of accuracy, detection rate, precision, F1-score, and false alarm rate using the NSL-KDD dataset. Experimental results show that the proposed model is superior to the compared methods.

Evaluations of fiber optic sensors for interior applications

Energy Technology Data Exchange (ETDEWEB)

Sandoval, M.W.; Malone, T.P.

1996-02-01

This report addresses the testing and evaluation of commercial fiber optic intrusion detection systems in interior applications. The applications include laying optical fiber cable above suspended ceilings to detect removal of ceiling tiles, embedding optical fibers inside a tamper or item monitoring blanket that could be placed over an asset, and installing optical fibers on a door to detect movement or penetration. Detection capability of the fiber optic sensors as well as nuisance and false alarm information were focused on during the evaluation. Fiber optic sensor processing, system components, and system setup are described.

Intrusion detection model using fusion of chi-square feature selection and multi class SVM

Directory of Open Access Journals (Sweden)

Ikram Sumaiya Thaseen

2017-10-01

Full Text Available Intrusion detection is a promising area of research in the domain of security with the rapid development of internet in everyday life. Many intrusion detection systems (IDS employ a sole classifier algorithm for classifying network traffic as normal or abnormal. Due to the large amount of data, these sole classifier models fail to achieve a high attack detection rate with reduced false alarm rate. However by applying dimensionality reduction, data can be efficiently reduced to an optimal set of attributes without loss of information and then classified accurately using a multi class modeling technique for identifying the different network attacks. In this paper, we propose an intrusion detection model using chi-square feature selection and multi class support vector machine (SVM. A parameter tuning technique is adopted for optimization of Radial Basis Function kernel parameter namely gamma represented by ‘ϒ’ and over fitting constant ‘C’. These are the two important parameters required for the SVM model. The main idea behind this model is to construct a multi class SVM which has not been adopted for IDS so far to decrease the training and testing time and increase the individual classification accuracy of the network attacks. The investigational results on NSL-KDD dataset which is an enhanced version of KDDCup 1999 dataset shows that our proposed approach results in a better detection rate and reduced false alarm rate. An experimentation on the computational time required for training and testing is also carried out for usage in time critical applications.

System overview and applications of a panoramic imaging perimeter sensor

International Nuclear Information System (INIS)

Pritchard, D.A.

1995-01-01

This paper presents an overview of the design and potential applications of a 360-degree scanning, multi-spectral intrusion detection sensor. This moderate-resolution, true panoramic imaging sensor is intended for exterior use at ranges from 50 to 1,500 meters. This Advanced Exterior Sensor (AES) simultaneously uses three sensing technologies (infrared, visible, and radar) along with advanced data processing methods to provide low false-alarm intrusion detection, tracking, and immediate visual assessment. The images from the infrared and visible detector sets and the radar range data are updated as the sensors rotate once per second. The radar provides range data with one-meter resolution. This sensor has been designed for easy use and rapid deployment to cover wide areas beyond or in place of typical perimeters, and tactical applications around fixed or temporary high-value assets. AES prototypes are in development. Applications discussed in this paper include replacements, augmentations, or new installations at fixed sites where topological features, atmospheric conditions, environmental restrictions, ecological regulations, and archaeological features limit the use of conventional security components and systems

Indian program for development of technologies relevant to reliable, non-intrusive, concealed-contraband detection

International Nuclear Information System (INIS)

Auluck, S.K.H.

2007-01-01

Generating capability for reliable, non-intrusive detection of concealed-contraband, particularly, organic contraband like explosives and narcotics, has become a national priority. This capability spans a spectrum of technologies. If a technology mission addressing the needs of a highly sophisticated technology like PFNA is set up, the capabilities acquired would be adequate to meet the requirements of many other sets of technologies. This forms the background of the Indian program for development of technologies relevant to reliable, non-intrusive, concealed contraband detection. One of the central themes of the technology development programs would be modularization of the neutron source and detector technologies, so that common elements can be combined in different ways for meeting a variety of application requirements. (author)

Network Intrusion Dataset Assessment

Science.gov (United States)

2013-03-01

International Conference on Computational Intelligence and Natural Computing, volume 2, pages 413–416, June 2009. • Rung Ching Chen, Kai -Fan Cheng, and...Chia-Fen Hsieh . “Using rough set and support vector machine for network intrusion detection.” International Journal of Network Security & Its...intrusion detection using FP tree rules.” Journal Of Advanced Networking and Applications, 1(1):30–39, 2009. • Ming-Yang Su, Gwo-Jong Yu , and Chun-Yuen

Proposed Network Intrusion Detection System ‎Based on Fuzzy c Mean Algorithm in Cloud ‎Computing Environment

Directory of Open Access Journals (Sweden)

Shawq Malik Mehibs

2017-12-01

Full Text Available Nowadays cloud computing had become is an integral part of IT industry, cloud computing provides Working environment allow a user of environmental to share data and resources over the internet. Where cloud computing its virtual grouping of resources offered over the internet, this lead to different matters related to the security and privacy in cloud computing. And therefore, create intrusion detection very important to detect outsider and insider intruders of cloud computing with high detection rate and low false positive alarm in the cloud environment. This work proposed network intrusion detection module using fuzzy c mean algorithm. The kdd99 dataset used for experiments .the proposed system characterized by a high detection rate with low false positive alarm

A New Unified Intrusion Anomaly Detection in Identifying Unseen Web Attacks

Directory of Open Access Journals (Sweden)

Muhammad Hilmi Kamarudin

2017-01-01

Full Text Available The global usage of more sophisticated web-based application systems is obviously growing very rapidly. Major usage includes the storing and transporting of sensitive data over the Internet. The growth has consequently opened up a serious need for more secured network and application security protection devices. Security experts normally equip their databases with a large number of signatures to help in the detection of known web-based threats. In reality, it is almost impossible to keep updating the database with the newly identified web vulnerabilities. As such, new attacks are invisible. This research presents a novel approach of Intrusion Detection System (IDS in detecting unknown attacks on web servers using the Unified Intrusion Anomaly Detection (UIAD approach. The unified approach consists of three components (preprocessing, statistical analysis, and classification. Initially, the process starts with the removal of irrelevant and redundant features using a novel hybrid feature selection method. Thereafter, the process continues with the application of a statistical approach to identifying traffic abnormality. We performed Relative Percentage Ratio (RPR coupled with Euclidean Distance Analysis (EDA and the Chebyshev Inequality Theorem (CIT to calculate the normality score and generate a finest threshold. Finally, Logitboost (LB is employed alongside Random Forest (RF as a weak classifier, with the aim of minimising the final false alarm rate. The experiment has demonstrated that our approach has successfully identified unknown attacks with greater than a 95% detection rate and less than a 1% false alarm rate for both the DARPA 1999 and the ISCX 2012 datasets.

Alerts Analysis and Visualization in Network-based Intrusion Detection Systems

Energy Technology Data Exchange (ETDEWEB)

Yang, Dr. Li [University of Tennessee

2010-08-01

The alerts produced by network-based intrusion detection systems, e.g. Snort, can be difficult for network administrators to efficiently review and respond to due to the enormous number of alerts generated in a short time frame. This work describes how the visualization of raw IDS alert data assists network administrators in understanding the current state of a network and quickens the process of reviewing and responding to intrusion attempts. The project presented in this work consists of three primary components. The first component provides a visual mapping of the network topology that allows the end-user to easily browse clustered alerts. The second component is based on the flocking behavior of birds such that birds tend to follow other birds with similar behaviors. This component allows the end-user to see the clustering process and provides an efficient means for reviewing alert data. The third component discovers and visualizes patterns of multistage attacks by profiling the attacker s behaviors.

A Comparative Study of Data Mining Algorithms for High Detection Rate in Intrusion Detection System

Directory of Open Access Journals (Sweden)

Nabeela Ashraf

2018-01-01

Full Text Available Due to the fast growth and tradition of the internet over the last decades, the network security problems are increasing vigorously. Humans can not handle the speed of processes and the huge amount of data required to handle network anomalies. Therefore, it needs substantial automation in both speed and accuracy. Intrusion Detection System is one of the approaches to recognize illegal access and rare attacks to secure networks. In this proposed paper, Naive Bayes, J48 and Random Forest classifiers are compared to compute the detection rate and accuracy of IDS. For experiments, the KDD_NSL dataset is used.

FSM-F: Finite State Machine Based Framework for Denial of Service and Intrusion Detection in MANET.

Science.gov (United States)

N Ahmed, Malik; Abdullah, Abdul Hanan; Kaiwartya, Omprakash

2016-01-01

Due to the continuous advancements in wireless communication in terms of quality of communication and affordability of the technology, the application area of Mobile Adhoc Networks (MANETs) significantly growing particularly in military and disaster management. Considering the sensitivity of the application areas, security in terms of detection of Denial of Service (DoS) and intrusion has become prime concern in research and development in the area. The security systems suggested in the past has state recognition problem where the system is not able to accurately identify the actual state of the network nodes due to the absence of clear definition of states of the nodes. In this context, this paper proposes a framework based on Finite State Machine (FSM) for denial of service and intrusion detection in MANETs. In particular, an Interruption Detection system for Adhoc On-demand Distance Vector (ID-AODV) protocol is presented based on finite state machine. The packet dropping and sequence number attacks are closely investigated and detection systems for both types of attacks are designed. The major functional modules of ID-AODV includes network monitoring system, finite state machine and attack detection model. Simulations are carried out in network simulator NS-2 to evaluate the performance of the proposed framework. A comparative evaluation of the performance is also performed with the state-of-the-art techniques: RIDAN and AODV. The performance evaluations attest the benefits of proposed framework in terms of providing better security for denial of service and intrusion detection attacks.

Multi-Sensor Mud Detection

Science.gov (United States)

Rankin, Arturo L.; Matthies, Larry H.

2010-01-01

Robust mud detection is a critical perception requirement for Unmanned Ground Vehicle (UGV) autonomous offroad navigation. A military UGV stuck in a mud body during a mission may have to be sacrificed or rescued, both of which are unattractive options. There are several characteristics of mud that may be detectable with appropriate UGV-mounted sensors. For example, mud only occurs on the ground surface, is cooler than surrounding dry soil during the daytime under nominal weather conditions, is generally darker than surrounding dry soil in visible imagery, and is highly polarized. However, none of these cues are definitive on their own. Dry soil also occurs on the ground surface, shadows, snow, ice, and water can also be cooler than surrounding dry soil, shadows are also darker than surrounding dry soil in visible imagery, and cars, water, and some vegetation are also highly polarized. Shadows, snow, ice, water, cars, and vegetation can all be disambiguated from mud by using a suite of sensors that span multiple bands in the electromagnetic spectrum. Because there are military operations when it is imperative for UGV's to operate without emitting strong, detectable electromagnetic signals, passive sensors are desirable. JPL has developed a daytime mud detection capability using multiple passive imaging sensors. Cues for mud from multiple passive imaging sensors are fused into a single mud detection image using a rule base, and the resultant mud detection is localized in a terrain map using range data generated from a stereo pair of color cameras.

Microwave Sensors for Breast Cancer Detection.

Science.gov (United States)

Wang, Lulu

2018-02-23

Breast cancer is the leading cause of death among females, early diagnostic methods with suitable treatments improve the 5-year survival rates significantly. Microwave breast imaging has been reported as the most potential to become the alternative or additional tool to the current gold standard X-ray mammography for detecting breast cancer. The microwave breast image quality is affected by the microwave sensor, sensor array, the number of sensors in the array and the size of the sensor. In fact, microwave sensor array and sensor play an important role in the microwave breast imaging system. Numerous microwave biosensors have been developed for biomedical applications, with particular focus on breast tumor detection. Compared to the conventional medical imaging and biosensor techniques, these microwave sensors not only enable better cancer detection and improve the image resolution, but also provide attractive features such as label-free detection. This paper aims to provide an overview of recent important achievements in microwave sensors for biomedical imaging applications, with particular focus on breast cancer detection. The electric properties of biological tissues at microwave spectrum, microwave imaging approaches, microwave biosensors, current challenges and future works are also discussed in the manuscript.

Assessing Human Activity in Elderly People Using Non-Intrusive Load Monitoring.

Science.gov (United States)

Alcalá, José M; Ureña, Jesús; Hernández, Álvaro; Gualda, David

2017-02-11

The ageing of the population, and their increasing wish of living independently, are motivating the development of welfare and healthcare models. Existing approaches based on the direct heath-monitoring using body sensor networks (BSN) are precise and accurate. Nonetheless, their intrusiveness causes non-acceptance. New approaches seek the indirect monitoring through monitoring activities of daily living (ADLs), which proves to be a suitable solution. ADL monitoring systems use many heterogeneous sensors, are less intrusive, and are less expensive than BSN, however, the deployment and maintenance of wireless sensor networks (WSN) prevent them from a widespread acceptance. In this work, a novel technique to monitor the human activity, based on non-intrusive load monitoring (NILM), is presented. The proposal uses only smart meter data, which leads to minimum intrusiveness and a potential massive deployment at minimal cost. This could be the key to develop sustainable healthcare models for smart homes, capable of complying with the elderly people' demands. This study also uses the Dempster-Shafer theory to provide a daily score of normality with regard to the regular behavior. This approach has been evaluated using real datasets and, additionally, a benchmarking against a Gaussian mixture model approach is presented.

Zero Trust Intrusion Containment for Telemedicine

National Research Council Canada - National Science Library

Sood, Arun

2002-01-01

.... Our objective is the design and analysis of 'zero-trust' Intrusion Tolerant Systems. These are systems built under the extreme assumption that all intrusion detection techniques will eventually fail...

Comparative study of adaptive-noise-cancellation algorithms for intrusion detection systems

International Nuclear Information System (INIS)

Claassen, J.P.; Patterson, M.M.

1981-01-01

Some intrusion detection systems are susceptible to nonstationary noise resulting in frequent nuisance alarms and poor detection when the noise is present. Adaptive inverse filtering for single channel systems and adaptive noise cancellation for two channel systems have both demonstrated good potential in removing correlated noise components prior detection. For such noise susceptible systems the suitability of a noise reduction algorithm must be established in a trade-off study weighing algorithm complexity against performance. The performance characteristics of several distinct classes of algorithms are established through comparative computer studies using real signals. The relative merits of the different algorithms are discussed in the light of the nature of intruder and noise signals

-Net Approach to Sensor -Coverage

Directory of Open Access Journals (Sweden)

Fusco Giordano

2010-01-01

Full Text Available Wireless sensors rely on battery power, and in many applications it is difficult or prohibitive to replace them. Hence, in order to prolongate the system's lifetime, some sensors can be kept inactive while others perform all the tasks. In this paper, we study the -coverage problem of activating the minimum number of sensors to ensure that every point in the area is covered by at least sensors. This ensures higher fault tolerance, robustness, and improves many operations, among which position detection and intrusion detection. The -coverage problem is trivially NP-complete, and hence we can only provide approximation algorithms. In this paper, we present an algorithm based on an extension of the classical -net technique. This method gives an -approximation, where is the number of sensors in an optimal solution. We do not make any particular assumption on the shape of the areas covered by each sensor, besides that they must be closed, connected, and without holes.

HPNAIDM: The High-Performance Network Anomaly/Intrusion Detection and Mitigation System

Energy Technology Data Exchange (ETDEWEB)

Chen, Yan [Northwesten University

2013-12-05

Identifying traffic anomalies and attacks rapidly and accurately is critical for large network operators. With the rapid growth of network bandwidth, such as the next generation DOE UltraScience Network, and fast emergence of new attacks/virus/worms, existing network intrusion detection systems (IDS) are insufficient because they: • Are mostly host-based and not scalable to high-performance networks; • Are mostly signature-based and unable to adaptively recognize flow-level unknown attacks; • Cannot differentiate malicious events from the unintentional anomalies. To address these challenges, we proposed and developed a new paradigm called high-performance network anomaly/intrustion detection and mitigation (HPNAIDM) system. The new paradigm is significantly different from existing IDSes with the following features (research thrusts). • Online traffic recording and analysis on high-speed networks; • Online adaptive flow-level anomaly/intrusion detection and mitigation; • Integrated approach for false positive reduction. Our research prototype and evaluation demonstrate that the HPNAIDM system is highly effective and economically feasible. Beyond satisfying the pre-set goals, we even exceed that significantly (see more details in the next section). Overall, our project harvested 23 publications (2 book chapters, 6 journal papers and 15 peer-reviewed conference/workshop papers). Besides, we built a website for technique dissemination, which hosts two system prototype release to the research community. We also filed a patent application and developed strong international and domestic collaborations which span both academia and industry.

Perancangan dan Implementasi Instrusion Detection System di Jaringan Universitas Diponegoro

Directory of Open Access Journals (Sweden)

Dyakso Anindito Nugroho

2015-04-01

Full Text Available The use of information technology gives the advantage of open access for its users, but a new problem arises that there is a threat from unauthorized users. Intrusion Detection System (IDS is applied to assist administrator to monitoring network security. IDS displays illegal access information in a raw form which is require more time to read the detected threats. This final project aims to design an IDS with web application which is made for pulling information on IDS sensor database, then processing and representing them in tables and graphs that are easy to understand. The web application also has IpTables firewall module to block attacker's IP address. The hardware used is Cisco IPS 4240, two computers Compaq Presario 4010F as client and gateway, and Cisco Catalyst 2960 switch. The software used is Ubuntu 12.0 LTS Precise operating system, BackTrack 5 R1 operating system, PHP 5.4 programming language, MySQL 5 database, and web-based system configuration tool Webmin. Testing is done using several BackTrack applications with the aim of Cisco IPS 4240 is capable of detecting accordance with the applicable rules. Each events of any attack attempt or threat was obtained from IDS sensor database in XML form. XML file is sent using Security Device Event Exchange (SDEE protocol. The web application is tested by looking at the output tables and graphs that displays the appropriate results of sensor detection. This study generated an intrusion detection system that is easier to monitor. Network packets copied by the Cisco 2960 switch and then forwarded to the sensor. Intruder detection is done by Cisco IPS 4240 sensor. Log detection processed by the web application into tables and graphs. Intrusion detection systems are intended to improve network security.

Patient Posture Monitoring System Based on Flexible Sensors

Directory of Open Access Journals (Sweden)

Youngsu Cha

2017-03-01

Full Text Available Monitoring patients using vision cameras can cause privacy intrusion problems. In this paper, we propose a patient position monitoring system based on a patient cloth with unobtrusive sensors. We use flexible sensors based on polyvinylidene fluoride, which is a flexible piezoelectric material. Theflexiblesensorsareinsertedintopartsclosetothekneeandhipoftheloosepatientcloth. We measure electrical signals from the sensors caused by the piezoelectric effect when the knee and hip in the cloth are bent. The measured sensor outputs are transferred to a computer via Bluetooth. We use a custom-made program to detect the position of the patient through a rule-based algorithm and the sensor outputs. The detectable postures are based on six human motions in and around a bed. The proposed system can detect the patient positions with a success rate over 88 percent for three patients.

FSM-F: Finite State Machine Based Framework for Denial of Service and Intrusion Detection in MANET.

Directory of Open Access Journals (Sweden)

Malik N Ahmed

Full Text Available Due to the continuous advancements in wireless communication in terms of quality of communication and affordability of the technology, the application area of Mobile Adhoc Networks (MANETs significantly growing particularly in military and disaster management. Considering the sensitivity of the application areas, security in terms of detection of Denial of Service (DoS and intrusion has become prime concern in research and development in the area. The security systems suggested in the past has state recognition problem where the system is not able to accurately identify the actual state of the network nodes due to the absence of clear definition of states of the nodes. In this context, this paper proposes a framework based on Finite State Machine (FSM for denial of service and intrusion detection in MANETs. In particular, an Interruption Detection system for Adhoc On-demand Distance Vector (ID-AODV protocol is presented based on finite state machine. The packet dropping and sequence number attacks are closely investigated and detection systems for both types of attacks are designed. The major functional modules of ID-AODV includes network monitoring system, finite state machine and attack detection model. Simulations are carried out in network simulator NS-2 to evaluate the performance of the proposed framework. A comparative evaluation of the performance is also performed with the state-of-the-art techniques: RIDAN and AODV. The performance evaluations attest the benefits of proposed framework in terms of providing better security for denial of service and intrusion detection attacks.

A survey of intrusion detection techniques in Cloud

OpenAIRE

Modi, C.; Patel, D.; Patel, H.; Borisaniya, B.; Patel, A.; Rajarajan, M.

2013-01-01

Cloud computing provides scalable, virtualized on-demand services to the end users with greater flexibility and lesser infrastructural investment. These services are provided over the Internet using known networking protocols, standards and formats under the supervision of different managements. Existing bugs and vulnerabilities in underlying technologies and legacy protocols tend to open doors for intrusion. This paper, surveys different intrusions affecting availability, confidentiality and...

A Novel Architecture for Intrusion Detection in Mobile Ad hoc Network

OpenAIRE

Atul Patel; Ruchi Kansara; Dr. Paresh Virparia

2011-01-01

Today’s wireless networks are vulnerable in many ways including illegal use, unauthorized access, denial of service attacks, eavesdropping so called war chalking. These problems are one of the main issues for wider uses of wireless network. On wired network intruder can access by wire but in wireless it has possibilities to access the computer anywhere in neighborhood. However, securing MANETs is highly challenging issue due to their inherent characteristics. Intrusion detection is an importa...

Creating a two-layered augmented artificial immune system for application to computer network intrusion detection

Science.gov (United States)

Judge, Matthew G.; Lamont, Gary B.

2009-05-01

Computer network security has become a very serious concern of commercial, industrial, and military organizations due to the increasing number of network threats such as outsider intrusions and insider covert activities. An important security element of course is network intrusion detection which is a difficult real world problem that has been addressed through many different solution attempts. Using an artificial immune system has been shown to be one of the most promising results. By enhancing jREMISA, a multi-objective evolutionary algorithm inspired artificial immune system, with a secondary defense layer; we produce improved accuracy of intrusion classification and a flexibility in responsiveness. This responsiveness can be leveraged to provide a much more powerful and accurate system, through the use of increased processing time and dedicated hardware which has the flexibility of being located out of band.

Assessing Human Activity in Elderly People Using Non-Intrusive Load Monitoring

Directory of Open Access Journals (Sweden)

José M. Alcalá

2017-02-01

Full Text Available The ageing of the population, and their increasing wish of living independently, are motivating the development of welfare and healthcare models. Existing approaches based on the direct heath-monitoring using body sensor networks (BSN are precise and accurate. Nonetheless, their intrusiveness causes non-acceptance. New approaches seek the indirect monitoring through monitoring activities of daily living (ADLs, which proves to be a suitable solution. ADL monitoring systems use many heterogeneous sensors, are less intrusive, and are less expensive than BSN, however, the deployment and maintenance of wireless sensor networks (WSN prevent them from a widespread acceptance. In this work, a novel technique to monitor the human activity, based on non-intrusive load monitoring (NILM, is presented. The proposal uses only smart meter data, which leads to minimum intrusiveness and a potential massive deployment at minimal cost. This could be the key to develop sustainable healthcare models for smart homes, capable of complying with the elderly people’ demands. This study also uses the Dempster-Shafer theory to provide a daily score of normality with regard to the regular behavior. This approach has been evaluated using real datasets and, additionally, a benchmarking against a Gaussian mixture model approach is presented.

A Real-Time Intrusion Detection System using Data Mining Technique

Directory of Open Access Journals (Sweden)

Fang-Yie Leu

2008-04-01

Full Text Available Presently, most computers authenticate user ID and password before users can login these systems. However, danger soon comes if the two items are known to hackers. In this paper, we propose a system, named Intrusion Detection and Identification System (IDIS, which builds a profile for each user in an intranet to keep track his/her usage habits as forensic features with which IDIS can identify who the underlying user in the intranet is. Our experimental results show that the recognition accuracy of students of computer science department is up to 98.99%.

Multimodal UAV detection: study of various intrusion scenarios

Science.gov (United States)

Hengy, Sebastien; Laurenzis, Martin; Schertzer, Stéphane; Hommes, Alexander; Kloeppel, Franck; Shoykhetbrod, Alex; Geibig, Thomas; Johannes, Winfried; Rassy, Oussama; Christnacher, Frank

2017-10-01

Small unmanned aerial vehicles (UAVs) are becoming increasingly popular and affordable the last years for professional and private consumer market, with varied capacities and performances. Recent events showed that illicit or hostile uses constitute an emergent, quickly evolutionary threat. Recent developments in UAV technologies tend to bring autonomous, highly agile and capable unmanned aerial vehicles to the market. These UAVs can be used for spying operations as well as for transporting illicit or hazardous material (smuggling, flying improvised explosive devices). The scenario of interest concerns the protection of sensitive zones against the potential threat constituted by small drones. In the recent past, field trials were carried out to investigate the detection and tracking of multiple UAV flying at low altitude. Here, we present results which were achieved using a heterogeneous sensor network consisting of acoustic antennas, small FMCW RADAR systems and optical sensors. While acoustics and RADAR was applied to monitor a wide azimuthal area (360°), optical sensors were used for sequentially identification. The localization results have been compared to the ground truth data to estimate the efficiency of each detection system. Seven-microphone acoustic arrays allow single source localization. The mean azimuth and elevation estimation error has been measured equal to 1.5 and -2.5 degrees respectively. The FMCW radar allows tracking of multiple UAVs by estimating their range, azimuth and motion speed. Both technologies can be linked to the electro-optical system for final identification of the detected object.

Corrosion detection of nanowires by magnetic sensors

KAUST Repository

Kosel, Jü rgen; Amara, Selma; Ivanov, Iurii; Blanco, Mario

2017-01-01

Disclosed are various embodiments related to a corrosion detection device for detecting corrosive environments. A corrosion detection device comprises a magnetic sensor and at least one magnetic nanowire disposed on the magnetic sensor. The magnetic sensor is configured to detect corrosion of the one or more magnetic nanowires based at least in part on a magnetic field of the one or more magnetic nanowires.

Corrosion detection of nanowires by magnetic sensors

KAUST Repository

Kosel, Jürgen

2017-10-05

Disclosed are various embodiments related to a corrosion detection device for detecting corrosive environments. A corrosion detection device comprises a magnetic sensor and at least one magnetic nanowire disposed on the magnetic sensor. The magnetic sensor is configured to detect corrosion of the one or more magnetic nanowires based at least in part on a magnetic field of the one or more magnetic nanowires.

Idaho National Laboratory Supervisory Control and Data Acquisition Intrusion Detection System (SCADA IDS)

Energy Technology Data Exchange (ETDEWEB)

Jared Verba; Michael Milvich

2008-05-01

Current Intrusion Detection System (IDS) technology is not suited to be widely deployed inside a Supervisory, Control and Data Acquisition (SCADA) environment. Anomaly- and signature-based IDS technologies have developed methods to cover information technology-based networks activity and protocols effectively. However, these IDS technologies do not include the fine protocol granularity required to ensure network security inside an environment with weak protocols lacking authentication and encryption. By implementing a more specific and more intelligent packet inspection mechanism, tailored traffic flow analysis, and unique packet tampering detection, IDS technology developed specifically for SCADA environments can be deployed with confidence in detecting malicious activity.

The electrical self-potential method is a non-intrusive snow-hydrological sensor

Science.gov (United States)

Thompson, S. S.; Kulessa, B.; Essery, R. L. H.; Lüthi, M. P.

2015-08-01

Our ability to measure, quantify and assimilate hydrological properties and processes of snow in operational models is disproportionally poor compared to the significance of seasonal snowmelt as a global water resource and major risk factor in flood and avalanche forecasting. Encouraged by recent theoretical, modelling and laboratory work, we show here that the diurnal evolution of aerially-distributed self-potential magnitudes closely track those of bulk meltwater fluxes in melting in-situ snowpacks at Rhone and Jungfraujoch glaciers, Switzerland. Numerical modelling infers temporally-evolving liquid water contents in the snowpacks on successive days in close agreement with snow-pit measurements. Muting previous concerns, the governing physical and chemical properties of snow and meltwater became temporally invariant for modelling purposes. Because measurement procedure is straightforward and readily automated for continuous monitoring over significant spatial scales, we conclude that the self-potential geophysical method is a highly-promising non-intrusive snow-hydrological sensor for measurement practice, modelling and operational snow forecasting.

CRITICAL INFORMATION INFRASTRUCTURE SECURITY - NETWORK INTRUSION DETECTION SYSTEMS

Directory of Open Access Journals (Sweden)

Cristea DUMITRU

2011-12-01

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

Description, operation, and diagnostic routines for the adaptive intrusion data system

International Nuclear Information System (INIS)

Corlis, N.E.; Johnson, C.S.

1978-03-01

An Adaptive Intrusion Data System (AIDS) was developed to collect data from intrusion alarm sensors as part of an evaluation system to improve sensor performance. AIDS is a unique digital data compression, storage, and formatting system. It also incorporates a capability for video selection and recording for assessment of the sensors monitored by the system. The system is software reprogrammable to numerous configurations that may be utilized for the collection of environmental, bi-metal, analog, and video data. This manual covers the procedures for operating AIDS. Instructions are given to guide the operator in software programming and control option selections required to program AIDS for data collection. Software diagnostic programs are included in this manual as a method of isolating system problems

Surface Embedded Metal Oxide Sensors (SEMOS)

DEFF Research Database (Denmark)

Jespersen, Jesper Lebæk; Talat Ali, Syed; Pleth Nielsen, Lars

SEMOS is a joint project between Aalborg University, Danish Technological Institute and Danish Technical University in which micro temperature sensors and metal oxide-based gas sensors are developed and tested in a simulated fuel cell environment as well as in actual working fuel cells. Initially......, sensors for measuring the temperatures in an operating HT-PEM (High Temperature-Proton Exchange Membrane) fuel cell are developed for detecting in-plane temperature variations. 5 different tracks for embedded thermal sensors are investigated. The fuel cell MEA (Membrane Electrode Assembly) is quite...... complex and sensors are not easily implemented in the construction. Hence sensor interface and sensor position must therefore be chosen carefully in order to make the sensors as non-intrusive as possible. Metal Oxide Sensors (MOX) for measuring H2, O2 and CO concentration in a fuel cell environment...

A Hypergraph and Arithmetic Residue-based Probabilistic Neural Network for classification in Intrusion Detection Systems.

Science.gov (United States)

Raman, M R Gauthama; Somu, Nivethitha; Kirthivasan, Kannan; Sriram, V S Shankar

2017-08-01

Over the past few decades, the design of an intelligent Intrusion Detection System (IDS) remains an open challenge to the research community. Continuous efforts by the researchers have resulted in the development of several learning models based on Artificial Neural Network (ANN) to improve the performance of the IDSs. However, there exists a tradeoff with respect to the stability of ANN architecture and the detection rate for less frequent attacks. This paper presents a novel approach based on Helly property of Hypergraph and Arithmetic Residue-based Probabilistic Neural Network (HG AR-PNN) to address the classification problem in IDS. The Helly property of Hypergraph was exploited for the identification of the optimal feature subset and the arithmetic residue of the optimal feature subset was used to train the PNN. The performance of HG AR-PNN was evaluated using KDD CUP 1999 intrusion dataset. Experimental results prove the dominance of HG AR-PNN classifier over the existing classifiers with respect to the stability and improved detection rate for less frequent attacks. Copyright © 2017 Elsevier Ltd. All rights reserved.

Hybrid Modified K-Means with C4.5 for Intrusion Detection Systems in Multiagent Systems.

Science.gov (United States)

Laftah Al-Yaseen, Wathiq; Ali Othman, Zulaiha; Ahmad Nazri, Mohd Zakree

2015-01-01

Presently, the processing time and performance of intrusion detection systems are of great importance due to the increased speed of traffic data networks and a growing number of attacks on networks and computers. Several approaches have been proposed to address this issue, including hybridizing with several algorithms. However, this paper aims at proposing a hybrid of modified K-means with C4.5 intrusion detection system in a multiagent system (MAS-IDS). The MAS-IDS consists of three agents, namely, coordinator, analysis, and communication agent. The basic concept underpinning the utilized MAS is dividing the large captured network dataset into a number of subsets and distributing these to a number of agents depending on the data network size and core CPU availability. KDD Cup 1999 dataset is used for evaluation. The proposed hybrid modified K-means with C4.5 classification in MAS is developed in JADE platform. The results show that compared to the current methods, the MAS-IDS reduces the IDS processing time by up to 70%, while improving the detection accuracy.

The Use of Artificial-Intelligence-Based Ensembles for Intrusion Detection: A Review

Directory of Open Access Journals (Sweden)

Gulshan Kumar

2012-01-01

Full Text Available In supervised learning-based classification, ensembles have been successfully employed to different application domains. In the literature, many researchers have proposed different ensembles by considering different combination methods, training datasets, base classifiers, and many other factors. Artificial-intelligence-(AI- based techniques play prominent role in development of ensemble for intrusion detection (ID and have many benefits over other techniques. However, there is no comprehensive review of ensembles in general and AI-based ensembles for ID to examine and understand their current research status to solve the ID problem. Here, an updated review of ensembles and their taxonomies has been presented in general. The paper also presents the updated review of various AI-based ensembles for ID (in particular during last decade. The related studies of AI-based ensembles are compared by set of evaluation metrics driven from (1 architecture & approach followed; (2 different methods utilized in different phases of ensemble learning; (3 other measures used to evaluate classification performance of the ensembles. The paper also provides the future directions of the research in this area. The paper will help the better understanding of different directions in which research of ensembles has been done in general and specifically: field of intrusion detection systems (IDSs.

A Fusion of Multiagent Functionalities for Effective Intrusion Detection System

Directory of Open Access Journals (Sweden)

Dhanalakshmi Krishnan Sadhasivan

2017-01-01

Full Text Available Provision of high security is one of the active research areas in the network applications. The failure in the centralized system based on the attacks provides less protection. Besides, the lack of update of new attacks arrival leads to the minimum accuracy of detection. The major focus of this paper is to improve the detection performance through the adaptive update of attacking information to the database. We propose an Adaptive Rule-Based Multiagent Intrusion Detection System (ARMA-IDS to detect the anomalies in the real-time datasets such as KDD and SCADA. Besides, the feedback loop provides the necessary update of attacks in the database that leads to the improvement in the detection accuracy. The combination of the rules and responsibilities for multiagents effectively detects the anomaly behavior, misuse of response, or relay reports of gas/water pipeline data in KDD and SCADA, respectively. The comparative analysis of the proposed ARMA-IDS with the various existing path mining methods, namely, random forest, JRip, a combination of AdaBoost/JRip, and common path mining on the SCADA dataset conveys that the effectiveness of the proposed ARMA-IDS in the real-time fault monitoring. Moreover, the proposed ARMA-IDS offers the higher detection rate in the SCADA and KDD cup 1999 datasets.

Electrochemical sensor for detection of carcinoma

International Nuclear Information System (INIS)

Thakur, Bhawana; Sawant, Shilpa N.; Jayakumar, S.

2012-01-01

Detection of carcinoma in early stage is very important for its effective treatment. Although considerable advancement has been made in its detection and treatment, there is a significant need for rapid, low-cost, sensitive, and selective biosensors for detection of cancer. In recent years, electrochemical detection techniques have received much attention due to their rapid response, high sensitivity, and inherent selectivity. They can provide an inexpensive platform for detection of analytes in clinical diagnostics. Conducting polymers are a versatile material for development of electrochemical biosensors. Due to the conducting nature of these polymers, they act as a transducer to convert the biological signal into electrical signal. These polymers also exhibit good biocompatibility, hence are ideal for immobilisation of biological recognition element during the development of the sensor film. Recently author have demonstrated a whole cell based electrochemical biosensor for detection of the pesticide Lindane at very low concentrations. In the present study, we have tried to develop polyaniline based electrochemical sensor for detection of carcinoma. Polyaniline was deposited on gold interdigitated electrodes by electropolymerization using potentiodynamic method. The polymer film was suitably modified to obtain the sensor film for recognition of the tumour cells. Response of the sensor to various tumour cells such as lung cancer cells, human fibrosarcoma cells, prostate cancer cells, breast cancer cells was studied and was compared to that of normal cells. The sensor electrode could detect tumour cells based on the nature of response obtained

Anomaly detection in smart city wireless sensor networks

OpenAIRE

Garcia Font, Víctor

2017-01-01

Aquesta tesi proposa una plataforma de detecció d’intrusions per a revelar atacs a les xarxes de sensors sense fils (WSN, per les sigles en anglès) de les ciutats intel·ligents (smart cities). La plataforma està dissenyada tenint en compte les necessitats dels administradors de la ciutat intel·ligent, els quals necessiten accés a una arquitectura centralitzada que pugui gestionar alarmes de seguretat en un sistema altament heterogeni i distribuït. En aquesta tesi s’identifiquen els diversos p...

Anomaly detection in smart city wireless sensor networks

OpenAIRE

García Font, Víctor

2017-01-01

Aquesta tesi proposa una plataforma de detecció d'intrusions per a revelar atacs a les xarxes de sensors sense fils (WSN, per les sigles en anglès) de les ciutats intel·ligents (smart cities). La plataforma està dissenyada tenint en compte les necessitats dels administradors de la ciutat intel·ligent, els quals necessiten accés a una arquitectura centralitzada que pugui gestionar alarmes de seguretat en un sistema altament heterogeni i distribuït. En aquesta tesi s'identifiquen els diversos p...

Sensor Anomaly Detection in Wireless Sensor Networks for Healthcare

Science.gov (United States)

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

2015-01-01

Wireless Sensor Networks (WSN) are vulnerable to various sensor faults and faulty measurements. This vulnerability hinders efficient and timely response in various WSN applications, such as healthcare. For example, faulty measurements can create false alarms which may require unnecessary intervention from healthcare personnel. Therefore, an approach to differentiate between real medical conditions and false alarms will improve remote patient monitoring systems and quality of healthcare service afforded by WSN. In this paper, a novel approach is proposed to detect sensor anomaly by analyzing collected physiological data from medical sensors. The objective of this method is to effectively distinguish false alarms from true alarms. It predicts a sensor value from historic values and compares it with the actual sensed value for a particular instance. The difference is compared against a threshold value, which is dynamically adjusted, to ascertain whether the sensor value is anomalous. The proposed approach has been applied to real healthcare datasets and compared with existing approaches. Experimental results demonstrate the effectiveness of the proposed system, providing high Detection Rate (DR) and low False Positive Rate (FPR). PMID:25884786

Cannula sensor for nitric oxide detection

Energy Technology Data Exchange (ETDEWEB)

Glazier, S.A. [National Institute of Standard and Technology, Gaithersburg, MD (United States)

1995-12-31

Nitric oxide (NO) has received much attention because of its numerous roles in mammalian systems. It has been found in the brain and nervous system to act as a neurotransmitter, in blood vessels as a blood pressure regulator, in the immune system to act as a bactericide and tumorcide, and in other postulated roles as well. Nitric oxide is produced in mammalian cells by the enzyme nitric oxide synthetase. Once produced, NO is oxidized or reacts rapidly with components in living systems and hence has a short half-life. Only a few sensors have been constructed which can detect NO at nanomolar to micromolar levels found in these systems. We are currently examining the use of a cannula sensor employing oxyhemoglobin for NO detection. This sensor continuously draws in liquid sample at a low rate and immediately reacts it with oxyhemoglobin. The absorbance changes which accompany the reaction are monitored. The sensor has a linear response range from approximately 50 to 1000 nM of NO in aqueous solution. Its utility in monitoring NO produced by stimulated murine macrophage cells (RAW 264.7) in culture is currently being examined. The sensor design is generic in that it can also employ fluorescence and chemiluminescence detection chemistries which may allow lower detection limits to be achieved. Details of the sensor`s performance will be given.

APTAMER-BASED SERRS SENSOR FOR THROMBIN DETECTION

Energy Technology Data Exchange (ETDEWEB)

Cho, H; Baker, B R; Wachsmann-Hogiu, S; Pagba, C V; Laurence, T A; Lane, S M; Lee, L P; Tok, J B

2008-07-02

We describe an aptamer-based Surface Enhanced Resonance Raman Scattering (SERRS) sensor with high sensitivity, specificity, and stability for the detection of a coagulation protein, human a-thrombin. The sensor achieves high sensitivity and a limit of detection of 100 pM by monitoring the SERRS signal change upon the single step of thrombin binding to immobilized thrombin binding aptamer. The selectivity of the sensor is demonstrated by the specific discrimination of thrombin from other protein analytes. The specific recognition and binding of thrombin by the thrombin binding aptamer is essential to the mechanism of the aptamer-based sensor, as shown through measurements using negative control oligonucleotides. In addition, the sensor can detect 1 nM thrombin in the presence of complex biofluids, such as 10% fetal calf serum, demonstrating that the immobilized, 5{prime}-capped, 3{prime}-capped aptamer is sufficiently robust for clinical diagnostic applications. Furthermore, the proposed sensor may be implemented for multiplexed detection using different aptamer-Raman probe complexes.

Towards Reliable Evaluation of Anomaly-Based Intrusion Detection Performance

Science.gov (United States)

Viswanathan, Arun

2012-01-01

This report describes the results of research into the effects of environment-induced noise on the evaluation process for anomaly detectors in the cyber security domain. This research was conducted during a 10-week summer internship program from the 19th of August, 2012 to the 23rd of August, 2012 at the Jet Propulsion Laboratory in Pasadena, California. The research performed lies within the larger context of the Los Angeles Department of Water and Power (LADWP) Smart Grid cyber security project, a Department of Energy (DoE) funded effort involving the Jet Propulsion Laboratory, California Institute of Technology and the University of Southern California/ Information Sciences Institute. The results of the present effort constitute an important contribution towards building more rigorous evaluation paradigms for anomaly-based intrusion detectors in complex cyber physical systems such as the Smart Grid. Anomaly detection is a key strategy for cyber intrusion detection and operates by identifying deviations from profiles of nominal behavior and are thus conceptually appealing for detecting "novel" attacks. Evaluating the performance of such a detector requires assessing: (a) how well it captures the model of nominal behavior, and (b) how well it detects attacks (deviations from normality). Current evaluation methods produce results that give insufficient insight into the operation of a detector, inevitably resulting in a significantly poor characterization of a detectors performance. In this work, we first describe a preliminary taxonomy of key evaluation constructs that are necessary for establishing rigor in the evaluation regime of an anomaly detector. We then focus on clarifying the impact of the operational environment on the manifestation of attacks in monitored data. We show how dynamic and evolving environments can introduce high variability into the data stream perturbing detector performance. Prior research has focused on understanding the impact of this

Non-intrusive, fast and sensitive ammonia detection by laser photothermal deflection

International Nuclear Information System (INIS)

Vries, H.S.M. de; Harren, F.J.M.; Wyers, G.P.; Otjes, R.P.; Slanina, J.; Reuss, J.

1995-01-01

A recently developed non-intrusive photothermal deflection (PTD) instrument allows sensitive, rapid and quantitative detection of local ammonia concentrations in the air. Ammonia is vibrationally excited by an infrared CO 2 laser in an intracavity configuration. A HeNe beam passing over the CO 2 laser beam (multipass arrangement) is deflected by the induced refractive index gradient. The detection limit for ammonia in ambient air is 0.5 ppbv with a spatial resolution of a few mm 3 . The time resolution is 0.1 s (single line) or 15 s (multi line). The system is fully automated and suited for non-stop measuring periods of at least one week. Results were compared to those obtained with a continuous-flow denuder (CFD). (author)

Accurate Modeling of The Siemens S7 SCADA Protocol For Intrusion Detection And Digital Forensic

Directory of Open Access Journals (Sweden)

Amit Kleinmann

2014-09-01

Full Text Available The Siemens S7 protocol is commonly used in SCADA systems for communications between a Human Machine Interface (HMI and the Programmable Logic Controllers (PLCs. This paper presents a model-based Intrusion Detection Systems (IDS designed for S7 networks. The approach is based on the key observation that S7 traffic to and from a specific PLC is highly periodic; as a result, each HMI-PLC channel can be modeled using its own unique Deterministic Finite Automaton (DFA. The resulting DFA-based IDS is very sensitive and is able to flag anomalies such as a message appearing out of its position in the normal sequence or a message referring to a single unexpected bit. The intrusion detection approach was evaluated on traffic from two production systems. Despite its high sensitivity, the system had a very low false positive rate - over 99.82% of the traffic was identified as normal.

A new rechargeable intelligent vehicle detection sensor

International Nuclear Information System (INIS)

Lin, L; Han, X B; Ding, R; Li, G; Lu, Steven C-Y; Hong, Q

2005-01-01

Intelligent Transportation System (ITS) is a valid approach to solve the increasing transportation issue in cities. Vehicle detection is one of the key technologies in ITS. The ITS collects and processes traffic data (vehicle flow, vehicular speed, vehicle density and occupancy ratios) from vehicle detection sensors buried under the road or installed along the road. Inductive loop detector as one type of the vehicle detector is applied extensively, with the characters of stability, high value to cost ratio and feasibility. On the other hand, most of the existing inductive loop vehicle detection sensors have some weak points such as friability of detective loop, huge engineering for setting and traffic interruption during installing the sensor. The design and reality of a new rechargeable intelligent vehicle detection sensor is presented in this paper against these weak points existing now. The sensor consists of the inductive loop detector, the rechargeable batteries, the MCU (microcontroller) and the transmitter. In order to reduce the installing project amount, make the loop durable and easily maintained, the volume of the detective loop is reduced as much as we can. Communication in RF (radio frequency) brings on the advantages of getting rid of the feeder cable completely and reducing the installing project amount enormously. For saving the cable installation, the sensor is supplied by the rechargeable batteries. The purpose of the intelligent management of the energy and transmitter by means of MCU is to minimize the power consumption and prolong the working period of the sensor. In a word, the new sensor is more feasible with smaller volume, wireless communication, rechargeable batteries, low power consumption, low cost, high detector precision and easy maintenance and installation

A new rechargeable intelligent vehicle detection sensor

Energy Technology Data Exchange (ETDEWEB)

Lin, L [Inspiring Technology Research Laboratory, Tianjin University, Tianjin 300072 (China); Han, X B [Inspiring Technology Research Laboratory, Tianjin University, Tianjin 300072 (China); Ding, R [Tianjin University of Technology and Education, Tianjin 300222 (China); Li, G [Inspiring Technology Research Laboratory, Tianjin University, Tianjin 300072 (China); Lu, Steven C-Y [Inspiring Technology Research Laboratory, Tianjin University, Tianjin 300072 (China); Hong, Q [Inspiring Technology Research Laboratory, Tianjin University, Tianjin 300072 (China)

2005-01-01

Intelligent Transportation System (ITS) is a valid approach to solve the increasing transportation issue in cities. Vehicle detection is one of the key technologies in ITS. The ITS collects and processes traffic data (vehicle flow, vehicular speed, vehicle density and occupancy ratios) from vehicle detection sensors buried under the road or installed along the road. Inductive loop detector as one type of the vehicle detector is applied extensively, with the characters of stability, high value to cost ratio and feasibility. On the other hand, most of the existing inductive loop vehicle detection sensors have some weak points such as friability of detective loop, huge engineering for setting and traffic interruption during installing the sensor. The design and reality of a new rechargeable intelligent vehicle detection sensor is presented in this paper against these weak points existing now. The sensor consists of the inductive loop detector, the rechargeable batteries, the MCU (microcontroller) and the transmitter. In order to reduce the installing project amount, make the loop durable and easily maintained, the volume of the detective loop is reduced as much as we can. Communication in RF (radio frequency) brings on the advantages of getting rid of the feeder cable completely and reducing the installing project amount enormously. For saving the cable installation, the sensor is supplied by the rechargeable batteries. The purpose of the intelligent management of the energy and transmitter by means of MCU is to minimize the power consumption and prolong the working period of the sensor. In a word, the new sensor is more feasible with smaller volume, wireless communication, rechargeable batteries, low power consumption, low cost, high detector precision and easy maintenance and installation.

Preliminary experimental results for a non-intrusive scheme for the detection of flaws in metal pipelines

Science.gov (United States)

Aydin, K.; Shinde, S.; Suhail, M.; Vyas, A.; Zieher, K. W.

2002-05-01

An acoustic pulse echo scheme for non-intrusive detection of flaws in metal pipelines has been investigated in the laboratory. The primary pulse is generated by a pulsed magnetic field enclosing a short section of a free pipe. The detection is by an electrostatic detector surrounding a short section of the pipe. Reflected pulses from thin areas, with a longitudinal extension of about one pipe radius and a reduction of the wall thickness of 40%, can be detected clearly.

A Targeted Attack For Enhancing Resiliency of Intelligent Intrusion Detection Modules in Energy Cyber Physical Systems

Energy Technology Data Exchange (ETDEWEB)

Youssef, Tarek [Florida Intl Univ., Miami, FL (United States); El Hariri, Mohammad [Florida Intl Univ., Miami, FL (United States); Habib, Hani [Florida Intl Univ., Miami, FL (United States); Mohammed, Osama [Florida Intl Univ., Miami, FL (United States); Harmon, E [Florida Intl Univ., Miami, FL (United States)

2017-02-28

Abstract— Secure high-speed communication is required to ensure proper operation of complex power grid systems and prevent malicious tampering activities. In this paper, artificial neural networks with temporal dependency are introduced for false data identification and mitigation for broadcasted IEC 61850 SMV messages. The fast responses of such intelligent modules in intrusion detection make them suitable for time- critical applications, such as protection. However, care must be taken in selecting the appropriate intelligence model and decision criteria. As such, this paper presents a customizable malware script to sniff and manipulate SMV messages and demonstrates the ability of the malware to trigger false positives in the neural network’s response. The malware developed is intended to be as a vaccine to harden the intrusion detection system against data manipulation attacks by enhancing the neural network’s ability to learn and adapt to these attacks.

Development of compact slip detection sensor using dielectric elastomer

Science.gov (United States)

Choi, Jae-young; Hwang, Do-Yeon; Kim, Baek-chul; Moon, Hyungpil; Choi, Hyouk Ryeol; Koo, Ja Choon

2015-04-01

In this paper, we developed a resistance tactile sensor that can detect a slip on the surface of sensor structure. The presented sensor device has fingerprint-like structures that are similar with the role of the humans finger print. The resistance slip sensor that the novel developed uses acrylo-nitrile butadiene rubber (NBR) as a dielectric substrate and graphene as an electrode material. We can measure the slip as the structure of sensor makes a deformation and it changes the resistance through forming a new conductive route. To manufacture our sensor, we developed a new imprint process. By using this process, we can produce sensor with micro unit structure. To verify effectiveness of the proposed slip detection, experiment using prototype of resistance slip sensor is conducted with an algorithm to detect slip and slip is successfully detected. We will discuss the slip detection properties.

Sensor Fault Detection and Diagnosis for autonomous vehicles

Directory of Open Access Journals (Sweden)

Realpe Miguel

2015-01-01

Full Text Available In recent years testing autonomous vehicles on public roads has become a reality. However, before having autonomous vehicles completely accepted on the roads, they have to demonstrate safe operation and reliable interaction with other traffic participants. Furthermore, in real situations and long term operation, there is always the possibility that diverse components may fail. This paper deals with possible sensor faults by defining a federated sensor data fusion architecture. The proposed architecture is designed to detect obstacles in an autonomous vehicle’s environment while detecting a faulty sensor using SVM models for fault detection and diagnosis. Experimental results using sensor information from the KITTI dataset confirm the feasibility of the proposed architecture to detect soft and hard faults from a particular sensor.

DFCL: DYNAMIC FUZZY LOGIC CONTROLLER FOR INTRUSION DETECTION

Directory of Open Access Journals (Sweden)

Abdulrahim Haroun Ali

2014-08-01

Full Text Available Intrusions are a problem with the deployment of Networks which give misuse and abnormal behavior in running reliable network operations and services. In this work, a Dynamic Fuzzy Logic Controller (DFLC is proposed for an anomaly detection problem, with the aim of solving the problem of attack detection rate and faster response process. Data is collected by PingER project. PingER project actively measures the worldwide Internet’s end-to-end performance. It covers over 168 countries around the world. PingER uses simple ubiquitous Internet Ping facility to calculate number of useful performance parameters. From each set of 10 pings between a monitoring host and a remote host, the features being calculated include Minimum Round Trip Time (RTT, Jitter, Packet loss, Mean Opinion Score (MOS, Directness of Connection (Alpha, Throughput, ping unpredictability and ping reachability. A set of 10 pings is being sent from the monitoring node to the remote node every 30 minutes. The received data shows the current characteristic and behavior of the networks. Any changes in the received data signify the existence of potential threat or abnormal behavior. D-FLC uses the combination of parameters as an input to detect the existence of any abnormal behavior of the network. The proposed system is simulated in Matlab Simulink environment. Simulations results show that the system managed to catch 95% of the anomalies with the ability to distinguish normal and abnormal behavior of the network.

Sensor fusion for intelligent alarm analysis

International Nuclear Information System (INIS)

Nelson, C.L.; Fitzgerald, D.S.

1996-01-01

The purpose of an intelligent alarm analysis system is to provide complete and manageable information to a central alarm station operator by applying alarm processing and fusion techniques to sensor information. This paper discusses the sensor fusion approach taken to perform intelligent alarm analysis for the Advanced Exterior Sensor (AES). The AES is an intrusion detection and assessment system designed for wide-area coverage, quick deployment, low false/nuisance alarm operation, and immediate visual assessment. It combines three sensor technologies (visible, infrared, and millimeter wave radar) collocated on a compact and portable remote sensor module. The remote sensor module rotates at a rate of 1 revolution per second to detect and track motion and provide assessment in a continuous 360 degree field-of-regard. Sensor fusion techniques are used to correlate and integrate the track data from these three sensors into a single track for operator observation. Additional inputs to the fusion process include environmental data, knowledge of sensor performance under certain weather conditions, sensor priority, and recent operator feedback. A confidence value is assigned to the track as a result of the fusion process. This helps to reduce nuisance alarms and to increase operator confidence in the system while reducing the workload of the operator

Anomaly Detection Based on Sensor Data in Petroleum Industry Applications

Directory of Open Access Journals (Sweden)

Luis Martí

2015-01-01

Full Text Available Anomaly detection is the problem of finding patterns in data that do not conform to an a priori expected behavior. This is related to the problem in which some samples are distant, in terms of a given metric, from the rest of the dataset, where these anomalous samples are indicated as outliers. Anomaly detection has recently attracted the attention of the research community, because of its relevance in real-world applications, like intrusion detection, fraud detection, fault detection and system health monitoring, among many others. Anomalies themselves can have a positive or negative nature, depending on their context and interpretation. However, in either case, it is important for decision makers to be able to detect them in order to take appropriate actions. The petroleum industry is one of the application contexts where these problems are present. The correct detection of such types of unusual information empowers the decision maker with the capacity to act on the system in order to correctly avoid, correct or react to the situations associated with them. In that application context, heavy extraction machines for pumping and generation operations, like turbomachines, are intensively monitored by hundreds of sensors each that send measurements with a high frequency for damage prevention. In this paper, we propose a combination of yet another segmentation algorithm (YASA, a novel fast and high quality segmentation algorithm, with a one-class support vector machine approach for efficient anomaly detection in turbomachines. The proposal is meant for dealing with the aforementioned task and to cope with the lack of labeled training data. As a result, we perform a series of empirical studies comparing our approach to other methods applied to benchmark problems and a real-life application related to oil platform turbomachinery anomaly detection.

Molecular detection by active Fano-sensor

Energy Technology Data Exchange (ETDEWEB)

Tao, Yifei; Guo, Zhongyi [School of Computer and Information, Hefei University of Technology, Hefei, 230009 (China)

2017-04-15

The optical properties and sensing performances of the molecular sensors based on plasmonic Fano-resonance (PFR) nanostructures have been numerically investigated in detail. The on-resonance sensor, in which the Fano-resonance position is overlapping with the absorption-band of the detected molecules perfectly, reveals a powerful ability to detect the molecules with a low concentration or thin thickness. By the bias-modulation of a single-layer graphene, the Fano-resonance position of the nanostructures can be tuned effectively. On being modulated properly, the PFR sensor shows an ultrahigh performance because of the unprecedentedly high overlap of the Fano-resonance position with the absorption-band of molecules, which is enabling superior signal strength in the molecular detections based on their vibrational fingerprints. Our proposed strategy may enable the development of dynamic sensors and open exciting prospects for bio-sensing. (copyright 2017 by WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim)

Miniaturized remission sensor for carbon dioxide detection

International Nuclear Information System (INIS)

Martan, T; Will, M

2010-01-01

Recently, optical sensors for detection of carbon dioxide (CO 2 ) have been explored for variety of applications in chemistry, industry, and medicine. This paper deals with the development of a planar optical remission sensor employing a dye immobilized in a polymer layer designed for gaseous CO 2 detection. The principle of CO 2 detection was based on colour changes of Tetraethylammonium Cresol red immobilized in a special composed polymer layer that was irradiated by LED diodes. Absorption properties of the dye were changed due to its chemical reaction with CO 2 and corresponding colour changes were detected by PIN diodes. These changes were analyzed by using a PC-controlled board connected by USB. The sensitivity, response time, and the detection limit of the remission sensor were characterized.

A Metrics-Based Approach to Intrusion Detection System Evaluation for Distributed Real-Time Systems

Science.gov (United States)

2002-04-01

Based Approach to Intrusion Detection System Evaluation for Distributed Real - Time Systems Authors: G. A. Fink, B. L. Chappell, T. G. Turner, and...Distributed, Security. 1 Introduction Processing and cost requirements are driving future naval combat platforms to use distributed, real - time systems of...distributed, real - time systems . As these systems grow more complex, the timing requirements do not diminish; indeed, they may become more constrained

Detection principles of biological and chemical FET sensors.

Science.gov (United States)

Kaisti, Matti

2017-12-15

The seminal importance of detecting ions and molecules for point-of-care tests has driven the search for more sensitive, specific, and robust sensors. Electronic detection holds promise for future miniaturized in-situ applications and can be integrated into existing electronic manufacturing processes and technology. The resulting small devices will be inherently well suited for multiplexed and parallel detection. In this review, different field-effect transistor (FET) structures and detection principles are discussed, including label-free and indirect detection mechanisms. The fundamental detection principle governing every potentiometric sensor is introduced, and different state-of-the-art FET sensor structures are reviewed. This is followed by an analysis of electrolyte interfaces and their influence on sensor operation. Finally, the fundamentals of different detection mechanisms are reviewed and some detection schemes are discussed. In the conclusion, current commercial efforts are briefly considered. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Wireless Sensor Networks for Detection of IED Emplacement

Science.gov (United States)

2009-06-01

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

Optical detection system for MEMS-type pressure sensor

International Nuclear Information System (INIS)

Sareło, K; Górecka-Drzazga, A; Dziuban, J A

2015-01-01

In this paper a special optical detection system designed for a MEMS-type (micro-electro-mechanical system) silicon pressure sensor is presented. The main part of the optical system—a detection unit with a perforated membrane—is bonded to the silicon sensor, and placed in a measuring system. An external light source illuminates the membrane of the pressure sensor. Owing to the light reflected from the deflected membrane sensor, the optical pattern consisting of light points is visible, and pressure can be estimated. The optical detection unit (20   ×   20   ×   20.4 mm 3 ) is fabricated using microengineering techniques. Its dimensions are adjusted to the dimensions of the pressure sensor (5   ×   5 mm 2 silicon membrane). Preliminary tests of the optical detection unit integrated with the silicon pressure sensor are carried out. For the membrane sensor from 15 to 60 µm thick, a repeatable detection of the differential pressure in the range of 0 to 280 kPa is achieved. The presented optical microsystem is especially suitable for the pressure measurements in a high radiation environment. (paper)

Finite State Machine Analysis of Remote Sensor Data

International Nuclear Information System (INIS)

Barbson, John M.

1999-01-01

The use of unattended monitoring systems for monitoring the status of high value assets and processes has proven to be less costly and less intrusive than the on-site inspections which they are intended to replace. However, these systems present a classic information overload problem to anyone trying to analyze the resulting sensor data. These data are typically so voluminous and contain information at such a low level that the significance of any single reading (e.g., a door open event) is not obvious. Sophisticated, automated techniques are needed to extract expected patterns in the data and isolate and characterize the remaining patterns that are due to undeclared activities. This paper describes a data analysis engine that runs a state machine model of each facility and its sensor suite. It analyzes the raw sensor data, converting and combining the inputs from many sensors into operator domain level information. It compares the resulting activities against a set of activities declared by an inspector or operator, and then presents the differences in a form comprehensible to an inspector. Although the current analysis engine was written with international nuclear material safeguards, nonproliferation, and transparency in mind, since there is no information about any particular facility in the software, there is no reason why it cannot be applied anywhere it is important to verify processes are occurring as expected, to detect intrusion into a secured area, or to detect the diversion of valuable assets

Wireless Sensor Network for Forest Fire Detection 2

OpenAIRE

João Gilberto Fernandes Gonçalves Teixeira

2017-01-01

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

Stochastic Tools for Network Intrusion Detection

OpenAIRE

Yu, Lu; Brooks, Richard R.

2017-01-01

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

The NIDS Cluster: Scalable, Stateful Network Intrusion Detection on Commodity Hardware

Energy Technology Data Exchange (ETDEWEB)

Tierney, Brian L; Vallentin, Matthias; Sommer, Robin; Lee, Jason; Leres, Craig; Paxson, Vern; Tierney, Brian

2007-09-19

In this work we present a NIDS cluster as a scalable solution for realizing high-performance, stateful network intrusion detection on commodity hardware. The design addresses three challenges: (i) distributing traffic evenly across an extensible set of analysis nodes in a fashion that minimizes the communication required for coordination, (ii) adapting the NIDS's operation to support coordinating its low-level analysis rather than just aggregating alerts; and (iii) validating that the cluster produces sound results. Prototypes of our NIDS cluster now operate at the Lawrence Berkeley National Laboratory and the University of California at Berkeley. In both environments the clusters greatly enhance the power of the network security monitoring.

Provide a model to improve the performance of intrusion detection systems in the cloud

OpenAIRE

Foroogh Sedighi

2016-01-01

High availability of tools and service providers in cloud computing and the fact that cloud computing services are provided by internet and deal with public, have caused important challenges for new computing model. Cloud computing faces problems and challenges such as user privacy, data security, data ownership, availability of services, and recovery after breaking down, performance, scalability, programmability. So far, many different methods are presented for detection of intrusion in clou...

Time-resolved seismic tomography detects magma intrusions at Mount Etna.

Science.gov (United States)

Patanè, D; Barberi, G; Cocina, O; De Gori, P; Chiarabba, C

2006-08-11

The continuous volcanic and seismic activity at Mount Etna makes this volcano an important laboratory for seismological and geophysical studies. We used repeated three-dimensional tomography to detect variations in elastic parameters during different volcanic cycles, before and during the October 2002-January 2003 flank eruption. Well-defined anomalous low P- to S-wave velocity ratio volumes were revealed. Absent during the pre-eruptive period, the anomalies trace the intrusion of volatile-rich (>/=4 weight percent) basaltic magma, most of which rose up only a few months before the onset of eruption. The observed time changes of velocity anomalies suggest that four-dimensional tomography provides a basis for more efficient volcano monitoring and short- and midterm eruption forecasting of explosive activity.

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

Directory of Open Access Journals (Sweden)

Didit Suhartono

2015-02-01

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

Wireless sensor network for sodium leak detection

International Nuclear Information System (INIS)

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

2012-01-01

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

Orthodontic intrusion : Conventional and mini-implant assisted intrusion mechanics

Directory of Open Access Journals (Sweden)

Anup Belludi

2012-01-01

intrusion has revolutionized orthodontic anchorage and biomechanics by making anchorage perfectly stable. This article addresses various conventional clinical intrusion mechanics and especially intrusion using mini-implants that have proven effective over the years for intrusion of maxillary anteriors.

Wireless sensor for detecting explosive material

Science.gov (United States)

Lamberti, Vincent E; Howell, Jr., Layton N; Mee, David K; Sepaniak, Michael J

2014-10-28

Disclosed is a sensor for detecting explosive devices. The sensor includes a ferromagnetic metal and a molecular recognition reagent coupled to the ferromagnetic metal. The molecular recognition reagent is operable to expand upon absorption of vapor from an explosive material such that the molecular recognition reagent changes a tensile stress upon the ferromagnetic metal. The explosive device is detected based on changes in the magnetic switching characteristics of the ferromagnetic metal caused by the tensile stress.

Toward real time detection of the basic living activity in home using a wearable sensor and smart home sensors.

Science.gov (United States)

Bang, Sunlee; Kim, Minho; Song, Sa-Kwang; Park, Soo-Jun

2008-01-01

As the elderly people living alone are enormously increasing recently, we need the system inferring activities of daily living (ADL) for maintaining healthy life and recognizing emergency. The system should be constructed with sensors, which are used to associate with people's living while remaining as non intrusive views as possible. To do this, the proposed system use a triaxial accelerometer sensor and environment sensors indicating contact with subject in home. Particularly, in order to robustly infer ADLs, we present component ADL, which is decided with conjunction of human motion together, not just only contacted object identification. It is an important component in inferring ADL. In special, component ADL decision firstly refines misclassified initial activities, which improves the accuracy of recognizing ADL. Preliminary experiments results for proposed system provides overall recognition rate of over 97% over 8 component ADLs, which can be effectively applicable to recognize the final ADLs.

Intrusion Detection in NEAR System by Anti-denoising Traffic Data Series using Discrete Wavelet Transform

Directory of Open Access Journals (Sweden)

VANCEA, F.

2014-11-01

Full Text Available The paper presents two methods for detecting anomalies in data series derived from network traffic. Intrusion detection systems based on network traffic analysis are able to respond to incidents never seen before by detecting anomalies in data series extracted from the traffic. Some anomalies manifest themselves as pulses of various sizes and shapes, superimposed on series corresponding to normal traffic. In order to detect those impulses we propose two methods based on discrete wavelet transformation. Their effectiveness expressed in relative thresholds on pulse amplitude for no false negatives and no false positives is then evaluated against pulse duration and Hurst characteristic of original series. Different base functions are also evaluated for efficiency in the context of the proposed methods.

Automatic, non-intrusive, flame detection in pipelines

Energy Technology Data Exchange (ETDEWEB)

Morgan, M.D.; Mehta, S.A.; Moore, R.G. [Calgary Univ., AB (Canada). Dept. of Chemical and Petroleum Engineering; Al-Himyary, T.J. [Al-Himyary Consulting Inc., Calgary, AB (Canada)

2004-07-01

Flames have been known to occur within small diameter pipes operating under conditions of high turbulent flow. Although there are several methods of flame detection, few offer remote, non-line-of-site detection. In particular, combustion cannot be detected in cases where flammable mixtures are carried in flare lines, storage tank vents, air drilling or improperly designed purging operations. Combustion noise is being examined as a means to address this problem. A study was conducted in which flames within a small diameter tube were automatically detected using high speed pressure measurements and a newly developed algorithm. Commercially available, high-pressure, dynamic-pressure transducers were used for the measurements. The results of an experimental study showed that combustion noise can be distinguished from other sources of noise by its inverse power law relationship with frequency. This paper presented a newly developed algorithm which provides early detection of flames when combined with high-speed pressure measurements. The algorithm can also separate combustion noise automatically from other sources of noise when combined with other filters. In this study, the noise generated by a fluttering check valve was attenuated using a stop band filter. This detection method was found to be very reliable under the conditions tests, as long as there was no flow restriction between the sensor and the flame. A flow restriction would have resulted in the detection of only the strongest flame noise. It was shown that acoustic flame detection can be applied successfully in flare stacks, industrial burners and turbine combustors. It can be 15 times more sensitive than optical or electrical methods in diagnosing combustion problems with lean burning combustors. It may also be the only method available in applications that require remote, non-line-of-sight detection. 11 refs., 3 tabs., 15 figs.

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.

Long-distance fiber optic sensing solutions for pipeline leakage, intrusion, and ground movement detection

Science.gov (United States)

Nikles, Marc

2009-05-01

An increasing number of pipelines are constructed in remote regions affected by harsh environmental conditions where pipeline routes often cross mountain areas which are characterized by unstable grounds and where soil texture changes between winter and summer increase the probability of hazards. Third party intentional interference or accidental intrusions are a major cause of pipeline failures leading to large leaks or even explosions. Due to the long distances to be monitored and the linear nature of pipelines, distributed fiber optic sensing techniques offer significant advantages and the capability to detect and localize pipeline disturbance with great precision. Furthermore pipeline owner/operators lay fiber optic cable parallel to transmission pipelines for telecommunication purposes and at minimum additional cost monitoring capabilities can be added to the communication system. The Brillouin-based Omnisens DITEST monitoring system has been used in several long distance pipeline projects. The technique is capable of measuring strain and temperature over 100's kilometers with meter spatial resolution. Dedicated fiber optic cables have been developed for continuous strain and temperature monitoring and their deployment along the pipeline has enabled permanent and continuous pipeline ground movement, intrusion and leak detection. This paper presents a description of the fiber optic Brillouin-based DITEST sensing technique, its measurement performance and limits, while addressing future perspectives for pipeline monitoring. The description is supported by case studies and illustrated by field data.

Microfabricated Chemical Sensors for Aerospace Fire Detection Applications

Science.gov (United States)

Hunter, Gary W.; Neudeck, Philip G.; Fralick, Gustave; Thomas, Valarie; Makel, D.; Liu, C. C.; Ward, B.; Wu, Q. H.

2001-01-01

The detection of fires on-board commercial aircraft is extremely important for safety reasons. Although dependable fire detection equipment presently exists within the cabin, detection of fire within the cargo hold has been less reliable and susceptible to false alarms. A second, independent method of fire detection to complement the conventional smoke detection techniques, such as the measurement of chemical species indicative of a fire, will help reduce false alarms and improve aircraft safety. Although many chemical species are indicative of a fire, two species of particular interest are CO and CO2. This paper discusses microfabricated chemical sensor development tailored to meet the needs of fire safety applications. This development is based on progress in three types of technology: 1) Micromachining and microfabrication (Microsystem) technology to fabricate miniaturized sensors. 2) The use of nanocrystalline materials to develop sensors with improved stability combined with higher sensitivity. 3) The development of high temperature semiconductors, especially silicon carbide. The individual sensor being developed and their level of maturity will be presented.

Hydrogen Leak Detection Sensor Database

Science.gov (United States)

Baker, Barton D.

2010-01-01

This slide presentation reviews the characteristics of the Hydrogen Sensor database. The database is the result of NASA's continuing interest in and improvement of its ability to detect and assess gas leaks in space applications. The database specifics and a snapshot of an entry in the database are reviewed. Attempts were made to determine the applicability of each of the 65 sensors for ground and/or vehicle use.

Underwater detection by using ultrasonic sensor

Science.gov (United States)

Bakar, S. A. A.; Ong, N. R.; Aziz, M. H. A.; Alcain, J. B.; Haimi, W. M. W. N.; Sauli, Z.

2017-09-01

This paper described the low cost implementation of hardware and software in developing the system of ultrasonic which can visualize the feedback of sound in the form of measured distance through mobile phone and monitoring the frequency of detection by using real time graph of Java application. A single waterproof transducer of JSN-SR04T had been used to determine the distance of an object based on operation of the classic pulse echo detection method underwater. In this experiment, the system was tested by placing the housing which consisted of Arduino UNO, Bluetooth module of HC-06, ultrasonic sensor and LEDs at the top of the box and the transducer was immersed in the water. The system which had been tested for detection in vertical form was found to be capable of reporting through the use of colored LEDs as indicator to the relative proximity of object distance underwater form the sensor. As a conclusion, the system can detect the presence of an object underwater within the range of ultrasonic sensor and display the measured distance onto the mobile phone and the real time graph had been successfully generated.

Development of eddy current sensor for detecting defect on ferromagnetic material

International Nuclear Information System (INIS)

Choi, Duck Su; Lee, Hyang Beom

2002-01-01

In this paper, the eddy current sensor is developed for observing the ability of detecting defect on ferromagnetic material with variation of frequency and velocity. In order to research the characteristics on eddy current sensor. The circuit which is designed for processing detected voltage is developed and differential frequency is used for eddy current sensor to detect defect with variation of frequency. The ability of eddy current sensor to detect defects is studied with variation of velocity adjusted by rotating the circular plate. This study shows that the ability of eddy current sensor for detecting defect is increased and decreased by frequency. This fact means that the sensor has its best ability at a certain frequency. And the ability of eddy current sensor by velocity is decreased by increased velocity. Therefore, the eddy current sensor has to be developed with consideration of its operation velocity and frequency.

Microcontact imprinted surface plasmon resonance sensor for myoglobin detection

International Nuclear Information System (INIS)

Osman, Bilgen; Uzun, Lokman; Beşirli, Necati; Denizli, Adil

2013-01-01

In this study, we prepared surface plasmon resonance (SPR) sensor using the molecular imprinting technique for myoglobin detection in human serum. For this purpose, we synthesized myoglobin imprinted poly(hydroxyethyl methacrylate-N-methacryloyl-L-tryptophan methyl ester) [poly(HEMA-MATrp)] nanofilm on the surface of SPR sensor. We also synthesized non-imprinted poly(HEMA-MATrp) nanofilm without myoglobin for the control experiments. The SPR sensor was characterized with contact angle measurements, atomic force microscopy, X-ray photoelectron spectroscopy, and ellipsometry. We investigated the effectiveness of the sensor using the SPR system. We evaluated the ability of SPR sensor to sense myoglobin with myoglobin solutions (pH 7.4, phosphate buffer) in different concentration range and in the serum taken from a patient with acute myocardial infarction. We found that the Langmuir adsorption model was the most suitable for the sensor system. The detection limit was 87.6 ng/mL. In order to show the selectivity of the SPR sensor, we investigated the competitive detection of myoglobin, lysozyme, cytochrome c and bovine serum albumin. The results showed that the SPR sensor has high selectivity and sensitivity for myoglobin. - Highlights: • Micro-contact imprinted surface plasmon resonance sensor. • Real-time myoglobin detection in the serum taken from a patient with acute myocardial infarction • Reproducible results for consecutive myoglobin solution supplement • LOD and LOQ values of the SPR sensor were determined to be 26.3 and 87.6 ng/mL. • The SPR sensor has potential for myoglobin sensing during acute MI cases

Probabilistic monitoring in intrusion detection module for energy efficiency in mobile ad hoc networks

Science.gov (United States)

De Rango, Floriano; Lupia, Andrea

2016-05-01

MANETs allow mobile nodes communicating to each other using the wireless medium. A key aspect of these kind of networks is the security, because their setup is done without an infrastructure, so external nodes could interfere in the communication. Mobile nodes could be compromised, misbehaving during the multi-hop transmission of data, or they could have a selfish behavior to save energy, which is another important constraint in MANETs. The detection of these behaviors need a framework that takes into account the latest interactions among nodes, so malicious or selfish nodes could be detected also if their behavior is changed over time. The monitoring activity increases the energy consumption, so our proposal takes into account this issue reducing the energy required by the monitoring system, keeping the effectiveness of the intrusion detection system. The results show an improvement in the saved energy, improving the detection performance too.

Human intrusion

International Nuclear Information System (INIS)

Hora, S.; Neill, R.; Williams, R.; Bauser, M.; Channell, J.

1993-01-01

This paper focused on the possible approaches to evaluating the impacts of human intrusion on nuclear waste disposal. Several major issues were reviewed. First, it was noted that human intrusion could be addressed either quantitatively through performance assessments or qualitatively through design requirements. Second, it was decided that it was impossible to construct a complete set of possible future human intrusion scenarios. Third, the question of when the effect of possible human intrusion should be considered, before or after site selection was reviewed. Finally, the time frame over which human intrusion should be considered was discussed

Non-intrusive gesture recognition system combining with face detection based on Hidden Markov Model

Science.gov (United States)

Jin, Jing; Wang, Yuanqing; Xu, Liujing; Cao, Liqun; Han, Lei; Zhou, Biye; Li, Minggao

2014-11-01

A non-intrusive gesture recognition human-machine interaction system is proposed in this paper. In order to solve the hand positioning problem which is a difficulty in current algorithms, face detection is used for the pre-processing to narrow the search area and find user's hand quickly and accurately. Hidden Markov Model (HMM) is used for gesture recognition. A certain number of basic gesture units are trained as HMM models. At the same time, an improved 8-direction feature vector is proposed and used to quantify characteristics in order to improve the detection accuracy. The proposed system can be applied in interaction equipments without special training for users, such as household interactive television

A Non-Intrusive GMA Welding Process Quality Monitoring System Using Acoustic Sensing.

Science.gov (United States)

Cayo, Eber Huanca; Alfaro, Sadek Crisostomo Absi

2009-01-01

Most of the inspection methods used for detection and localization of welding disturbances are based on the evaluation of some direct measurements of welding parameters. This direct measurement requires an insertion of sensors during the welding process which could somehow alter the behavior of the metallic transference. An inspection method that evaluates the GMA welding process evolution using a non-intrusive process sensing would allow not only the identification of disturbances during welding runs and thus reduce inspection time, but would also reduce the interference on the process caused by the direct sensing. In this paper a nonintrusive method for weld disturbance detection and localization for weld quality evaluation is demonstrated. The system is based on the acoustic sensing of the welding electrical arc. During repetitive tests in welds without disturbances, the stability acoustic parameters were calculated and used as comparison references for the detection and location of disturbances during the weld runs.

Investigation of contactless detection using a giant magnetoresistance sensor for detecting prostate specific antigen.

Science.gov (United States)

Sun, Xuecheng; Zhi, Shaotao; Lei, Chong; Zhou, Yong

2016-08-01

This paper presents a contactless detection method for detecting prostate specific antigen with a giant magnetoresistance sensor. In contactless detection case, the prostate specific antigen sample preparation was separated from the sensor that prevented the sensor from being immersed in chemical solvents, and made the sensor implementing in immediately reuse without wash. Experimental results showed that applied an external magnetic field in a range of 50 Oe to 90 Oe, Dynabeads with a concentration as low as 0.1 μg/mL can be detected by this system and could give an approximate quantitation to the logarithmic of Dynabeads concentration. Sandwich immunoassay was employed for preparing PSA samples. The PSA capture was implemented on a gold film modified with a self-assembled monolayer and using biotinylated secondary antibody against PSA and streptavidinylated Dynabeads. With DC magnetic field in the range of 50 to 90 Oe, PSA can be detected with a detection limit as low as 0.1 ng/mL. Samples spiked with different concentrations of PSA can be distinguished clearly. Due to the contactless detection method, the detection system exhibited advantages such as convenient manipulation, reusable, inexpensive, small weight. So, this detection method was a promising candidate in biomarker detection, especially in point of care detection.

A Magnetic Sensor System for Biological Detection

KAUST Repository

Li, Fuquan

2015-05-01

Magnetic biosensors detect biological targets through sensing the stray field of magnetic beads which label the targets. Commonly, magnetic biosensors employ the “sandwich” method to immobilize biological targets, i.e., the targets are sandwiched between a bio-functionalized sensor surface and bio-functionalized magnetic beads. This method has been used very successfully in different application, but its execution requires a rather elaborate procedure including several washing and incubation steps. This dissertation investigates a new magnetic biosensor concept, which enables a simple and effective detection of biological targets. The biosensor takes advantage of the size difference between bare magnetic beads and compounds of magnetic beads and biological targets. First, the detection of super-paramagnetic beads via magnetic tunnel junction (MTJ) sensors is implemented. Frequency modulation is used to enhance the signal-to-noise ratio, enabling the detection of a single magnetic bead. Second, the concept of the magnetic biosensor is investigated theoretically. The biosensor consists of an MTJ sensor, which detects the stray field of magnetic beads inside of a trap on top of the MTJ. A microwire between the trap and the MTJ is used to attract magnetic beads to the trapping well by applying a current to it. The MTJ sensor’s output depends on the number of beads inside the trap. If biological targets are in the sample solution, the beads will form bead compounds consisting of beads linked to the biological targets. Since bead compounds are larger than bare beads, the number of beads inside the trapping well will depend on the presence of biological targets. Hence, the output of the MTJ sensor will depend on the biological targets. The dependences of sensor signals on the sizes of the MTJ sensor, magnetic beads and biological targets are studied to find the optimum constellations for the detection of specific biological targets. The optimization is demonstrated

Computationally Efficient Neural Network Intrusion Security Awareness

Energy Technology Data Exchange (ETDEWEB)

Todd Vollmer; Milos Manic

2009-08-01

An enhanced version of an algorithm to provide anomaly based intrusion detection alerts for cyber security state awareness is detailed. A unique aspect is the training of an error back-propagation neural network with intrusion detection rule features to provide a recognition basis. Network packet details are subsequently provided to the trained network to produce a classification. This leverages rule knowledge sets to produce classifications for anomaly based systems. Several test cases executed on ICMP protocol revealed a 60% identification rate of true positives. This rate matched the previous work, but 70% less memory was used and the run time was reduced to less than 1 second from 37 seconds.

RF sensor for multiphase flow measurement through an oil pipeline

Science.gov (United States)

Wylie, S. R.; Shaw, A.; Al-Shamma'a, A. I.

2006-08-01

We have developed, in conjunction with Solartron ISA, an electromagnetic cavity resonator based sensor for multiphase flow measurement through an oil pipeline. This sensor is non-intrusive and transmits low power (10 mW) radio frequencies (RF) in the range of 100-350 MHz and detects the pipeline contents using resonant peaks captured instantaneously. The multiple resonances from each captured RF spectrum are analysed to determine the phase fractions in the pipeline. An industrial version of the sensor for a 102 mm (4 inch) diameter pipe has been constructed and results from this sensor are compared to those given by simulations performed using the electromagnetic high frequency structure simulator software package HFSS. This paper was presented at the 13th International Conference on Sensors and held in Chatham, Kent, on 6-7 September 2005.

Sensor fault detection and recovery in satellite attitude control

Science.gov (United States)

Nasrolahi, Seiied Saeed; Abdollahi, Farzaneh

2018-04-01

This paper proposes an integrated sensor fault detection and recovery for the satellite attitude control system. By introducing a nonlinear observer, the healthy sensor measurements are provided. Considering attitude dynamics and kinematic, a novel observer is developed to detect the fault in angular rate as well as attitude sensors individually or simultaneously. There is no limit on type and configuration of attitude sensors. By designing a state feedback based control signal and Lyapunov stability criterion, the uniformly ultimately boundedness of tracking errors in the presence of sensor faults is guaranteed. Finally, simulation results are presented to illustrate the performance of the integrated scheme.

Wireless Sensor Network Based Smart Grid Communications: Cyber Attacks, Intrusion Detection System and Topology Control

Directory of Open Access Journals (Sweden)

Lipi Chhaya

2017-01-01

Full Text Available The existing power grid is going through a massive transformation. Smart grid technology is a radical approach for improvisation in prevailing power grid. Integration of electrical and communication infrastructure is inevitable for the deployment of Smart grid network. Smart grid technology is characterized by full duplex communication, automatic metering infrastructure, renewable energy integration, distribution automation and complete monitoring and control of entire power grid. Wireless sensor networks (WSNs are small micro electrical mechanical systems that are deployed to collect and communicate the data from surroundings. WSNs can be used for monitoring and control of smart grid assets. Security of wireless sensor based communication network is a major concern for researchers and developers. The limited processing capabilities of wireless sensor networks make them more vulnerable to cyber-attacks. The countermeasures against cyber-attacks must be less complex with an ability to offer confidentiality, data readiness and integrity. The address oriented design and development approach for usual communication network requires a paradigm shift to design data oriented WSN architecture. WSN security is an inevitable part of smart grid cyber security. This paper is expected to serve as a comprehensive assessment and analysis of communication standards, cyber security issues and solutions for WSN based smart grid infrastructure.

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.

Live demonstration: Screen printed, microwave based level sensor for automated drug delivery

KAUST Repository

Karimi, Muhammad Akram

2018-01-02

Level sensors find numerous applications in many industries to automate the processes involving chemicals. Recently, some commercial ultrasound based level sensors are also being used to automate the drug delivery process [1]. Some of the most desirable features of level sensors to be used for medical use are their non-intrusiveness, low cost and consistent performance. In this demo, we will present a completely new method of sensing the liquid level using microwaves. It is a common stereotype to consider microwaves sensing mechanism as being expensive. Unlike usual expensive, intrusive and bulky microwave methods of level sensing using guided radars, we will present an extremely low cost printed, non-intrusive microwave sensor to reliably sense the liquid level.

SERS-based pesticide detection by using nanofinger sensors

Science.gov (United States)

Kim, Ansoon; Barcelo, Steven J.; Li, Zhiyong

2015-01-01

Simple, sensitive, and rapid detection of trace levels of extensively used and highly toxic pesticides are in urgent demand for public health. Surface-enhanced Raman scattering (SERS)-based sensor was designed to achieve ultrasensitive and simple pesticide sensing. We developed a portable sensor system composed of high performance and reliable gold nanofinger sensor strips and a custom-built portable Raman spectrometer. Compared to the general procedure and previously reported studies that are limited to laboratory settings, our analytical method is simple, sensitive, rapid, and cost-effective. Based on the SERS results, the chemical interaction of two pesticides, chlorpyrifos (CPF) and thiabendazole (TBZ), with gold nanofingers was studied to determine a fingerprint for each pesticide. The portable SERS-sensor system was successfully demonstrated to detect CPF and TBZ pesticides within 15 min with a detection limit of 35 ppt in drinking water and 7 ppb on apple skin, respectively.

Impact of sensor detection limits on protecting water distribution systems from contamination events

International Nuclear Information System (INIS)

McKenna, Sean Andrew; Hart, David Blaine; Yarrington, Lane

2006-01-01

Real-time water quality sensors are becoming commonplace in water distribution systems. However, field deployable, contaminant-specific sensors are still in the development stage. As development proceeds, the necessary operating parameters of these sensors must be determined to protect consumers from accidental and malevolent contamination events. This objective can be quantified in several different ways including minimization of: the time necessary to detect a contamination event, the population exposed to contaminated water, the extent of the contamination within the network, and others. We examine the ability of a sensor set to meet these objectives as a function of both the detection limit of the sensors and the number of sensors in the network. A moderately sized distribution network is used as an example and different sized sets of randomly placed sensors are considered. For each combination of a certain number of sensors and a detection limit, the mean values of the different objectives across multiple random sensor placements are calculated. The tradeoff between the necessary detection limit in a sensor and the number of sensors is evaluated. Results show that for the example problem examined here, a sensor detection limit of 0.01 of the average source concentration is adequate for maximum protection. Detection of events is dependent on the detection limit of the sensors, but for those events that are detected, the values of the performance measures are not a function of the sensor detection limit. The results of replacing a single sensor in a network with a sensor having a much lower detection limit show that while this replacement can improve results, the majority of the additional events detected had performance measures of relatively low consequence.

Guided wave and damage detection in composite laminates using different fiber optic sensors.

Science.gov (United States)

Li, Fucai; Murayama, Hideaki; Kageyama, Kazuro; Shirai, Takehiro

2009-01-01

Guided wave detection using different fiber optic sensors and their applications in damage detection for composite laminates were systematically investigated and compared in this paper. Two types of fiber optic sensors, namely fiber Bragg gratings (FBG) and Doppler effect-based fiber optic (FOD) sensors, were addressed and guided wave detection systems were constructed for both types. Guided waves generated by a piezoelectric transducer were propagated through a quasi-isotropic carbon fiber reinforced plastic (CFRP) laminate and acquired by these fiber optic sensors. Characteristics of these fiber optic sensors in ultrasonic guided wave detection were systematically compared. Results demonstrated that both the FBG and FOD sensors can be applied in guided wave and damage detection for the CFRP laminates. The signal-to-noise ratio (SNR) of guided wave signal captured by an FOD sensor is relatively high in comparison with that of the FBG sensor because of their different physical principles in ultrasonic detection. Further, the FOD sensor is sensitive to the damage-induced fundamental shear horizontal (SH(0)) guided wave that, however, cannot be detected by using the FBG sensor, because the FOD sensor is omnidirectional in ultrasound detection and, in contrast, the FBG sensor is severely direction dependent.

Radiation detection and situation management by distributed sensor networks

International Nuclear Information System (INIS)

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

2009-01-01

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

Detection of sensor failures in nuclear plants using analytic redundancy

International Nuclear Information System (INIS)

Kitamura, M.

1980-01-01

A method for on-line, nonperturbative detection and identification of sensor failures in nuclear power plants was studied to determine its feasibility. This method is called analytic redundancy, or functional redundancy. Sensor failure has traditionally been detected by comparing multiple signals from redundant sensors, such as in two-out-of-three logic. In analytic redundancy, with the help of an assumed model of the physical system, the signals from a set of sensors are processed to reproduce the signals from all system sensors

The Electrical Self-Potential Method as a Non-Intrusive Snow-Hydrological Sensor

Science.gov (United States)

Kulessa, B.; Thompson, S. S.; Luethi, M. P.; Essery, R.

2015-12-01

Building on growing momentum in the application of geophysical techniques to snow problems and, specifically, on new theory and an electrical geophysical snow hydrological model published recently; we demonstrate for the first time that the electrical self-potential geophysical technique can sense in-situ bulk meltwater fluxes. This has broad and immediate implications for snow measurement practice, modelling and operational snow forecasting. Our ability to measure, quantify and assimilate hydrological properties and processes of snow in operational models is disproportionally poor compared to the significance of seasonal snowmelt as a global water resource and major risk factor in flood and avalanche forecasting. Encouraged by recent theoretical, modelling and laboratory work, we show here that the diurnal evolution of aerially-distributed self-potential magnitudes closely track those of bulk meltwater fluxes in melting in-situ snowpacks at Rhone and Jungfraujoch glaciers, Switzerland. Numerical modelling infers temporally-evolving liquid water contents in the snowpacks on successive days in close agreement with snow-pit measurements. Muting previous concerns, the governing physical and chemical properties of snow and meltwater became temporally invariant for modelling purposes. Because measurement procedure is straightforward and readily automated for continuous monitoring over significant spatial scales, we conclude that the self-potential geophysical method is a highly-promising non-intrusive snow-hydrological sensor for measurement practice, modelling and operational snow forecasting.

Network Intrusion Detection System (NIDS in Cloud Environment based on Hidden Naïve Bayes Multiclass Classifier

Directory of Open Access Journals (Sweden)

Hafza A. Mahmood

2018-04-01

Full Text Available Cloud Environment is next generation internet based computing system that supplies customiza-ble services to the end user to work or access to the various cloud applications. In order to provide security and decrease the damage of information system, network and computer system it is im-portant to provide intrusion detection system (IDS. Now Cloud environment are under threads from network intrusions, as one of most prevalent and offensive means Denial of Service (DoS attacks that cause dangerous impact on cloud computing systems. This paper propose Hidden naïve Bayes (HNB Classifier to handle DoS attacks which is a data mining (DM model used to relaxes the conditional independence assumption of Naïve Bayes classifier (NB, proposed sys-tem used HNB Classifier supported with discretization and feature selection where select the best feature enhance the performance of the system and reduce consuming time. To evaluate the per-formance of proposal system, KDD 99 CUP and NSL KDD Datasets has been used. The experi-mental results show that the HNB classifier improves the performance of NIDS in terms of accu-racy and detecting DoS attacks, where the accuracy of detect DoS is 100% in three test KDD cup 99 dataset by used only 12 feature that selected by use gain ratio while in NSL KDD Dataset the accuracy of detect DoS attack is 90 % in three Experimental NSL KDD dataset by select 10 fea-ture only.

Thin film sensor materials for detection of Nitro-Aromatic explosives

Science.gov (United States)

Ramdasi, Dipali; Mudhalwadkar, Rohini

2018-03-01

Many countries have experienced terrorist activities and innocent people have suffered. Timely detection of explosives can avoid this situation. This paper targets the detection of Nitrobenzene and Nitrotoluene, which are nitroaromatic compounds possessing explosive properties. As direct sensors for detecting these compounds are not available, Polyaniline based thin film sensors doped with palladium are developed using the spin coating technique. The response of the developed sensors is observed for varying concentrations of explosives. It is observed that zinc oxide based sensor is more sensitive to Nitrotoluene exhibiting a relative change in resistance of 0.78. The tungsten oxide sensor is more sensitive to Nitrobenzene with a relative change in resistance of 0.48. The sensor performance is assessed by measuring the response and recovery time. The cross sensitivity of the sensors is evaluated for ethanol, acetone and methanol which was observed as very low.

Optical Sensors for Detection of Amino Acids.

Science.gov (United States)

Pettiwala, Aafrin M; Singh, Prabhat K

2017-11-06

Amino acids are crucially involved in a myriad of biological processes. Any aberrant changes in physiological level of amino acids often manifest in common metabolic disorders, serious neurological conditions and cardiovascular diseases. Thus, devising methods for detection of trace amounts of amino acids becomes highly elemental to their efficient clinical diagnosis. Recently, the domain of developing optical sensors for detection of amino acids has witnessed significant activity which is the focus of the current review article. We undertook a detailed search of the peer-reviewed literature that primarily deals with optical sensors for amino acids and focuses on the use of different type of materials as a sensing platform. Ninety-five papers have been included in the review, majority of which deals with optical sensors. We attempt to systematically classify these contributions based on applications of various chemical and biological scaffolds such as polymers, supramolecular assemblies, nanoparticles, DNA, heparin etc. for the sensing of amino acids. This review identifies that supramolecular assemblies and nanomaterial continue to be commonly used materials to devise sensors for amino acids followed by surfactant assemblies. The broad implications of amino acids in human health and diagnosis have stirred a lot of interest to develop optimized optical detection systems for amino acids in recent years, using different materials based on chemical and biological scaffolds. We have also attempted to highlight the merits and demerits of some of the noteworthy sensor systems to instigate further efforts for constructing amino acids sensor based on unconventional concepts. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

基于信息熵的SVM入侵检测技术%Exploring SVM-based intrusion detection through information entropy theory

Institute of Scientific and Technical Information of China (English)

朱文杰; 王强; 翟献军

2013-01-01

在传统基于SVM的入侵检测中,核函数构造和特征选择采用先验知识,普遍存在准确度不高、效率低下的问题.通过信息熵理论与SVM算法相结合的方法改进为基于信息熵的SVM入侵检测算法,可以提高入侵检测的准确性,提升入侵检测的效率.基于信息熵的SVM入侵检测算法包括两个方面:一方面,根据样本包含的用户信息熵和方差,将样本特征统一,以特征是否属于置信区间来度量.将得到的样本特征置信向量作为SVM核函数的构造参数,既可保证训练样本集与最优分类面之间的对应关系,又可得到入侵检测需要的最大分类间隔；另一方面,将样本包含的用户信息量作为度量大幅度约简样本特征子集,不但降低了样本计算规模,而且提高了分类器的训练速度.实验表明,该算法在入侵检测系统中的应用优于传统的SVM算法.%In traditional SVM based intrusion detection approaches,both core function construction and feature selection use prior knowdege.Due to this,they are not only inefficient but also inaccurate.It is observed that integrating information entropy theory into SVM-based intrusion detection can enhance both the precision and the speed.Concludely speaking,SVM-based entropy intrusion detection algorithms are made up of two aspects:on one hand,setting sample confidence vector as core function's constructor of SVM algorithm can guarantee the mapping relationship between training sample and optimization classification plane.Also,the intrusion detection's maximum interval can be acquired.On the other hand,simplifying feature subset with samples's entropy as metric standard can not only shrink the computing scale but also improve the speed.Experiments prove that the SVM based entropy intrusion detection algoritm outperfomrs other tradional algorithms.

Pulse-driven magnetoimpedance sensor detection of cardiac magnetic activity.

Directory of Open Access Journals (Sweden)

Shinsuke Nakayama

Full Text Available This study sought to establish a convenient method for detecting biomagnetic activity in the heart. Electrical activity of the heart simultaneously induces a magnetic field. Detection of this magnetic activity will enable non-contact, noninvasive evaluation to be made. We improved the sensitivity of a pulse-driven magnetoimpedance (PMI sensor, which is used as an electric compass in mobile phones and as a motion sensor of the operation handle in computer games, toward a pico-Tesla (pT level, and measured magnetic fields on the surface of the thoracic wall in humans. The changes in magnetic field detected by this sensor synchronized with the electric activity of the electrocardiogram (ECG. The shape of the magnetic wave was largely altered by shifting the sensor position within 20 mm in parallel and/or perpendicular to the thoracic wall. The magnetic activity was maximal in the 4th intercostals near the center of the sterna. Furthermore, averaging the magnetic activity at 15 mm in the distance between the thoracic wall and the sensor demonstrated magnetic waves mimicking the P wave and QRS complex. The present study shows the application of PMI sensor in detecting cardiac magnetic activity in several healthy subjects, and suggests future applications of this technology in medicine and biology.

Achieving Crossed Strong Barrier Coverage in Wireless Sensor Network.

Science.gov (United States)

Han, Ruisong; Yang, Wei; Zhang, Li

2018-02-10

Barrier coverage has been widely used to detect intrusions in wireless sensor networks (WSNs). It can fulfill the monitoring task while extending the lifetime of the network. Though barrier coverage in WSNs has been intensively studied in recent years, previous research failed to consider the problem of intrusion in transversal directions. If an intruder knows the deployment configuration of sensor nodes, then there is a high probability that it may traverse the whole target region from particular directions, without being detected. In this paper, we introduce the concept of crossed barrier coverage that can overcome this defect. We prove that the problem of finding the maximum number of crossed barriers is NP-hard and integer linear programming (ILP) is used to formulate the optimization problem. The branch-and-bound algorithm is adopted to determine the maximum number of crossed barriers. In addition, we also propose a multi-round shortest path algorithm (MSPA) to solve the optimization problem, which works heuristically to guarantee efficiency while maintaining near-optimal solutions. Several conventional algorithms for finding the maximum number of disjoint strong barriers are also modified to solve the crossed barrier problem and for the purpose of comparison. Extensive simulation studies demonstrate the effectiveness of MSPA.

A comparative study of misalignment detection using a novel Wireless Sensor with conventional Wired Sensors

International Nuclear Information System (INIS)

Arebi, L; Gu, F; Ball, A

2012-01-01

The advancement in low cost and low power MEMS sensors makes it possible to develop a cost-effective wireless accelerometer for condition monitoring. Especially, the MEMS accelerometer can be mounted directly on a rotating shaft, which has the potential to capture the dynamics of the shaft more accurately and hence to achieve high monitoring performance. In this paper a systematic comparison of shaft misalignment detection is conducted, based on a bearing test rig, between the wireless sensor measurement scheme and other three common sensors: a laser vibrometer, an accelerometer and a shaft encoder. These four sensors are used to measure simultaneously the dynamic responses: Instantaneous Angular Speed (IAS) from the encoder, bearing house acceleration from the accelerometer, shaft displacements from the laser vibrometer and angular acceleration from the wireless sensor. These responses are then compared in both the time and frequency domains in detecting and diagnosing different levels of shaft misalignment. Results show the effectiveness of wireless accelerometer in detecting the faults.

Chemiresistive Graphene Sensors for Ammonia Detection.

Science.gov (United States)

Mackin, Charles; Schroeder, Vera; Zurutuza, Amaia; Su, Cong; Kong, Jing; Swager, Timothy M; Palacios, Tomás

2018-05-09

The primary objective of this work is to demonstrate a novel sensor system as a convenient vehicle for scaled-up repeatability and the kinetic analysis of a pixelated testbed. This work presents a sensor system capable of measuring hundreds of functionalized graphene sensors in a rapid and convenient fashion. The sensor system makes use of a novel array architecture requiring only one sensor per pixel and no selector transistor. The sensor system is employed specifically for the evaluation of Co(tpfpp)ClO 4 functionalization of graphene sensors for the detection of ammonia as an extension of previous work. Co(tpfpp)ClO 4 treated graphene sensors were found to provide 4-fold increased ammonia sensitivity over pristine graphene sensors. Sensors were also found to exhibit excellent selectivity over interfering compounds such as water and common organic solvents. The ability to monitor a large sensor array with 160 pixels provides insights into performance variations and reproducibility-critical factors in the development of practical sensor systems. All sensors exhibit the same linearly related responses with variations in response exhibiting Gaussian distributions, a key finding for variation modeling and quality engineering purposes. The mean correlation coefficient between sensor responses was found to be 0.999 indicating highly consistent sensor responses and excellent reproducibility of Co(tpfpp)ClO 4 functionalization. A detailed kinetic model is developed to describe sensor response profiles. The model consists of two adsorption mechanisms-one reversible and one irreversible-and is shown capable of fitting experimental data with a mean percent error of 0.01%.

Body-Sensor-Network-Based Spasticity Detection.

Science.gov (United States)

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

2016-05-01

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

Non-Intrusive Magneto-Optic Detecting System for Investigations of Air Switching Arcs

International Nuclear Information System (INIS)

Zhang Pengfei; Zhang Guogang; Dong Jinlong; Liu Wanying; Geng Yingsan

2014-01-01

In current investigations of electric arc plasmas, experiments based on modern testing technology play an important role. To enrich the testing methods and contribute to the understanding and grasping of the inherent mechanism of air switching arcs, in this paper, a non-intrusive detecting system is described that combines the magneto-optic imaging (MOI) technique with the solution to inverse electromagnetic problems. The detecting system works in a sequence of main steps as follows: MOI of the variation of the arc flux density over a plane, magnetic field information extracted from the magneto-optic (MO) images, arc current density distribution and spatial pattern reconstruction by inverting the resulting field data. Correspondingly, in the system, an MOI set-up is designed based on the Faraday effect and the polarization properties of light, and an intelligent inversion algorithm is proposed that involves simulated annealing (SA). Experiments were carried out for high current (2 kA RMS) discharge cases in a typical low-voltage switchgear. The results show that the MO detection system possesses the advantages of visualization, high resolution and response, and electrical insulation, which provides a novel diagnostics tool for further studies of the arc. (low temperature plasma)

Intrusion Prevention and Detection in Grid Computing - The ALICE Case

CERN Document Server

INSPIRE-00416173; Kebschull, Udo

2015-01-01

Grids allow users flexible on-demand usage of computing resources through remote communication networks. A remarkable example of a Grid in High Energy Physics (HEP) research is used in the ALICE experiment at European Organization for Nuclear Research CERN. Physicists can submit jobs used to process the huge amount of particle collision data produced by the Large Hadron Collider (LHC). Grids face complex security challenges. They are interesting targets for attackers seeking for huge computational resources. Since users can execute arbitrary code in the worker nodes on the Grid sites, special care should be put in this environment. Automatic tools to harden and monitor this scenario are required. Currently, there is no integrated solution for such requirement. This paper describes a new security framework to allow execution of job payloads in a sandboxed context. It also allows process behavior monitoring to detect intrusions, even when new attack methods or zero day vulnerabilities are exploited, by a Machin...

Anomaly-Based Intrusion Detection Systems Utilizing System Call Data

Science.gov (United States)

2012-03-01

52 Table 7. Place Reachability Statistics for Low Level CPN...54 Table 8. Place Reachability Statistics for High Level CPN................................................. 55 Table 9. Password Stealing...the efficiency of traditional anti-virus software tools that are dependent on gigantic , continuously updated databases. Fortunately, Intrusion

Sensor Failure Detection of FASSIP System using Principal Component Analysis

Science.gov (United States)

Sudarno; Juarsa, Mulya; Santosa, Kussigit; Deswandri; Sunaryo, Geni Rina

2018-02-01

In the nuclear reactor accident of Fukushima Daiichi in Japan, the damages of core and pressure vessel were caused by the failure of its active cooling system (diesel generator was inundated by tsunami). Thus researches on passive cooling system for Nuclear Power Plant are performed to improve the safety aspects of nuclear reactors. The FASSIP system (Passive System Simulation Facility) is an installation used to study the characteristics of passive cooling systems at nuclear power plants. The accuracy of sensor measurement of FASSIP system is essential, because as the basis for determining the characteristics of a passive cooling system. In this research, a sensor failure detection method for FASSIP system is developed, so the indication of sensor failures can be detected early. The method used is Principal Component Analysis (PCA) to reduce the dimension of the sensor, with the Squarred Prediction Error (SPE) and statistic Hotteling criteria for detecting sensor failure indication. The results shows that PCA method is capable to detect the occurrence of a failure at any sensor.

Geophysical detection of marine intrusions in Black Sea coastal areas (Romania) using VES and ERT data

OpenAIRE

CHITEA, Florina; GEORGESCU, Paul; IOANE, Dumitru

2011-01-01

Abstract. Communities living in coastal areas depend in a great extent on the fresh water resources exploited from aquifers which are usually in a natural hydrodynamic equilibrium with the sea water. The contamination of fresh water with marine saltwater determines a significant increase in the aquifers electric conductivity, allowing an efficient application of resistivity methods in detecting and monitoring the marine intrusions. We present case studies from Romania (Costinesti and Vama Vec...

Research of detection depth for graphene-based optical sensor

Science.gov (United States)

Yang, Yong; Sun, Jialve; Liu, Lu; Zhu, Siwei; Yuan, Xiaocong

2018-03-01

Graphene-based optical sensors have been developed for research into the biological intercellular refractive index (RI) because they offer greater detection depths than those provided by the surface plasmon resonance technique. In this Letter, we propose an experimental approach for measurement of the detection depth in a graphene-based optical sensor system that uses transparent polydimethylsiloxane layers with different thicknesses. The experimental results show that detection depths of 2.5 μm and 3 μm can be achieved at wavelengths of 532 nm and 633 nm, respectively. These results prove that graphene-based optical sensors can realize long-range RI detection and are thus promising for use as tools in the biological cell detection field. Additionally, we analyze the factors that influence the detection depth and provide a feasible approach for detection depth control based on adjustment of the wavelength and the angle of incidence. We believe that this approach will be useful in RI tomography applications.

Unknown input observer based detection of sensor faults in a wind turbine

DEFF Research Database (Denmark)

Odgaard, Peter Fogh; Stoustrup, Jakob

2010-01-01

In this paper an unknown input observer is designed to detect three different sensor fault scenarios in a specified bench mark model for fault detection and accommodation of wind turbines. In this paper a subset of faults is dealt with, it are faults in the rotor and generator speed sensors as well...... as a converter sensor fault. The proposed scheme detects the speed sensor faults in question within the specified requirements given in the bench mark model, while the converter fault is detected but not within the required time to detect....

Towards an operational sensor-fusion system for anti-personnel landmine detection

NARCIS (Netherlands)

Cremer, F.; Schutte, K.; Schavemaker, J.G.M.; Breejen, E. den

2000-01-01

To acquire detection performance required for an operational system for the detection of anti-personnel landmines, it is necessary to use multiple sensors and sensor-fusion techniques. This paper describes five decision-level sensor-fusion techniques and their common optimisation method. The

Influence of seawater intrusion on microbial communities in groundwater.

Science.gov (United States)

Unno, Tatsuya; Kim, Jungman; Kim, Yumi; Nguyen, Son G; Guevarra, Robin B; Kim, Gee Pyo; Lee, Ji-Hoon; Sadowsky, Michael J

2015-11-01

Groundwater is the sole source of potable water on Jeju Island in the Republic of (South) Korea. Groundwater is also used for irrigation and industrial purposes, and it is severely impacted by seawater intrusion in coastal areas. Consequently, monitoring the intrusion of seawater into groundwater on Jeju is very important for health and environmental reasons. A number of studies have used hydrological models to predict the deterioration of groundwater quality caused by seawater intrusion. However, there is conflicting evidence of intrusion due to complicated environmental influences on groundwater quality. Here we investigated the use of next generation sequencing (NGS)-based microbial community analysis as a way to monitor groundwater quality and detect seawater intrusion. Pristine groundwater, groundwater from three coastal areas, and seawater were compared. Analysis of the distribution of bacterial species clearly indicated that the high and low salinity groundwater differed significantly with respect to microbial composition. While members of the family Parvularculaceae were only identified in high salinity water samples, a greater percentage of the phylum Actinobacteria was predominantly observed in pristine groundwater. In addition, we identified 48 shared operational taxonomic units (OTUs) with seawater, among which the high salinity groundwater sample shared a greater number of bacterial species with seawater (6.7%). In contrast, other groundwater samples shared less than 0.5%. Our results suggest that NGS-based microbial community analysis of groundwater may be a useful tool for monitoring groundwater quality and detect seawater intrusion. This technology may also provide additional insights in understanding hydrological dynamics. Copyright © 2015 Elsevier B.V. All rights reserved.

Quantitative Alpha Fetoprotein Detection with a Piezoelectric Microcantilever Mass Sensor

Energy Technology Data Exchange (ETDEWEB)

Lee, Sang Kyu; Cho, Jong Yun; Jeon, Sang Min; Cha, Hyung Joon; Moon, Won Kyu [Pohang University of Science and Technology, Pohang (Korea, Republic of); Lee, Yeol Ho [Samsung Advanced Institute of Technology, Yongin (Korea, Republic of)

2011-10-15

Alpha fetoprotein(AFP), which is serological marker for hepatocellular carcinoma, was quantitatively measured by its normal concentration, 10 ng/ml, with a label-free piezoelectric microcantilever mass sensor. The principle of detection is based on changes in the resonant frequency of the piezoelectric microcantilever before and after target molecules are attached to it, and its resonant frequency is measured electrically using a conductance spectrum. The resonant frequency of the developed sensor is approximately 1.34 MHz and the mass sensitivity is approximately 175 Hz/pg. The sensor has high reliability as mass sensor by reducing the effect of surface stress on resonant frequency due to attached proteins. 'Dip and dry' technique was used to react the sensor with reagents for immobilizing AFP antibody on the sensor and detecting AFP antigen. The measured mass of the detected AFP antigen was 6.02 pg at the concentration of 10 ng/ml, and 10.67 pg at 50 ng/ml when the immunoreaction time was 10 min.

Quantitative Alpha Fetoprotein Detection with a Piezoelectric Microcantilever Mass Sensor

International Nuclear Information System (INIS)

Lee, Sang Kyu; Cho, Jong Yun; Jeon, Sang Min; Cha, Hyung Joon; Moon, Won Kyu; Lee, Yeol Ho

2011-01-01

Alpha fetoprotein(AFP), which is serological marker for hepatocellular carcinoma, was quantitatively measured by its normal concentration, 10 ng/ml, with a label-free piezoelectric microcantilever mass sensor. The principle of detection is based on changes in the resonant frequency of the piezoelectric microcantilever before and after target molecules are attached to it, and its resonant frequency is measured electrically using a conductance spectrum. The resonant frequency of the developed sensor is approximately 1.34 MHz and the mass sensitivity is approximately 175 Hz/pg. The sensor has high reliability as mass sensor by reducing the effect of surface stress on resonant frequency due to attached proteins. 'Dip and dry' technique was used to react the sensor with reagents for immobilizing AFP antibody on the sensor and detecting AFP antigen. The measured mass of the detected AFP antigen was 6.02 pg at the concentration of 10 ng/ml, and 10.67 pg at 50 ng/ml when the immunoreaction time was 10 min

Integral Sensor Fault Detection and Isolation for Railway Traction Drive.

Science.gov (United States)

Garramiola, Fernando; Del Olmo, Jon; Poza, Javier; Madina, Patxi; Almandoz, Gaizka

2018-05-13

Due to the increasing importance of reliability and availability of electric traction drives in Railway applications, early detection of faults has become an important key for Railway traction drive manufacturers. Sensor faults are important sources of failures. Among the different fault diagnosis approaches, in this article an integral diagnosis strategy for sensors in traction drives is presented. Such strategy is composed of an observer-based approach for direct current (DC)-link voltage and catenary current sensors, a frequency analysis approach for motor current phase sensors and a hardware redundancy solution for speed sensors. None of them requires any hardware change requirement in the actual traction drive. All the fault detection and isolation approaches have been validated in a Hardware-in-the-loop platform comprising a Real Time Simulator and a commercial Traction Control Unit for a tram. In comparison to safety-critical systems in Aerospace applications, Railway applications do not need instantaneous detection, and the diagnosis is validated in a short time period for reliable decision. Combining the different approaches and existing hardware redundancy, an integral fault diagnosis solution is provided, to detect and isolate faults in all the sensors installed in the traction drive.

Early Detection of Plant Equipment Failures: A Case Study in Just-in-Time Maintenance

Energy Technology Data Exchange (ETDEWEB)

Parlos, Alexander G.; Kim, Kyusung; Bharadwaj, Raj M.

2001-06-17

The development and testing of a model-based fault detection system for electric motors is briefly presented. The fault detection system was developed using only motor nameplate information. The fault detection results presented utilize only motor voltage and current sensor information, minimizing the need for expensive or intrusive sensors. Dynamic recurrent neural networks are used to predict the input-output response of a three-phase induction motor while using an estimate of the motor speed signal. Multiresolution (or wavelet) signal-processing techniques are used in combination with more traditional methods to estimate fault features for use in winding insulation and motor mechanical and electromechanical failure detection.

Early Detection of Plant Equipment Failures: A Case Study in Just-in-Time Maintenance

International Nuclear Information System (INIS)

Parlos, Alexander G.; Kim, Kyusung; Bharadwaj, Raj M.

2001-01-01

The development and testing of a model-based fault detection system for electric motors is briefly presented. The fault detection system was developed using only motor nameplate information. The fault detection results presented utilize only motor voltage and current sensor information, minimizing the need for expensive or intrusive sensors. Dynamic recurrent neural networks are used to predict the input-output response of a three-phase induction motor while using an estimate of the motor speed signal. Multiresolution (or wavelet) signal-processing techniques are used in combination with more traditional methods to estimate fault features for use in winding insulation and motor mechanical and electromechanical failure detection

Hybrid architecture for building secure sensor networks

Science.gov (United States)

Owens, Ken R., Jr.; Watkins, Steve E.

2012-04-01

Sensor networks have various communication and security architectural concerns. Three approaches are defined to address these concerns for sensor networks. The first area is the utilization of new computing architectures that leverage embedded virtualization software on the sensor. Deploying a small, embedded virtualization operating system on the sensor nodes that is designed to communicate to low-cost cloud computing infrastructure in the network is the foundation to delivering low-cost, secure sensor networks. The second area focuses on securing the sensor. Sensor security components include developing an identification scheme, and leveraging authentication algorithms and protocols that address security assurance within the physical, communication network, and application layers. This function will primarily be accomplished through encrypting the communication channel and integrating sensor network firewall and intrusion detection/prevention components to the sensor network architecture. Hence, sensor networks will be able to maintain high levels of security. The third area addresses the real-time and high priority nature of the data that sensor networks collect. This function requires that a quality-of-service (QoS) definition and algorithm be developed for delivering the right data at the right time. A hybrid architecture is proposed that combines software and hardware features to handle network traffic with diverse QoS requirements.

An ethernet/IP security review with intrusion detection applications

International Nuclear Information System (INIS)

Laughter, S. A.; Williams, R. D.

2006-01-01

Supervisory Control and Data Acquisition (SCADA) and automation networks, used throughout utility and manufacturing applications, have their own specific set of operational and security requirements when compared to corporate networks. The modern climate of heightened national security and awareness of terrorist threats has made the security of these systems of prime concern. There is a need to understand the vulnerabilities of these systems and how to monitor and protect them. Ethernet/IP is a member of a family of protocols based on the Control and Information Protocol (CIP). Ethernet/IP allows automation systems to be utilized on and integrated with traditional TCP/IP networks, facilitating integration of these networks with corporate systems and even the Internet. A review of the CIP protocol and the additions Ethernet/IP makes to it has been done to reveal the kind of attacks made possible through the protocol. A set of rules for the SNORT Intrusion Detection software is developed based on the results of the security review. These can be used to monitor, and possibly actively protect, a SCADA or automation network that utilizes Ethernet/IP in its infrastructure. (authors)

Combined Colorimetric and Gravimetric CMUT Sensor for Detection of Phenylacetone

DEFF Research Database (Denmark)

Mølgaard, Mathias Johannes Grøndahl; Laustsen, Milan; Thygesen, Ida Lysgaard

2017-01-01

The detection of phenylacetone is of interest as it is a common precursor for the synthesis of (meth)amphetamine. Resonant gravimetric sensors can be used to detect the mass and hereby the concentration of a gas while colorimetric arrays typically have an exceptional selectivity to the target...... analyte if the right colorimetric dyes are chosen. We present a sensor system consisting of a Capacitive Micromachined Ultrasonic Transducer (CMUT) and a colorimetric array for detection of phenylacetone. The CMUT is used as a resonant gravimetric gas sensor where the resonance frequency shift due to mass...

Acoustic emission intrusion detector

International Nuclear Information System (INIS)

Carver, D.W.; Whittaker, J.W.

1980-01-01

An intrusion detector is provided for detecting a forcible entry into a secured structure while minimizing false alarms. The detector uses a piezoelectric crystal transducer to sense acoustic emissions. The transducer output is amplified by a selectable gain amplifier to control the sensitivity. The rectified output of the amplifier is applied to a Schmitt trigger circuit having a preselected threshold level to provide amplitude discrimination. Timing circuitry is provided which is activated by successive pulses from the Schmitt trigger which lie within a selected time frame for frequency discrimination. Detected signals having proper amplitude and frequency trigger an alarm within the first complete cycle time of a detected acoustical disturbance signal

A zinc fluorescent sensor used to detect mercury (II) and hydrosulfide.

Science.gov (United States)

Jung, Jae Min; Lee, Jae Jun; Nam, Eunju; Lim, Mi Hee; Kim, Cheal; Harrison, Roger G

2017-05-05

A zinc sensor based on quinoline and morpholine has been synthesized. The sensor selectively fluoresces in the presence of Zn 2+ , while not for other metal ions. Absorbance changes in the 350nm region are observed when Zn 2+ binds, which binds in a 1:1 ratio. The sensor fluoresces due to Zn 2+ above pH values of 6.0 and in the biological important region. The Zn 2+ -sensor complex has the unique ability to detect both Hg 2+ and HS - . The fluorescence of the Zn 2+ -sensor complex is quenched when it is exposed to aqueous solutions of Hg 2+ with sub-micromolar detection levels for Hg 2+ . The fluorescence of the Zn 2+ -sensor complex is also quenched by aqueous solutions of hydrosulfide. The sensor was used to detect Zn 2+ and Hg 2+ in living cells. Copyright © 2017 Elsevier B.V. All rights reserved.

A Survey on Cross-Layer Intrusion Detection System for Wireless ...

African Journals Online (AJOL)

pc

2018-03-05

Mar 5, 2018 ... excessive packet collision, artificially increases contention, decrease signal .... Intelligent security architecture was conducted by [36], as an intrusion ... the main disadvantages of this scheme is: The IDS node is static (runs ...

Radionuclide Sensors for Environmental Monitoring: From Flow Injection Solid-Phase Absorptiometry to Equilibration-Based Preconcentrating Minicolumn Sensors with Radiometric Detection

International Nuclear Information System (INIS)

Grate, Jay W.; Egorov, Oleg B.; O'Hara, Matthew J.; Devol, Timothy A.

2008-01-01

The development of in situ sensors for ultratrace detection applications in process control and environmental monitoring remains a significant challenge. Such sensors must meet difficult detection limit requirements while selectively detecting the analyte of interest in complex or otherwise challenging sample matrixes. Nowhere are these requirements more daunting than in the field of radionuclide sensing. The detection limit requirements can be extremely low. Nevertheless, a promising approach to radionuclide sensing based on preconcentrating minicolumn sensors has been developed. In addition, a method of operating such sensors, which we call equilibration-based sensing, has been developed that provides substantial preconcentration and a signal that is proportional to analyte concentration, while eliminating the need for reagents to regenerate the sorbent medium following each measurement. While this equilibration-based sensing method was developed for radionuclide sensing, it can be applied to nonradioactive species as well, given a suitable on-column detection system. By replacing costly sampling and laboratory analysis procedures, in situ sensors could have a significant impact on monitoring and long term stewardship applications. The aim of this review is to cover radionuclide sensors that combine some form of selective sorption with a radiometric detection method, and, as a primary aim, to comprehensively review preconcentrating minicolumn sensors for radionuclide detection. As a secondary aim, we will cover radionuclide sensors that combine sorption and scintillation in formats other than minicolumn sensors. We are particularly concerned with the detection of alpha- and beta-emitting radionuclides, which present particular challenges for measurements in liquid media

Efficient Hybrid Detection of Node Replication Attacks in Mobile Sensor Networks

Directory of Open Access Journals (Sweden)

Ze Wang

2017-01-01

Full Text Available The node replication attack is one of the notorious attacks that can be easily launched by adversaries in wireless sensor networks. A lot of literatures have studied mitigating the node replication attack in static wireless sensor networks. However, it is more difficult to detect the replicas in mobile sensor networks because of their node mobility. Considering the limitations of centralized detection schemes for static wireless sensor networks, a few distributed solutions have been recently proposed. Some existing schemes identified replicated attacks by sensing mobile nodes with identical ID but different locations. To facilitate the discovery of contradictory conflicts, we propose a hybrid local and global detection method. The local detection is performed in a local area smaller than the whole deployed area to improve the meeting probability of contradictory nodes, while the distant replicated nodes in larger area can also be efficiently detected by the global detection. The complementary two levels of detection achieve quick discovery by searching of the replicas with reasonable overhead.

Molecular Sensors for Moisture Detection by Moessbauer Spectroscopy

International Nuclear Information System (INIS)

Renz, F.; Souza, P. A. de; Klingelhoefer, G.; Goodwin, H. A.

2002-01-01

A parameter of importance in various industrial and commercial applications is sensitivity to moisture. A new class of molecular sensors which enable the qualitative and quantitative determination of air moisture (high selectivity and sensitivity) by application of Moessbauer spectroscopy as the probe technique has been investigated. The electronic properties of the iron-containing sensor depend upon the presence of moisture which is taken up by it and this process is accompanied by a change in electronic spin ground state which can be detected by Moessbauer spectroscopy. The sensor is suitable for in-field and industrial application using the recently developed Moessbauer spectrometer MIMOS II. Possible suitability for the detection of moisture in extraterrestrial environments is considered.

Novel Damage Detection Techniques for Structural Health Monitoring Using a Hybrid Sensor

Directory of Open Access Journals (Sweden)

Dengjiang Wang

2016-01-01

Full Text Available This study presents a technique for detecting fatigue cracks based on a hybrid sensor monitoring system consisting of a combination of intelligent coating monitoring (ICM and piezoelectric transducer (PZT sensors. An experimental procedure using this hybrid sensor system was designed to monitor the cracks generated by fatigue testing in plate structures. A probability of detection (POD model that quantifies the reliability of damage detection for a specific sensor or the nondestructive testing (NDT method was used to evaluate the weight factor for the ICM and PZT sensors. To estimate the uncertainty of model parameters in this study, the Bayesian method was employed. Realistic data from fatigue testing was used to validate the overall method, and the results show that the novel damage detection technique using a hybrid sensor can quantify fatigue cracks more accurately than results obtained by conventional sensor methods.

Quartz crystal microbalance sensor using ionophore for ammonium ion detection.

Science.gov (United States)

Kosaki, Yasuhiro; Takano, Kosuke; Citterio, Daniel; Suzuki, Koji; Shiratori, Seimei

2012-01-01

Ionophore-based quartz crystal microbalance (QCM) ammonium ion sensors with a detection limit for ammonium ion concentrations as low as 2.2 microM were fabricated. Ionophores are molecules, which selectively bind a particular ion. In this study, one of the known ionophores for ammonium, nonactin, was used to detect ammonium ions for environmental in-situ monitoring of aquarium water for the first time. To fabricate the sensing films, poly(vinyl chloride) was used as the matrix for the immobilization of nonactin. Furthermore, the anionic additive, tetrakis (4-chlorophenyl) borate potassium salt and the plasticizer dioctyl sebacate were used to enhance the sensor properties. The sensor allowed detecting ammonium ions not only in static solution, but also in flowing water. The sensor showed a nearly linear response with the increase of the ammonium ion concentration. The QCM resonance frequency increased with the increase of ammonium ion concentration, suggesting a decreasing weight of the sensing film. The detailed response mechanism could not be verified yet. However, from the results obtained when using a different plasticizer, nitrophenyl octyl ether, it is considered that this effect is caused by the release of water molecules. Consequently, the newly fabricated sensor detects ammonium ions by discharge of water. It shows high selectivity over potassium and sodium ions. We conclude that the newly fabricated sensor can be applied for detecting ammonium ions in aquarium water, since it allows measuring low ammonium ion concentrations. This sensor will be usable for water quality monitoring and controlling.

Boronic acid based imprinted electrochemical sensor for rutin recognition and detection.

Science.gov (United States)

Wang, Chunlei; Wang, Qi; Zhong, Min; Kan, Xianwen

2016-10-21

Multi-walled carbon nanotubes (MWNTs) and boronic acid based molecular imprinting polymer (MIP) were successively modified on a glassy carbon electrode surface to fabricate a novel electrochemical sensor for rutin recognition and detection. 3-Aminophenylboronic acid (APBA) was chosen as a monomer for the electropolymerization of MIP film in the presence of rutin. In addition to the imprinted cavities in MIP film to complement the template molecule in shape and functional groups, the high affinity between the boronic acid group of APBA and vicinal diols of rutin also enhanced the selectivity of the sensor, which made the sensor display a good selectivity to rutin. Moreover, the modified MWNTs improved the sensitivity of the sensor for rutin detection. The mole ratios of rutin and APBA, electropolymerized scan cycles and rates, and pH value of the detection solution were optimized. Under optimal conditions, the sensor was used to detect rutin in a linear range from 4.0 × 10 -7 to 1.0 × 10 -5 mol L -1 with a detection limit of 1.1 × 10 -7 mol L -1 . The sensor has also been applied to assay rutin in tablets with satisfactory results.

Piezoelectric microelectromechanical resonant sensors for chemical and biological detection.

Science.gov (United States)

Pang, Wei; Zhao, Hongyuan; Kim, Eun Sok; Zhang, Hao; Yu, Hongyu; Hu, Xiaotang

2012-01-07

Piezoelectric microelectromechanical systems (MEMS) resonant sensors, known for their excellent mass resolution, have been studied for many applications, including DNA hybridization, protein-ligand interactions, and immunosensor development. They have also been explored for detecting antigens, organic gas, toxic ions, and explosives. Most piezoelectric MEMS resonant sensors are acoustic sensors (with specific coating layers) that enable selective and label-free detection of biological events in real time. These label-free technologies have recently garnered significant attention for their sensitive and quantitative multi-parameter analysis of biological systems. Since piezoelectric MEMS resonant sensors do more than transform analyte mass or thickness into an electrical signal (e.g., frequency and impedance), special attention must be paid to their potential beyond microweighing, such as measuring elastic and viscous properties, and several types of sensors currently under development operate at different resonant modes (i.e., thickness extensional mode, thickness shear mode, lateral extensional mode, flexural mode, etc.). In this review, we provide an overview of recent developments in micromachined resonant sensors and activities relating to biochemical interfaces for acoustic sensors.

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.

Water Pollution Detection Based on Hypothesis Testing in Sensor Networks

Directory of Open Access Journals (Sweden)

Xu Luo

2017-01-01

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

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.

Intrusion Prevention and Detection in Grid Computing - The ALICE Case

Science.gov (United States)

Gomez, Andres; Lara, Camilo; Kebschull, Udo

2015-12-01

Grids allow users flexible on-demand usage of computing resources through remote communication networks. A remarkable example of a Grid in High Energy Physics (HEP) research is used in the ALICE experiment at European Organization for Nuclear Research CERN. Physicists can submit jobs used to process the huge amount of particle collision data produced by the Large Hadron Collider (LHC). Grids face complex security challenges. They are interesting targets for attackers seeking for huge computational resources. Since users can execute arbitrary code in the worker nodes on the Grid sites, special care should be put in this environment. Automatic tools to harden and monitor this scenario are required. Currently, there is no integrated solution for such requirement. This paper describes a new security framework to allow execution of job payloads in a sandboxed context. It also allows process behavior monitoring to detect intrusions, even when new attack methods or zero day vulnerabilities are exploited, by a Machine Learning approach. We plan to implement the proposed framework as a software prototype that will be tested as a component of the ALICE Grid middleware.

Intrusion Prevention and Detection in Grid Computing - The ALICE Case

International Nuclear Information System (INIS)

Gomez, Andres; Lara, Camilo; Kebschull, Udo

2015-01-01

Grids allow users flexible on-demand usage of computing resources through remote communication networks. A remarkable example of a Grid in High Energy Physics (HEP) research is used in the ALICE experiment at European Organization for Nuclear Research CERN. Physicists can submit jobs used to process the huge amount of particle collision data produced by the Large Hadron Collider (LHC). Grids face complex security challenges. They are interesting targets for attackers seeking for huge computational resources. Since users can execute arbitrary code in the worker nodes on the Grid sites, special care should be put in this environment. Automatic tools to harden and monitor this scenario are required. Currently, there is no integrated solution for such requirement. This paper describes a new security framework to allow execution of job payloads in a sandboxed context. It also allows process behavior monitoring to detect intrusions, even when new attack methods or zero day vulnerabilities are exploited, by a Machine Learning approach. We plan to implement the proposed framework as a software prototype that will be tested as a component of the ALICE Grid middleware. (paper)

Humidity detection using chitosan film based sensor

Science.gov (United States)

Nasution, T. I.; Nainggolan, I.; Dalimunthe, D.; Balyan, M.; Cuana, R.; Khanifah, S.

2018-02-01

A humidity sensor made of the natural polymer chitosan has been successfully fabricated in the film form by a solution casting method. Humidity testing was performed by placing a chitosan film sensor in a cooling machine room, model KT-2000 Ahu. The testing results showed that the output voltage values of chitosan film sensor increased with the increase in humidity percentage. For the increase in humidity percentage from 30 to 90% showed that the output voltage of chitosan film sensor increased from 32.19 to 138.75 mV. It was also found that the sensor evidenced good repeatability and stability during the testing. Therefore, chitosan has a great potential to be used as new sensing material for the humidity detection of which was cheaper and environmentally friendly.

Performance of UWB Array-Based Radar Sensor in a Multi-Sensor Vehicle-Based Suit for Landmine Detection

NARCIS (Netherlands)

Yarovoy, A.; Savelyev, T.; Zhuge, X.; Aubry, P.; Ligthart, L.; Schavemaker, J.G.M.; Tettelaar, P.; Breejen, E. de

2008-01-01

In this paper, integration of an UWB array-based timedomain radar sensor in a vehicle-mounted multi-sensor system for landmine detection is described. Dedicated real-time signal processing algorithms are developed to compute the radar sensor confidence map which is used for sensor fusion.

Sensitive Leptospira DNA detection using tapered optical fiber sensor.

Science.gov (United States)

Zainuddin, Nurul H; Chee, Hui Y; Ahmad, Muhammad Z; Mahdi, Mohd A; Abu Bakar, Muhammad H; Yaacob, Mohd H

2018-03-23

This paper presents the development of tapered optical fiber sensor to detect a specific Leptospira bacteria DNA. The bacteria causes Leptospirosis, a deadly disease but with common early flu-like symptoms. Optical single mode fiber (SMF) of 125 μm diameter is tapered to produce 12 μm waist diameter and 15 cm length. The novel DNA-based optical fiber sensor is functionalized by incubating the tapered region with sodium hydroxide (NaOH), (3-Aminopropyl) triethoxysilane and glutaraldehyde. Probe DNA is immobilized onto the tapered region and subsequently hybridized by its complementary DNA (cDNA). The transmission spectra of the DNA-based optical fiber sensor are measured in the 1500 to 1600 nm wavelength range. It is discovered that the shift of the wavelength in the SMF sensor is linearly proportional with the increase in the cDNA concentrations from 0.1 to 1.0 nM. The sensitivity of the sensor toward DNA is measured to be 1.2862 nm/nM and able to detect as low as 0.1 fM. The sensor indicates high specificity when only minimal shift is detected for non-cDNA testing. The developed sensor is able to distinguish between actual DNA of Leptospira serovars (Canicola and Copenhageni) against Clostridium difficile (control sample) at very low (femtomolar) target concentrations. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

LUSH-based SPR sensor for the detection of alcohols and pheromone

Science.gov (United States)

Lau, Hui-Chong; Lee, Yeon-Kyung; Kwon, Jae-Young; Sohn, Young-Soo; Lim, Jeong Ok

2013-05-01

Protein is a widely used sensing substrate in the biosensing technology. In the study conducted here, we used odorant binding protein, LUSH from Drosophila as a biosensing substrate in a miniaturized surface plasmon resonance (SPR) sensor. LUSH contains the specific alcohols binding sites, which mediates the detection of alcohols and pheromone. We first modified the surface of the gold sensor chip using the self assembled monolayer in the chloroform solution. The saturated concentration was determined prior to the detection of alcohols and pheromone at various concentrations. The results showed that the LUSH was saturated at 1000 μg/ml on the gold sensor chip. The detection response of LUSH was significant at higher concentration of alcohols. LUSH detected ethanol at concentration >=50% propanol was detected at >=25% whereas pheromone was detected at >=1.25 μg/μl. The results provide some fundamental information on the potential use of LUSH-based SPR as a simple and easy protein-based sensor in the near future.

Corrosion Detection of Reinforcement of Building Materials with Piezoelectric Sensors

Directory of Open Access Journals (Sweden)

Jia Peng

2017-06-01

Full Text Available The extensive use of reinforced materials in the construction industry has raised increased concerns about their safety and durability, while corrosion detection of steel materials is becoming increasingly important. For the scientific management, timely repair and health monitoring of construction materials, as well as to ensure construction safety and prevent accidents, this paper investigates corrosion detection on construction materials based on piezoelectric sensors. At present, the commonly used corrosion detection methods include physical and electrochemical methods, but there are shortcomings such as large equipment area, low detection frequency, and complex operation. In this study an improved piezoelectric ultrasonic sensor was designed, which could not only detect the internal defects of buildings while not causing structural damage, but also realize continuous detection and enable qualitative and quantitative assessment. Corrosion detection of reinforced building materials with piezoelectric sensors is quick and accurate, which can find hidden dangers and provide a reliable basis for the safety of the buildings.

Acoustic Detection Of Loose Particles In Pressure Sensors

Science.gov (United States)

Kwok, Lloyd C.

1995-01-01

Particle-impact-noise-detector (PIND) apparatus used in conjunction with computer program analyzing output of apparatus to detect extraneous particles trapped in pressure sensors. PIND tester essentially shaker equipped with microphone measuring noise in pressure sensor or other object being shaken. Shaker applies controlled vibration. Output of microphone recorded and expressed in terms of voltage, yielding history of noise subsequently processed by computer program. Data taken at sampling rate sufficiently high to enable identification of all impacts of particles on sensor diaphragm and on inner surfaces of sensor cavities.

Screen-printed fluorescent sensors for rapid and sensitive anthrax biomarker detection

International Nuclear Information System (INIS)

Lee, Inkyu; Oh, Wan-Kyu; Jang, Jyongsik

2013-01-01

Highlights: •We fabricated flexible anthrax sensors with a simple screen-printing method. •The sensors selectively detected B. anthracis biomarker. •The sensors provide the visible alarm against anthrax attack. -- Abstract: Since the 2001 anthrax attacks, efforts have focused on the development of an anthrax detector with rapid response and high selectivity and sensitivity. Here, we demonstrate a fluorescence sensor for detecting anthrax biomarker with high sensitivity and selectivity using a screen-printing method. A lanthanide–ethylenediamine tetraacetic acid complex was printed on a flexible polyethersulfone film. Screen-printing deposition of fluorescent detecting moieties produced fluorescent patterns that acted as a visual alarm against anthrax

Intrusion detection on oil pipeline right of way using monogenic signal representation

Science.gov (United States)

Nair, Binu M.; Santhaseelan, Varun; Cui, Chen; Asari, Vijayan K.

2013-05-01

We present an object detection algorithm to automatically detect and identify possible intrusions such as construction vehicles and equipment on the regions designated as the pipeline right-of-way (ROW) from high resolution aerial imagery. The pipeline industry has buried millions of miles of oil pipelines throughout the country and these regions are under constant threat of unauthorized construction activities. We propose a multi-stage framework which uses a pyramidal template matching scheme in the local phase domain by taking a single high resolution training image to classify a construction vehicle. The proposed detection algorithm makes use of the monogenic signal representation to extract the local phase information. Computing the monogenic signal from a two dimensional object region enables us to separate out the local phase information (structural details) from the local energy (contrast) thereby achieving illumination invariance. The first stage involves the local phase based template matching using only a single high resolution training image in a local region at multiple scales. Then, using the local phase histogram matching, the orientation of the detected region is determined and a voting scheme gives a certain weightage to the resulting clusters. The final stage involves the selection of clusters based on the number of votes attained and using the histogram of oriented phase feature descriptor, the object is located at the correct orientation and scale. The algorithm is successfully tested on four different datasets containing imagery with varying image resolution and object orientation.

Prioritizing alarms from sensor-based detection models in livestock production

DEFF Research Database (Denmark)

Dominiak, Katarina Nielsen; Kristensen, Anders Ringgaard

2017-01-01

The objective of this review is to present, evaluate and discuss methods for reducing false alarms in sensor-based detection models developed for livestock production as described in the scientific literature. Papers included in this review are all peer-reviewed and present sensor-based detection...... models developed for modern livestock production with the purpose of optimizing animal health or managerial routines. The papers must present a performance for the model, but no criteria were specified for animal species or the condition sought to be detected. 34 papers published during the last 20 years...... (NBN) and Hidden phase-type Markov model, the NBN shows the greatest potential for future reduction of alerts from sensor-based detection models in livestock production. The included detection models are evaluated on three criteria; performance, time-window and similarity to determine whether...

Reactive and multiphase modelling for the identification of monitoring parameters to detect CO2 intrusion into freshwater aquifers

Science.gov (United States)

Fahrner, S.; Schaefer, D.; Wiegers, C.; Köber, R.; Dahmke, A.

2011-12-01

A monitoring at geological CO2 storage sites has to meet environmental, regulative, financial and public demands and thus has to enable the detection of CO2 leakages. Current monitoring concepts for the detection of CO2 intrusion into freshwater aquifers located above saline storage formations in course of leakage events lack the identification of monitoring parameters. Their response to CO2 intrusion still has to be enlightened. Scenario simulations of CO2 intrusion in virtual synthetic aquifers are performed using the simulators PhreeqC and TOUGH2 to reveal relevant CO2-water-mineral interactions and multiphase behaviour on potential monitoring parameters. The focus is set on pH, total dissolved inorganic carbon (TIC) and the hydroelectric conductivity (EC). The study aims at identifying at which conditions the parameters react rapidly, durable and in a measurable degree. The depth of the aquifer, the mineralogy, the intrusion rates, the sorption specification and capacities, and groundwater flow velocities are varied in the course of the scenario modelling. All three parameters have been found suited in most scenarios. However, in case of a lack of calcite combined with low saturation of the water with respect to CO2 and shallow conditions, changes are close to the measurement resolution. Predicted changes in EC result from the interplay between carbonic acid production and its dissociation, and pH buffering by mineral dissolution. The formation of a discrete gas phase in cases of full saturation of the groundwater in confined aquifers illustrates the potential bipartite resistivity response: An increased hydroelectric conductivity at locations with dissolved CO2, and a high resistivity where the gas phase dominates the pore volume occupation. Increased hydrostatic pressure with depth and enhanced groundwater flow velocities enforce gas dissolution and diminish the formation of a discrete gas phase. Based on the results, a monitoring strategy is proposed which

Optimizing Systems of Threshold Detection Sensors

National Research Council Canada - National Science Library

Banschbach, David C

2008-01-01

.... Below the threshold all signals are ignored. We develop a mathematical model for setting individual sensor thresholds to obtain optimal probability of detecting a significant event, given a limit on the total number of false positives allowed...

Subsurface event detection and classification using Wireless Signal Networks.

Science.gov (United States)

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

2012-11-05

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

The Detection of Helicobacter hepaticus Using Whispering-Gallery Mode Microcavity Optical Sensors

Directory of Open Access Journals (Sweden)

Mark E. Anderson

2015-08-01

Full Text Available Current bacterial detection techniques are relatively slow, require bulky instrumentation, and usually require some form of specialized training. The gold standard for bacterial detection is culture testing, which can take several days to receive a viable result. Therefore, simpler detection techniques that are both fast and sensitive could greatly improve bacterial detection and identification. Here, we present a new method for the detection of the bacteria Helicobacter hepaticus using whispering-gallery mode (WGM optical microcavity-based sensors. Due to minimal reflection losses and low material adsorption, WGM-based sensors have ultra-high quality factors, resulting in high-sensitivity sensor devices. In this study, we have shown that bacteria can be non-specifically detected using WGM optical microcavity-based sensors. The minimum detection for the device was 1 × 104 cells/mL, and the minimum time of detection was found to be 750 s. Given that a cell density as low as 1 × 103 cells/mL for Helicobacter hepaticus can cause infection, the limit of detection shown here would be useful for most levels where Helicobacter hepaticus is biologically relevant. This study suggests a new approach for H. hepaticus detection using label-free optical sensors that is faster than, and potentially as sensitive as, standard techniques.

Smart CMOS image sensor for lightning detection and imaging.

Science.gov (United States)

Rolando, Sébastien; Goiffon, Vincent; Magnan, Pierre; Corbière, Franck; Molina, Romain; Tulet, Michel; Bréart-de-Boisanger, Michel; Saint-Pé, Olivier; Guiry, Saïprasad; Larnaudie, Franck; Leone, Bruno; Perez-Cuevas, Leticia; Zayer, Igor

2013-03-01

We present a CMOS image sensor dedicated to lightning detection and imaging. The detector has been designed to evaluate the potentiality of an on-chip lightning detection solution based on a smart sensor. This evaluation is performed in the frame of the predevelopment phase of the lightning detector that will be implemented in the Meteosat Third Generation Imager satellite for the European Space Agency. The lightning detection process is performed by a smart detector combining an in-pixel frame-to-frame difference comparison with an adjustable threshold and on-chip digital processing allowing an efficient localization of a faint lightning pulse on the entire large format array at a frequency of 1 kHz. A CMOS prototype sensor with a 256×256 pixel array and a 60 μm pixel pitch has been fabricated using a 0.35 μm 2P 5M technology and tested to validate the selected detection approach.

Damage and failure detection of composites using optical fiber vibration sensor

International Nuclear Information System (INIS)

Yang, Y. C.; Han, K. S.

2001-01-01

An intensity-based optical fiber vibration sensor is applied to detect and evaluate damages and fiber failure of composites. The optical fiber vibration sensor is constructed by placing two cleaved fiber end, one of which is cantilevered in a hollow glass tube. The movement of the cantilevered section lags behind the rest of the sensor in response to an applied vibration and the amount of light coupled between the two fibers is thereby modulated. Vibration characteristics of the optical fiber vibration sensor are investigated. Surface mounted optical fiber vibration sensor is used in tensile and indentation test. Experimental results show that the optical fiber sensor can detect damages and fiber failure of composites correctly

MicroSensors Systems: detection of a dismounted threat

Science.gov (United States)

Davis, Bill; Berglund, Victor; Falkofske, Dwight; Krantz, Brian

2005-05-01

The Micro Sensor System (MSS) is a layered sensor network with the goal of detecting dismounted threats approaching high value assets. A low power unattended ground sensor network is dependant on a network protocol for efficiency in order to minimize data transmissions after network establishment. The reduction of network 'chattiness' is a primary driver for minimizing power consumption and is a factor in establishing a low probability of detection and interception. The MSS has developed a unique protocol to meet these challenges. Unattended ground sensor systems are most likely dependant on batteries for power which due to size determines the ability of the sensor to be concealed after placement. To minimize power requirements, overcome size limitations, and maintain a low system cost the MSS utilizes advanced manufacturing processes know as Fluidic Self-Assembly and Chip Scale Packaging. The type of sensing element and the ability to sense various phenomenologies (particularly magnetic) at ranges greater than a few meters limits the effectiveness of a system. The MicroSensor System will overcome these limitations by deploying large numbers of low cost sensors, which is made possible by the advanced manufacturing process used in production of the sensors. The MSS program will provide unprecedented levels of real-time battlefield information which greatly enhances combat situational awareness when integrated with the existing Command, Control, Communications, Computers, Intelligence, Surveillance and Reconnaissance (C4ISR) infrastructure. This system will provide an important boost to realizing the information dominant, network-centric objective of Joint Vision 2020.

Sensors and Instrumentation towards early detection of osteoporosis

KAUST Repository

Afsarimanesh, Nasrin

2016-07-27

A label-free non-invasive sensing system for detection of C-terminal telopeptide of type-I collagen (CTX-I) has been developed in order to detect bone loss at an early stage, by Electrochemical Impedance Spectroscopy (EIS). A planar interdigital sensor was functionalized by immobilizing streptavidin agarose on the sensing area of the sensor to introduce selectivity for the antigen-antibody solution. Different concentrations of CTX-I were tested using the functionalized sensing surface to capture the target molecule. Preliminary results are provided in the paper assisted with the respective equivalent circuit of the working technology on the sensor using Complex Non-linear Least Square (CNLS). The results are encouraging and will be used to develop a complete system for commercialization complementing the existing systems. © 2016 IEEE.

Animals as Mobile Biological Sensors for Forest Fire Detection

Directory of Open Access Journals (Sweden)

Yasar Guneri Sahin

2007-12-01

Full Text Available This paper proposes a mobile biological sensor system that can assist in earlydetection of forest fires one of the most dreaded natural disasters on the earth. The main ideapresented in this paper is to utilize animals with sensors as Mobile Biological Sensors(MBS. The devices used in this system are animals which are native animals living inforests, sensors (thermo and radiation sensors with GPS features that measure thetemperature and transmit the location of the MBS, access points for wireless communicationand a central computer system which classifies of animal actions. The system offers twodifferent methods, firstly: access points continuously receive data about animals’ locationusing GPS at certain time intervals and the gathered data is then classified and checked tosee if there is a sudden movement (panic of the animal groups: this method is called animalbehavior classification (ABC. The second method can be defined as thermal detection(TD: the access points get the temperature values from the MBS devices and send the datato a central computer to check for instant changes in the temperatures. This system may beused for many purposes other than fire detection, namely animal tracking, poachingprevention and detecting instantaneous animal death.

Au nanoparticle-based sensor for apomorphine detection in plasma

Directory of Open Access Journals (Sweden)

Chiara Zanchi

2015-11-01

Full Text Available Artificially roughened gold surfaces with controlled nanostructure produced by pulsed laser deposition have been investigated as sensors for apomorphine detection aiming at clinical application. The use of such gold surfaces has been optimized using aqueous solutions of apomorphine in the concentration range between 3.3 × 10−4 M and 3.3 × 10−7 M. The experimental parameters have been investigated and the dynamic concentration range of the sensor has been assessed by the selection of two apomorphine surface enhanced Raman scattering (SERS peaks. The sensor behavior used to detect apomorphine in unfiltered human blood plasma is presented and discussed.

Damage Detection Response Characteristics of Open Circuit Resonant (SansEC) Sensors

Science.gov (United States)

Dudley, Kenneth L.; Szatkowski, George N.; Smith, Laura J.; Koppen, Sandra V.; Ely, Jay J.; Nguyen, Truong X.; Wang, Chuantong; Ticatch, Larry A.; Mielnik, John J.

2013-01-01

The capability to assess the current or future state of the health of an aircraft to improve safety, availability, and reliability while reducing maintenance costs has been a continuous goal for decades. Many companies, commercial entities, and academic institutions have become interested in Integrated Vehicle Health Management (IVHM) and a growing effort of research into "smart" vehicle sensing systems has emerged. Methods to detect damage to aircraft materials and structures have historically relied on visual inspection during pre-flight or post-flight operations by flight and ground crews. More quantitative non-destructive investigations with various instruments and sensors have traditionally been performed when the aircraft is out of operational service during major scheduled maintenance. Through the use of reliable sensors coupled with data monitoring, data mining, and data analysis techniques, the health state of a vehicle can be detected in-situ. NASA Langley Research Center (LaRC) is developing a composite aircraft skin damage detection method and system based on open circuit SansEC (Sans Electric Connection) sensor technology. Composite materials are increasingly used in modern aircraft for reducing weight, improving fuel efficiency, and enhancing the overall design, performance, and manufacturability of airborne vehicles. Materials such as fiberglass reinforced composites (FRC) and carbon-fiber-reinforced polymers (CFRP) are being used to great advantage in airframes, wings, engine nacelles, turbine blades, fairings, fuselage structures, empennage structures, control surfaces and aircraft skins. SansEC sensor technology is a new technical framework for designing, powering, and interrogating sensors to detect various types of damage in composite materials. The source cause of the in-service damage (lightning strike, impact damage, material fatigue, etc.) to the aircraft composite is not relevant. The sensor will detect damage independent of the cause

Development and evaluation of a decision-supporting model for identifying the source location of microbial intrusions in real gravity sewer systems.

Science.gov (United States)

Kim, Minyoung; Choi, Christopher Y; Gerba, Charles P

2013-09-01

Assuming a scenario of a hypothetical pathogenic outbreak, we aimed this study at developing a decision-support model for identifying the location of the pathogenic intrusion as a means of facilitating rapid detection and efficient containment. The developed model was applied to a real sewer system (the Campbell wash basin in Tucson, AZ) in order to validate its feasibility. The basin under investigation was divided into 14 sub-basins. The geometric information associated with the sewer network was digitized using GIS (Geological Information System) and imported into an urban sewer network simulation model to generate microbial breakthrough curves at the outlet. A pre-defined amount of Escherichia coli (E. coli), which is an indicator of fecal coliform bacteria, was hypothetically introduced into 56 manholes (four in each sub-basin, chosen at random), and a total of 56 breakthrough curves of E. coli were generated using the simulation model at the outlet. Transport patterns were classified depending upon the location of the injection site (manhole), various known characteristics (peak concentration and time, pipe length, travel time, etc.) extracted from each E. coli breakthrough curve and the layout of sewer network. Using this information, we back-predicted the injection location once an E. coli intrusion was detected at a monitoring site using Artificial Neural Networks (ANNs). The results showed that ANNs identified the location of the injection sites with 57% accuracy; ANNs correctly recognized eight out of fourteen expressions with relying on data from a single detection sensor. Increasing the available sensors within the basin significantly improved the accuracy of the simulation results (from 57% to 100%). Copyright © 2013 Elsevier Ltd. All rights reserved.

The Efficacy of Epidemic Algorithms on Detecting Node Replicas in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Narasimha Shashidhar

2015-12-01

Full Text Available A node replication attack against a wireless sensor network involves surreptitious efforts by an adversary to insert duplicate sensor nodes into the network while avoiding detection. Due to the lack of tamper-resistant hardware and the low cost of sensor nodes, launching replication attacks takes little effort to carry out. Naturally, detecting these replica nodes is a very important task and has been studied extensively. In this paper, we propose a novel distributed, randomized sensor duplicate detection algorithm called Discard to detect node replicas in group-deployed wireless sensor networks. Our protocol is an epidemic, self-organizing duplicate detection scheme, which exhibits emergent properties. Epidemic schemes have found diverse applications in distributed computing: load balancing, topology management, audio and video streaming, computing aggregate functions, failure detection, network and resource monitoring, to name a few. To the best of our knowledge, our algorithm is the first attempt at exploring the potential of this paradigm to detect replicas in a wireless sensor network. Through analysis and simulation, we show that our scheme achieves robust replica detection with substantially lower communication, computational and storage requirements than prior schemes in the literature.

A Self-Learning Sensor Fault Detection Framework for Industry Monitoring IoT

Directory of Open Access Journals (Sweden)

Yu Liu

2013-01-01

Full Text Available Many applications based on Internet of Things (IoT technology have recently founded in industry monitoring area. Thousands of sensors with different types work together in an industry monitoring system. Sensors at different locations can generate streaming data, which can be analyzed in the data center. In this paper, we propose a framework for online sensor fault detection. We motivate our technique in the context of the problem of the data value fault detection and event detection. We use the Statistics Sliding Windows (SSW to contain the recent sensor data and regress each window by Gaussian distribution. The regression result can be used to detect the data value fault. Devices on a production line may work in different workloads and the associate sensors will have different status. We divide the sensors into several status groups according to different part of production flow chat. In this way, the status of a sensor is associated with others in the same group. We fit the values in the Status Transform Window (STW to get the slope and generate a group trend vector. By comparing the current trend vector with history ones, we can detect a rational or irrational event. In order to determine parameters for each status group we build a self-learning worker thread in our framework which can edit the corresponding parameter according to the user feedback. Group-based fault detection (GbFD algorithm is proposed in this paper. We test the framework with a simulation dataset extracted from real data of an oil field. Test result shows that GbFD detects 95% sensor fault successfully.

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.

Laboratory sensor design for fiber-optic detection of 85Kr

International Nuclear Information System (INIS)

Geelhood, B.D.; Knopf, M.A.

1994-06-01

The goal of the fiber-optic detection of 85 Kr project is to produce a sensor to detect 85 Kr in real-time from either an airborne or ground-based platform. The 85 Kr gas is a fission product which is released in large quantities during fuel reprocessing and in minor quantities during nuclear reactor operations. Thus an airborne plume of 85 Kr is a radioactive signature of proliferation. Since 85 Kr has a 10.72 year half life, it is difficult for a proliferator to contain the gas for several half lives to avoid releasing the radioactive signature of proliferation. The long half life also results in a plume that can extend several kilometers from the source, which allows initial proliferation monitoring from large distances. The sensor can be used to make stand-alone, real-time measurements of 85 Kr that can be used as direct evidence for proliferation and/or as a screening sensor to determine when to collect air samples for further laboratory analysis. This report provides a summary of the 85 Kr beta sensor design that PNL will use in the laboratory to: (1) demonstrate the measurement technique, (2) establish minimum detection limits, and (3) optimize the sensor design for the final airborne sensor package. The goal of the final airborne sensor package will be to measure 85 Kr at activity levels as low as or as close to ambient background levels as possible with a reasonably sized sensor

Multiwalled carbon nanotubes sensor for organic liquid detection at room temperature

Science.gov (United States)

Chaudhary, Deepti; Khare, Neeraj; Vankar, V. D.

2016-04-01

We have explored the possibility of using multiwalled carbon nanotubes (MWCNTs) as room temperature chemical sensor for the detection of organic liquids such as ethanol, propanol, methanol and toluene. MWCNTs were synthesized by thermal chemical vapor deposition (TCVD) technique. The interdigitated electrodes were fabricated by conventional photolithography technique. The sensor was fabricated by drop depositing MWCNT suspension onto the interdigitated electrodes. The sensing properties of MWCNTs sensor was studied for organic liquids detection. The resistance of sensor was found to increase upon exposure to these liquids. Sensor shows good reversibility and fast response at room temperature. Charge transfer between the organic liquid and sensing element is the dominant sensing mechanism.

Design and characterization of a magnetoelastic sensor for the detection of biological agents

International Nuclear Information System (INIS)

Shen Wen; Mathison, Leslie C; Chin, Bryan A; Petrenko, Valery A

2010-01-01

This paper presents the design and development of a free-standing, magnetoelastic biosensor. The detection principle is presented and various resonance characteristics of the sensor are discussed. Experimental measurements of the sensor resonance frequencies agree with theoretical predictions. The influence of the external magnetic field on the resonance behaviour of the sensor was studied and the optimum dc magnetic fields for best sensitivity in air and in water solutions for 2000 x 400 x 15 μm (2 mm) sensors and 1000 x 200 x 15 μm (1 mm) size sensors were determined to be 75 Oe and 38 Oe, respectively. Both theoretical prediction and experimental results show that smaller sensors have greater mass sensitivity and can theoretically detect mass as small as one biological spore. The sensor platform was immobilized with JRB7 phages for specific, in vitro detection of B. anthracis spores. Real-time detection of spores suspended in water was demonstrated using a flowing system. The 1 mm and 2 mm sensors were found to have a detection limit of 10 4 spores ml -1 and 10 5 spores ml -1 , respectively.

DHT-Based Detection of Node Clone in Wireless Sensor Networks

Science.gov (United States)

Li, Zhijun; Gong, Guang

Wireless sensor networks are vulnerable to the node clone attack because of low-cost, resource-constrained sensor nodes, and uncontrolled environments where they are left unattended. Several distributed protocols have been proposed for detecting clone. However, some protocols rely on an implicit assumption that every node is aware of all other nodes' existence; other protocols using an geographic hash table require that nodes know the general network deployment graph. Those assumptions hardly hold for many sensor networks. In this paper, we present a novel node clone detection protocol based on Distributed Hash Table (DHT). DHT provides good distributed properties and our protocol is practical for every kind of sensor networks. We analyze the protocol performance theoretically. Moreover, we implement our protocol in the OMNeT++ simulation framework. The extensive simulation results show that our protocol can detect clone efficiently and holds strong resistance against adversaries.

A PVC/polypyrrole sensor designed for beef taste detection using electrochemical methods and sensory evaluation.

Science.gov (United States)

Zhu, Lingtao; Wang, Xiaodan; Han, Yunxiu; Cai, Yingming; Jin, Jiahui; Wang, Hongmei; Xu, Liping; Wu, Ruijia

2018-03-01

An electrochemical sensor for detection of beef taste was designed in this study. This sensor was based on the structure of polyvinyl chloride/polypyrrole (PVC/PPy), which was polymerized onto the surface of a platinum (Pt) electrode to form a Pt-PPy-PVC film. Detecting by electrochemical methods, the sensor was well characterized by electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV). The sensor was applied to detect 10 rib-eye beef samples and the accuracy of the new sensor was validated by sensory evaluation and ion sensor detection. Several cluster analysis methods were used in the study to distinguish the beef samples. According to the obtained results, the designed sensor showed a high degree of association of electrochemical detection and sensory evaluation, which proved a fast and precise sensor for beef taste detection. Copyright © 2017 Elsevier Ltd. All rights reserved.

Design and development of an optical fiber sensor for hydrogen detection

International Nuclear Information System (INIS)

Perrotton, Cedric

2012-01-01

Hydrogen detection is an environmental priority. Numerous hydrogen sensors have been developed, but none of them meet the industry requirements. Optical fiber sensors, electrically isolated, are excellent candidates for operating in explosive environments. Our goal is to develop an intrinsic optical fiber sensor based on Surface Plasmon Resonance. In this thesis, we study two optical fiber hydrogen sensors. The first sensor, based on amplitude modulation, consists of a thin Pd layer deposited on the multimode fiber core, after removing the optical cladding. The second design, based on wavelength modulation, consists of replacing the single Pd layer by a Au/SiO 2 /Pd multilayer stack. We demonstrate in this thesis that plasmonic sensors may be a solution to develop fast and reliable fiber hydrogen sensors. Finally, we study Mg alloys as hydrogen sensitive material in order to improve the detection range of hydrogen sensors. (author)

Infrared processing and sensor fusion for anti-personnel land-mine detection

NARCIS (Netherlands)

Schavemaker, J.G.M.; Cremer, F.; Schutte, K.; Breejen, E. den

2000-01-01

In this paper we present the results of infrared processing and sensor fusion obtained within the European research project GEODE (Ground Explosive Ordnance DEtection) that strives for the realization of a vehicle-mounted, multi-sensor anti-personnel land-mine detection system for humanitarian

Sensor Fusion of Position- and Micro-Sensors (MEMS) integrated in a Wireless Sensor Network for movement detection in landslide areas

Science.gov (United States)

Arnhardt, Christian; Fernández-Steeger, Tomas; Azzam, Rafig

2010-05-01

Monitoring systems in landslide areas are important elements of effective Early Warning structures. Data acquisition and retrieval allows the detection of movement processes and thus is essential to generate warnings in time. Apart from the precise measurement, the reliability of data is fundamental, because outliers can trigger false alarms and leads to the loss of acceptance of such systems. For the monitoring of mass movements and their risk it is important to know, if there is movement, how fast it is and how trustworthy is the information. The joint project "Sensorbased landslide early warning system" (SLEWS) deals with these questions, and tries to improve data quality and to reduce false alarm rates, due to the combination of sensor date (sensor fusion). The project concentrates on the development of a prototypic Alarm- and Early Warning system (EWS) for different types of landslides by using various low-cost sensors, integrated in a wireless sensor network (WSN). The network consists of numerous connection points (nodes) that transfer data directly or over other nodes (Multi-Hop) in real-time to a data collection point (gateway). From there all the data packages are transmitted to a spatial data infrastructure (SDI) for further processing, analyzing and visualizing with respect to end-user specifications. The ad-hoc characteristic of the network allows the autonomous crosslinking of the nodes according to existing connections and communication strength. Due to the independent finding of new or more stable connections (self healing) a breakdown of the whole system is avoided. The bidirectional data stream enables the receiving of data from the network but also allows the transfer of commands and pointed requests into the WSN. For the detection of surface deformations in landslide areas small low-cost Micro-Electro-Mechanical-Systems (MEMS) and positionsensors from the automobile industries, different industrial applications and from other measurement

On the importance of sensor height variation for detection of magnetic labels by magnetoresistive sensors

DEFF Research Database (Denmark)

Henriksen, Anders Dahl; Wang, Shan Xiang; Hansen, Mikkel Fougt

2015-01-01

Magnetoresistive sensors are widely used for biosensing by detecting the signal from magnetic labels bound to a functionalized area that usually covers the entire sensor structure. Magnetic labels magnetized by a homogeneous applied magnetic field weaken and strengthen the applied field when...

Structural damage detection-oriented multi-type sensor placement with multi-objective optimization

Science.gov (United States)

Lin, Jian-Fu; Xu, You-Lin; Law, Siu-Seong

2018-05-01

A structural damage detection-oriented multi-type sensor placement method with multi-objective optimization is developed in this study. The multi-type response covariance sensitivity-based damage detection method is first introduced. Two objective functions for optimal sensor placement are then introduced in terms of the response covariance sensitivity and the response independence. The multi-objective optimization problem is formed by using the two objective functions, and the non-dominated sorting genetic algorithm (NSGA)-II is adopted to find the solution for the optimal multi-type sensor placement to achieve the best structural damage detection. The proposed method is finally applied to a nine-bay three-dimensional frame structure. Numerical results show that the optimal multi-type sensor placement determined by the proposed method can avoid redundant sensors and provide satisfactory results for structural damage detection. The restriction on the number of each type of sensors in the optimization can reduce the searching space in the optimization to make the proposed method more effective. Moreover, how to select a most optimal sensor placement from the Pareto solutions via the utility function and the knee point method is demonstrated in the case study.

Opto-mechanical lab-on-fibre seismic sensors detected the Norcia earthquake.

Science.gov (United States)

Pisco, Marco; Bruno, Francesco Antonio; Galluzzo, Danilo; Nardone, Lucia; Gruca, Grzegorz; Rijnveld, Niek; Bianco, Francesca; Cutolo, Antonello; Cusano, Andrea

2018-04-27

We have designed and developed lab-on-fibre seismic sensors containing a micro-opto-mechanical cavity on the fibre tip. The mechanical cavity is designed as a double cantilever suspended on the fibre end facet and connected to a proof mass to tune its response. Ground acceleration leads to displacement of the cavity length, which in turn can be remotely detected using an interferometric interrogation technique. After the sensors characterization, an experimental validation was conducted at the Italian National Institute of Geophysics and Volcanology (INGV), which is responsible for seismic surveillance over the Italian country. The fabricated sensors have been continuously used for long periods to demonstrate their effectiveness as seismic accelerometer sensors. During the tests, fibre optic seismic accelerometers clearly detected the seismic sequence that culminated in the severe Mw6.5 Norcia earthquake that struck central Italy on October 30, 2016. The seismic data provided by the optical sensors were analysed by specialists at the INGV. The wave traces were compared with state-of-the-art traditional sensors typically incorporated into the INGV seismic networks. The comparison verifies the high fidelity of the optical sensors in seismic wave detection, indicating their suitability for a novel class of seismic sensors to be employed in practical scenarios.

Sensor and instrumentation for progesterone detection

KAUST Repository

Zia, Asif I.

2012-05-01

The reported research work uses a real time and noninvasive method to detect progesterone hormone concentration in purified water using Electrochemical Impedance Spectroscopy (E.I.S.) technique. Planar capacitive sensor, consisting of inter-digitated microelectrodes, is designed and fabricated on silicon substrate using thin-film Microelectromechanical system (MEMS) based semiconductor device fabrication technology. The sensor in conjunction with EIS is used to evaluate conductivity, permeability and dielectric properties of reproductive hormone progesterone and its concentration quantification in purified water. Impedance spectrums are obtained with various concentrations of the hormone in purified water by using an electric circuit in order to extract sample conductance. Relationship of sample conductance with progesterone concentration level is studied in this research work. The ability of E.I.S. to detect progesterone concentration is aimed to be used in dairy farming industry in order to obtain better reproductive performance of the dairy cattle. © 2012 IEEE.

Sensor and instrumentation for progesterone detection

KAUST Repository

Zia, Asif I.; Mohd. Syaifudin, A. R.; Mukhopadhyay, Subhas Chandra; Yu, Paklam; Al-Bahadly, Ibrahim H.; Kosel, Jü rgen; Gooneratne, Chinthaka Pasan

2012-01-01

The reported research work uses a real time and noninvasive method to detect progesterone hormone concentration in purified water using Electrochemical Impedance Spectroscopy (E.I.S.) technique. Planar capacitive sensor, consisting of inter-digitated microelectrodes, is designed and fabricated on silicon substrate using thin-film Microelectromechanical system (MEMS) based semiconductor device fabrication technology. The sensor in conjunction with EIS is used to evaluate conductivity, permeability and dielectric properties of reproductive hormone progesterone and its concentration quantification in purified water. Impedance spectrums are obtained with various concentrations of the hormone in purified water by using an electric circuit in order to extract sample conductance. Relationship of sample conductance with progesterone concentration level is studied in this research work. The ability of E.I.S. to detect progesterone concentration is aimed to be used in dairy farming industry in order to obtain better reproductive performance of the dairy cattle. © 2012 IEEE.

Human intrusion: New ideas?

International Nuclear Information System (INIS)

Cooper, J.R.

2002-01-01

Inadvertent human intrusion has been an issue for the disposal of solid radioactive waste for many years. This paper discusses proposals for an approach for evaluating the radiological significance of human intrusion as put forward by ICRP with contribution from work at IAEA. The approach focuses on the consequences of the intrusion. Protective actions could, however, include steps to reduce the probability of human intrusion as well as the consequences. (author)

A novel bicistronic sensor vector for detecting caspase-3 activation.

Science.gov (United States)

Vagner, Tatyana; Mouravlev, Alexandre; Young, Deborah

2015-01-01

Apoptosis is involved in pathological cell death of a wide range of human diseases. One of the most important biochemical markers of apoptosis is activation of caspase-3. Ability to detect caspase-3 activation early in the pathological process is important for determining the timing for interfering with apoptosis initiation and prevention of cell damage. Techniques allowing detection of caspase-3 activity at a single cell level show increased sensitivity, compared to biochemical assays; therefore, we developed a novel bicistronic caspase-3 sensor vector enabling detection of caspase-3 activity in individual cells. We employed green fluorescent protein (GFP) as a reporter for caspase-3 activation in our constructs and assessed the functionality of the generated constructs in transiently transfected Neuro2A and HEK293 cells under basal conditions and following application of okadaic acid (OA) or staurosporine (STS) to induce apoptosis. To ensure responsiveness of the new sensor vector to active caspase-3, we co-transfected the sensor with plasmid(s) overexpressing active caspase-3 and quantified GFP fluorescence using a plate reader. We observed an increase in GFP expression in cells transfected with the new bicistronic caspase-3 sensor in response to both OA and STS. We also showed a significant increase in GFP fluorescence intensity in cells co-expressing the sensor with the plasmid(s) encoding active caspase-3. We generated a novel bicistronic caspase-3 sensor vector which relies on a transcription factor/response element system. The obtained sensor combines high sensitivity of the single cell level detection with the possibility of automated quantification. Copyright © 2015 Elsevier Inc. All rights reserved.

Vision Sensor-Based Road Detection for Field Robot Navigation

Directory of Open Access Journals (Sweden)

Keyu Lu

2015-11-01

Full Text Available Road detection is an essential component of field robot navigation systems. Vision sensors play an important role in road detection for their great potential in environmental perception. In this paper, we propose a hierarchical vision sensor-based method for robust road detection in challenging road scenes. More specifically, for a given road image captured by an on-board vision sensor, we introduce a multiple population genetic algorithm (MPGA-based approach for efficient road vanishing point detection. Superpixel-level seeds are then selected in an unsupervised way using a clustering strategy. Then, according to the GrowCut framework, the seeds proliferate and iteratively try to occupy their neighbors. After convergence, the initial road segment is obtained. Finally, in order to achieve a globally-consistent road segment, the initial road segment is refined using the conditional random field (CRF framework, which integrates high-level information into road detection. We perform several experiments to evaluate the common performance, scale sensitivity and noise sensitivity of the proposed method. The experimental results demonstrate that the proposed method exhibits high robustness compared to the state of the art.

Wireless Sensor Networks for Heritage Object Deformation Detection and Tracking Algorithm

Directory of Open Access Journals (Sweden)

Zhijun Xie

2014-10-01

Full Text Available Deformation is the direct cause of heritage object collapse. It is significant to monitor and signal the early warnings of the deformation of heritage objects. However, traditional heritage object monitoring methods only roughly monitor a simple-shaped heritage object as a whole, but cannot monitor complicated heritage objects, which may have a large number of surfaces inside and outside. Wireless sensor networks, comprising many small-sized, low-cost, low-power intelligent sensor nodes, are more useful to detect the deformation of every small part of the heritage objects. Wireless sensor networks need an effective mechanism to reduce both the communication costs and energy consumption in order to monitor the heritage objects in real time. In this paper, we provide an effective heritage object deformation detection and tracking method using wireless sensor networks (EffeHDDT. In EffeHDDT, we discover a connected core set of sensor nodes to reduce the communication cost for transmitting and collecting the data of the sensor networks. Particularly, we propose a heritage object boundary detecting and tracking mechanism. Both theoretical analysis and experimental results demonstrate that our EffeHDDT method outperforms the existing methods in terms of network traffic and the precision of the deformation detection.

Network hydraulics inclusion in water quality event detection using multiple sensor stations data.

Science.gov (United States)

Oliker, Nurit; Ostfeld, Avi

2015-09-01

Event detection is one of the current most challenging topics in water distribution systems analysis: how regular on-line hydraulic (e.g., pressure, flow) and water quality (e.g., pH, residual chlorine, turbidity) measurements at different network locations can be efficiently utilized to detect water quality contamination events. This study describes an integrated event detection model which combines multiple sensor stations data with network hydraulics. To date event detection modelling is likely limited to single sensor station location and dataset. Single sensor station models are detached from network hydraulics insights and as a result might be significantly exposed to false positive alarms. This work is aimed at decreasing this limitation through integrating local and spatial hydraulic data understanding into an event detection model. The spatial analysis complements the local event detection effort through discovering events with lower signatures by exploring the sensors mutual hydraulic influences. The unique contribution of this study is in incorporating hydraulic simulation information into the overall event detection process of spatially distributed sensors. The methodology is demonstrated on two example applications using base runs and sensitivity analyses. Results show a clear advantage of the suggested model over single-sensor event detection schemes. Copyright © 2015 Elsevier Ltd. All rights reserved.

Mobile Sensor Networks for Leak and Backflow Detection in Water Distribution Systems

KAUST Repository

Suresh, M. Agumbe; Smith, L.; Rasekh, A.; Stoleru, R.; Banks, M.K.; Shihada, Basem

2014-01-01

Leak and backflow detection are essential aspects of Water Distribution System (WDS) monitoring. Most existing solutions for leak/backflow detection in WDSs focus on the placement of expensive static sensors located strategically. In contrast to these, we propose a solution whereby mobile sensors (i.e., their movement aided only by the inherent water flow in the system) detect leaks/backflow. Information about the leaks/backflow is collected from the sensors either by physically capturing them, or through wireless communication. Specifically, we propose models to maximize leak/backflow detection given a cost constraint (a limit on the number of sensors). Through extensive simulations, we demonstrate the superior performance of our proposed solution when compared with the state of the art solutions (e.g., algorithms/protocols and analysis).

Mobile Sensor Networks for Leak and Backflow Detection in Water Distribution Systems

KAUST Repository

Suresh, M. Agumbe

2014-05-01

Leak and backflow detection are essential aspects of Water Distribution System (WDS) monitoring. Most existing solutions for leak/backflow detection in WDSs focus on the placement of expensive static sensors located strategically. In contrast to these, we propose a solution whereby mobile sensors (i.e., their movement aided only by the inherent water flow in the system) detect leaks/backflow. Information about the leaks/backflow is collected from the sensors either by physically capturing them, or through wireless communication. Specifically, we propose models to maximize leak/backflow detection given a cost constraint (a limit on the number of sensors). Through extensive simulations, we demonstrate the superior performance of our proposed solution when compared with the state of the art solutions (e.g., algorithms/protocols and analysis).

Intelligent Intrusion Detection of Grey Hole and Rushing Attacks in Self-Driving Vehicular Networks

Directory of Open Access Journals (Sweden)

Khattab M. Ali Alheeti

2016-07-01

Full Text Available Vehicular ad hoc networks (VANETs play a vital role in the success of self-driving and semi self-driving vehicles, where they improve safety and comfort. Such vehicles depend heavily on external communication with the surrounding environment via data control and Cooperative Awareness Messages (CAMs exchanges. VANETs are potentially exposed to a number of attacks, such as grey hole, black hole, wormhole and rushing attacks. This work presents an intelligent Intrusion Detection System (IDS that relies on anomaly detection to protect the external communication system from grey hole and rushing attacks. These attacks aim to disrupt the transmission between vehicles and roadside units. The IDS uses features obtained from a trace file generated in a network simulator and consists of a feed-forward neural network and a support vector machine. Additionally, the paper studies the use of a novel systematic response, employed to protect the vehicle when it encounters malicious behaviour. Our simulations of the proposed detection system show that the proposed schemes possess outstanding detection rates with a reduction in false alarms. This safe mode response system has been evaluated using four performance metrics, namely, received packets, packet delivery ratio, dropped packets and the average end to end delay, under both normal and abnormal conditions.

Oxidative stress detection by MEMS cantilever sensor array based electronic nose

Science.gov (United States)

Gupta, Anurag; Singh, T. Sonamani; Singh, Priyanka; Yadava, R. D. S.

2018-05-01

This paper is concerned with analyzing the role of polymer swelling induced surface stress in MEMS chemical sensors. The objective is to determine the impact of surface stress on the chemical discrimination ability of MEMS resonator sensors. We considered a case study of hypoxia detection by MEMS sensor array and performed several types of simulation experiments for detection of oxidative stress volatile organic markers in human breath. Both types of sensor response models that account for the surface stress effect and that did not were considered for the analyses in comparison. It is found that the surface stress (hence the polymer swelling) provides better chemical discrimination ability to polymer coated MEMS sensors.

Detection and isolation of routing attacks through sensor watermarking

NARCIS (Netherlands)

Ferrari, R.; Herdeiro Teixeira, A.M.; Sun, J; Jiang, Z-P

2017-01-01

In networked control systems, leveraging the peculiarities of the cyber-physical domains and their interactions may lead to novel detection and defense mechanisms against malicious cyber-attacks. In this paper, we propose a multiplicative sensor watermarking scheme, where each sensor's output is

Phosphatase activity tunes two-component system sensor detection threshold.

Science.gov (United States)

Landry, Brian P; Palanki, Rohan; Dyulgyarov, Nikola; Hartsough, Lucas A; Tabor, Jeffrey J

2018-04-12

Two-component systems (TCSs) are the largest family of multi-step signal transduction pathways in biology, and a major source of sensors for biotechnology. However, the input concentrations to which biosensors respond are often mismatched with application requirements. Here, we utilize a mathematical model to show that TCS detection thresholds increase with the phosphatase activity of the sensor histidine kinase. We experimentally validate this result in engineered Bacillus subtilis nitrate and E. coli aspartate TCS sensors by tuning their detection threshold up to two orders of magnitude. We go on to apply our TCS tuning method to recently described tetrathionate and thiosulfate sensors by mutating a widely conserved residue previously shown to impact phosphatase activity. Finally, we apply TCS tuning to engineer B. subtilis to sense and report a wide range of fertilizer concentrations in soil. This work will enable the engineering of tailor-made biosensors for diverse synthetic biology applications.

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.

MEMS Skin Friction Sensor, Phase I

Data.gov (United States)

National Aeronautics and Space Administration — Interdisciplinary Consulting Corporation proposes a sensor that offers the unique capability to make non-intrusive, direct, simultaneous mean and fluctuating shear...

REMOTE DETECTION OF INTERNAL PIPELINE CORROSION USING FLUIDIZED SENSORS

Energy Technology Data Exchange (ETDEWEB)

Narasi Sridhar; Garth Tormoen; Ashok Sabata

2005-10-31

Pipelines present a unique challenge to monitoring because of the great geographical distances they cover, their burial depth, their age, and the need to keep the product flowing without much interruption. Most other engineering structures that require monitoring do not pose such combined challenges. In this regard, a pipeline system can be considered analogous to the blood vessels in the human body. The human body has an extensive ''pipeline'' through which blood and other fluids are transported. The brain can generally sense damage to the system at any location and alert the body to provide temporary repair, unless the damage is severe. This is accomplished through a vast network of fixed and floating sensors combined with a vast and extremely complex communication/decision making system. The project described in this report mimics the distributed sensor system of our body, albeit in a much more rudimentary fashion. Internal corrosion is an important factor in pipeline integrity management. At present, the methods to assess internal corrosion in pipelines all have certain limitations. In-line inspection tools are costly and cannot be used in all pipelines. Because there is a significant time interval between inspections, any impact due to upsets in pipeline operations can be missed. Internal Corrosion Direct Assessment (ICDA) is a procedure that can be used to identify locations of possible internal corrosion. However, the uncertainties in the procedure require excavation and location of damage using more detailed inspection tools. Non-intrusive monitoring techniques can be used to monitor internal corrosion, but these tools also require pipeline excavation and are limited in the spatial extent of corrosion they can examine. Therefore, a floating sensor system that can deposit at locations of water accumulation and communicate the corrosion information to an external location is needed. To accomplish this, the project is divided into four main

Detection of Aeromonas hydrophila Using Fiber Optic Microchannel Sensor

Directory of Open Access Journals (Sweden)

Samla Gauri

2017-01-01

Full Text Available This research focuses on the detection of Aeromonas hydrophila using fiber optic microchannel biosensor. Microchannel was fabricated by photolithography method. The fiber optic was chosen as signal transmitting medium and light absorption characteristic of different microorganisms was investigated for possible detection. Experimental results showed that Aeromonas hydrophila can be detected at the region of UV-Vis spectra between 352 nm and 354 nm which was comparable to measurement provided by UV spectrophotometer and also theoretical calculation by Beer-Lambert Absorption Law. The entire detection can be done in less than 10 minutes using a total volume of 3 μL only. This result promises good potential of this fiber optic microchannel sensor as a reliable, portable, and disposable sensor.

Small Surface Target Detection with EO/IR Sensors

NARCIS (Netherlands)

Jong, A.N. de; Kemp, R.A.W.

1998-01-01

The detection of small surface targets at sea is an increasing requirement for warships. The present sensors on board do not provide the required detection probabilities for these low observable targets like small rubber boats, floating mines, periscopes, people etc. The reason for the low

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.

Photonic sensor opportunities for distributed and wireless systems in security applications

Science.gov (United States)

Krohn, David

2006-10-01

There are broad ranges of homeland security sensing applications that can be facilitated by distributed fiber optic sensors and photonics integrated wireless systems. These applications include [1]: Pipeline, (Monitoring, Security); Smart structures (Bridges, Tunnels, Dams, Public spaces); Power lines (Monitoring, Security); Transportation security; Chemical/biological detection; Wide area surveillance - perimeter; and Port Security (Underwater surveillance, Cargo container). Many vital assets which cover wide areas, such as pipeline and borders, are under constant threat of being attacked or breached. There is a rapidly emerging need to be able to provide identification of intrusion threats to such vital assets. Similar problems exit for monitoring the basic infrastructure such as water supply, power utilities, communications systems as well as transportation. There is a need to develop a coordinated and integrated solution for the detection of threats. From a sensor standpoint, consideration must not be limited to detection, but how does detection lead to intervention and deterrence. Fiber optic sensor technology must be compatible with other surveillance technologies such as wireless mote technology to facilitate integration. In addition, the multi-functionality of fiber optic sensors must be expanded to include bio-chemical detection. There have been a number of barriers for the acceptance and broad use of smart fiber optic sensors. Compared to telecommunications, the volume is low. This fact coupled with proprietary and custom specifications has kept the price of fiber optic sensors high. There is a general lack of a manufacturing infrastructure and lack of standards for packaging and reliability. Also, there are several competing technologies; some photonic based and other approaches based on conventional non-photonic technologies.

PERFORMANCE COMPARISON OF INTRUSION DETECTION SYSTEM USING VARIOUS TECHNIQUES – A REVIEW

Directory of Open Access Journals (Sweden)

S. Devaraju

2013-09-01

Full Text Available Nowadays, the security has become a critical part of any organization or industry information systems. The Intrusion Detection System is an effective method to deal with the new kind of threats such as DoS, Porbe, R2L and U2R. In this paper, we analyze the various approaches such as Hidden Semi Markov Model, Conditional Random Fields and Layered Approach, Bayesian classification, Data Mining techniques, Clustering Algorithms such as K-Means and Fuzzy c-Means, Back Propagation Neural Network, SOM Neural Network, Rough Set Neural Network Algorithm, Genetic Algorithm, Pattern Matching, Principle Component Analysis, Linear Discriminant Analysis, Independent Component Analysis, Multivariate Statistical Analysis, SOM/PSO algorithm etc. The performance is measured for two different datasets using various approaches. The datasets are trained and tested for identifying the new attacks that will affect the hosts or networks. The well known KDD Cup 1999 or DARPA 1999 dataset has been used to improve the accuracy and performance. The four groups of attacks are identified as Probe, DoS, U2R and R2L. The dataset used for training set is 494,021 and testing set is 311,028. The aim is to improve the detection rate and performance of the proposed system.

Phage-based magnetoelastic sensor for the detection of Salmonella typhimurium

Science.gov (United States)

Lakshmanan, Ramji S.

In recent years, food-borne illness have garnered the attention of mainstream America with calls now coming from the media for more inspections to ensure the safety of our food supply. Food borne illness from the ingestion of S. typhimurium has been of great concern due to its common occurrence in food products of daily consumption. Annually approximately 80 million cases of food poisoning are reported in the United States alone. The ever growing need for rapid detection of pathogenic microorganisms present in food, environmental and clinical samples has invoked an increased interest in research efforts towards the development of novel diagnostic methodologies. Currently, the detection of bacteria in contaminated food relies on conventional microbiological methods that are time consuming and manpower intensive. This study presents the results of the characterization of a phage-based magnetoelastic biosensor for the detection of Salmonella typhimurium . This affinity-based biosensensor is comprised of a magnetoelastic material as the transducer and filamentous phage as the bio-recognition element. Magnetoelastic materials are ferromagnetic amorphous alloys that change dimensions in the presence of a magnetic field. This effect in combination with the reverse effect (inverse magnetostriction) is utilized in a typical sensor application. A time varying magnetic field causes these sensors to oscillate at a characteristic resonance frequency. The characteristic resonance frequency is dependent on the initial dimensions and physical properties of the material. These materials are of particular interest owing to their unique capability to perform remote (without direct wire contacts to the sensor) sensing, making in-vivo detection and detection in closed containers possible. The phage-immobilized magnetoelastic biosensor was characterized for specificity; dose response in water, spiked apple juice and in spiked milk; selectivity; and longevity. The sensor's sensitivity is

Effective Sensor Selection and Data Anomaly Detection for Condition Monitoring of Aircraft Engines

Directory of Open Access Journals (Sweden)

Liansheng Liu

2016-04-01

Full Text Available In a complex system, condition monitoring (CM can collect the system working status. The condition is mainly sensed by the pre-deployed sensors in/on the system. Most existing works study how to utilize the condition information to predict the upcoming anomalies, faults, or failures. There is also some research which focuses on the faults or anomalies of the sensing element (i.e., sensor to enhance the system reliability. However, existing approaches ignore the correlation between sensor selecting strategy and data anomaly detection, which can also improve the system reliability. To address this issue, we study a new scheme which includes sensor selection strategy and data anomaly detection by utilizing information theory and Gaussian Process Regression (GPR. The sensors that are more appropriate for the system CM are first selected. Then, mutual information is utilized to weight the correlation among different sensors. The anomaly detection is carried out by using the correlation of sensor data. The sensor data sets that are utilized to carry out the evaluation are provided by National Aeronautics and Space Administration (NASA Ames Research Center and have been used as Prognostics and Health Management (PHM challenge data in 2008. By comparing the two different sensor selection strategies, the effectiveness of selection method on data anomaly detection is proved.

Lightning Current Measurement with Fiber-Optic Sensor

Science.gov (United States)

Nguyen, Truong X.; Ely, Jay J.; Szatkowski, George N.; Mata, Carlos T.; Mata, Angel G.; Snyder, Gary P.

2014-01-01

A fiber-optic current sensor is successfully developed with many potential applications for electric current measurement. Originally developed for in-flight lightning measurement, the sensor utilizes Faraday Effect in an optical fiber. The Faraday Effect causes linear light polarization in a fiber to rotate when the fiber is exposed to a magnetic field. The polarization change is detected using a reflective polarimetric scheme. Forming fiber loops and applying Ampere's law, measuring the total light rotation results in the determination of the total current enclosed. The sensor is conformable to complex structure geometry. It is also non-conductive and immune to electromagnetic interference, saturation or hysteresis. Installation is non-intrusive, and the sensor can be safely routed through flammable areas. Two similar sensor systems are described in this paper. The first system operates at 1310nm laser wavelength and is capable of measuring approximately 300 A - 300 kA, a 60 dB range. Laboratory validation results of aircraft lighting direct and in-direct effect current amplitudes are reported for this sensor. The second system operates at 1550nm wavelength and can measure about 400 A - 400 kA. Triggered-lightning measurement data are presented for this system. Good results are achieved in all cases.

Tactile sensor of hardness recognition based on magnetic anomaly detection

Science.gov (United States)

Xue, Lingyun; Zhang, Dongfang; Chen, Qingguang; Rao, Huanle; Xu, Ping

2018-03-01

Hardness, as one kind of tactile sensing, plays an important role in the field of intelligent robot application such as gripping, agricultural harvesting, prosthetic hand and so on. Recently, with the rapid development of magnetic field sensing technology with high performance, a number of magnetic sensors have been developed for intelligent application. The tunnel Magnetoresistance(TMR) based on magnetoresistance principal works as the sensitive element to detect the magnetic field and it has proven its excellent ability of weak magnetic detection. In the paper, a new method based on magnetic anomaly detection was proposed to detect the hardness in the tactile way. The sensor is composed of elastic body, ferrous probe, TMR element, permanent magnet. When the elastic body embedded with ferrous probe touches the object under the certain size of force, deformation of elastic body will produce. Correspondingly, the ferrous probe will be forced to displace and the background magnetic field will be distorted. The distorted magnetic field was detected by TMR elements and the output signal at different time can be sampled. The slope of magnetic signal with the sampling time is different for object with different hardness. The result indicated that the magnetic anomaly sensor can recognize the hardness rapidly within 150ms after the tactile moment. The hardness sensor based on magnetic anomaly detection principal proposed in the paper has the advantages of simple structure, low cost, rapid response and it has shown great application potential in the field of intelligent robot.

Quartz Microbalance Sensor for the Detection of Acrylamide