WorldWideScience

Sample records for based intrusion detection

  1. Community-Based Intrusion Detection

    OpenAIRE

    Weigert, Stefan

    2017-01-01

    Today, virtually every company world-wide is connected to the Internet. This wide-spread connectivity has given rise to sophisticated, targeted, Internet-based attacks. For example, between 2012 and 2013 security researchers counted an average of about 74 targeted attacks per day. These attacks are motivated by economical, financial, or political interests and commonly referred to as “Advanced Persistent Threat (APT)” attacks. Unfortunately, many of these attacks are successful and the advers...

  2. 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)

  3. 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.

  4. 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...

  5. 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.

  6. 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.

  7. 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

  8. 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

  9. 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

  10. On Emulation-Based Network Intrusion Detection Systems

    NARCIS (Netherlands)

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

    2014-01-01

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

  11. On emulation-based network intrusion detection systems

    NARCIS (Netherlands)

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

    2014-01-01

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

  12. 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.

  13. 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

  14. Anomaly-based Network Intrusion Detection Methods

    Directory of Open Access Journals (Sweden)

    Pavel Nevlud

    2013-01-01

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

  15. Neural Network Based Intrusion Detection System for Critical Infrastructures

    Energy Technology Data Exchange (ETDEWEB)

    Todd Vollmer; Ondrej Linda; Milos Manic

    2009-07-01

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

  16. 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.

  17. 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.

  18. 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.

  19. 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)

  20. Interior intrusion detection systems

    Energy Technology Data Exchange (ETDEWEB)

    Rodriguez, J.R.; Matter, J.C. (Sandia National Labs., Albuquerque, NM (United States)); Dry, B. (BE, Inc., Barnwell, SC (United States))

    1991-10-01

    The purpose of this NUREG is to present technical information that should be useful to NRC licensees in designing interior intrusion detection systems. Interior intrusion sensors are discussed according to their primary application: boundary-penetration detection, volumetric detection, and point protection. Information necessary for implementation of an effective interior intrusion detection system is presented, including principles of operation, performance characteristics and guidelines for design, procurement, installation, testing, and maintenance. A glossary of sensor data terms is included. 36 figs., 6 tabs.

  1. Interior intrusion detection systems

    International Nuclear Information System (INIS)

    Rodriguez, J.R.; Matter, J.C.; Dry, B.

    1991-10-01

    The purpose of this NUREG is to present technical information that should be useful to NRC licensees in designing interior intrusion detection systems. Interior intrusion sensors are discussed according to their primary application: boundary-penetration detection, volumetric detection, and point protection. Information necessary for implementation of an effective interior intrusion detection system is presented, including principles of operation, performance characteristics and guidelines for design, procurement, installation, testing, and maintenance. A glossary of sensor data terms is included. 36 figs., 6 tabs

  2. 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

  3. Case-Based Multi-Sensor Intrusion Detection

    Science.gov (United States)

    Schwartz, Daniel G.; Long, Jidong

    2009-08-01

    Multi-sensor intrusion detection systems (IDSs) combine the alerts raised by individual IDSs and possibly other kinds of devices such as firewalls and antivirus software. A critical issue in building a multi-sensor IDS is alert-correlation, i.e., determining which alerts are caused by the same attack. This paper explores a novel approach to alert correlation using case-based reasoning (CBR). Each case in the CBR system's library contains a pattern of alerts raised by some known attack type, together with the identity of the attack. Then during run time, the alert streams gleaned from the sensors are compared with the patterns in the cases, and a match indicates that the attack described by that case has occurred. For this purpose the design of a fast and accurate matching algorithm is imperative. Two such algorithms were explored: (i) the well-known Hungarian algorithm, and (ii) an order-preserving matching of our own device. Tests were conducted using the DARPA Grand Challenge Problem attack simulator. These showed that the both matching algorithms are effective in detecting attacks; but the Hungarian algorithm is inefficient; whereas the order-preserving one is very efficient, in fact runs in linear time.

  4. 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.

  5. Intrusion detection sensors

    International Nuclear Information System (INIS)

    Williams, J.D.

    1978-07-01

    Intrusion detection sensors are an integral part of most physical security systems. Under the sponsorship of the U.S. Department of Energy, Office of Safeguards and Security, Sandia Laboratories has conducted a survey of available intrusion detection sensors and has tested a number of different sensors. An overview of these sensors is provided. This overview includes (1) the operating principles of each type of sensor, (2) unique sensor characteristics, (3) desired sensor improvements which must be considered in planning an intrusion detection system, and (4) the site characteristics which affect the performance of both exterior and interior sensors. Techniques which have been developed to evaluate various intrusion detection sensors are also discussed

  6. 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.

  7. 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.

  8. 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...

  9. An armored-cable-based fiber Bragg grating sensor array for perimeter fence intrusion detection

    Science.gov (United States)

    Hao, Jianzhong; Dong, Bo; Varghese, Paulose; Phua, Jiliang; Foo, Siang Fook

    2012-01-01

    In this paper, an armored-cable-based optical fiber Bragg grating (FBG) sensor array, for perimeter fence intrusion detection, is demonstrated and some of the field trial results are reported. The field trial was conducted at a critical local installation in Singapore in December 2010. The sensor array was put through a series of both simulated and live intrusion scenarios to test the stability and suitability of operation in the local environmental conditions and to determine its capabilities in detecting and reporting these intrusions accurately to the control station. Such a sensor array can provide perimeter intrusion detection with fine granularity and preset pin-pointing accuracy. The various types of intrusions included aided or unaided climbs, tampering and cutting of the fence, etc. The unique sensor packaging structure provides high sensitivity, crush resistance and protection against rodents. It is also capable of resolving nuisance events such as rain, birds sitting on the fence or seismic vibrations. These sensors are extremely sensitive with a response time of a few seconds. They can be customized for a desired spatial resolution and pre-determined sensitivity. Furthermore, it is easy to cascade a series of such sensors to monitor and detect intrusion events over a long stretch of fence line. Such sensors can be applied to real-time intrusion detection for perimeter security, pipeline security and communications link security.

  10. 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.

  11. 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...

  12. 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.

  13. RIDES: Robust Intrusion Detection System for IP-Based Ubiquitous Sensor Networks.

    Science.gov (United States)

    Amin, Syed Obaid; Siddiqui, Muhammad Shoaib; Hong, Choong Seon; Lee, Sungwon

    2009-01-01

    The IP-based Ubiquitous Sensor Network (IP-USN) is an effort to build the "Internet of things". By utilizing IP for low power networks, we can benefit from existing well established tools and technologies of IP networks. Along with many other unresolved issues, securing IP-USN is of great concern for researchers so that future market satisfaction and demands can be met. Without proper security measures, both reactive and proactive, it is hard to envisage an IP-USN realm. In this paper we present a design of an IDS (Intrusion Detection System) called RIDES (Robust Intrusion DEtection System) for IP-USN. RIDES is a hybrid intrusion detection system, which incorporates both Signature and Anomaly based intrusion detection components. For signature based intrusion detection this paper only discusses the implementation of distributed pattern matching algorithm with the help of signature-code, a dynamically created attack-signature identifier. Other aspects, such as creation of rules are not discussed. On the other hand, for anomaly based detection we propose a scoring classifier based on the SPC (Statistical Process Control) technique called CUSUM charts. We also investigate the settings and their effects on the performance of related parameters for both of the components.

  14. RIDES: Robust Intrusion Detection System for IP-Based Ubiquitous Sensor Networks

    Directory of Open Access Journals (Sweden)

    Sungwon Lee

    2009-05-01

    Full Text Available TheIP-based Ubiquitous Sensor Network (IP-USN is an effort to build the “Internet of things”. By utilizing IP for low power networks, we can benefit from existing well established tools and technologies of IP networks. Along with many other unresolved issues, securing IP-USN is of great concern for researchers so that future market satisfaction and demands can be met. Without proper security measures, both reactive and proactive, it is hard to envisage an IP-USN realm. In this paper we present a design of an IDS (Intrusion Detection System called RIDES (Robust Intrusion DEtection System for IP-USN. RIDES is a hybrid intrusion detection system, which incorporates both Signature and Anomaly based intrusion detection components. For signature based intrusion detection this paper only discusses the implementation of distributed pattern matching algorithm with the help of signature-code, a dynamically created attack-signature identifier. Other aspects, such as creation of rules are not discussed. On the other hand, for anomaly based detection we propose a scoring classifier based on the SPC (Statistical Process Control technique called CUSUM charts. We also investigate the settings and their effects on the performance of related parameters for both of the components.

  15. 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

  16. 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

  17. 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%.

  18. 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.

  19. 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.

  20. 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

  1. 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

  2. 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

  3. 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

  4. 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

  5. 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...

  6. 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

  7. An Integrated Intrusion Detection Model of Cluster-Based Wireless Sensor Network.

    Science.gov (United States)

    Sun, Xuemei; Yan, Bo; Zhang, Xinzhong; Rong, Chuitian

    2015-01-01

    Considering wireless sensor network characteristics, this paper combines anomaly and mis-use detection and proposes an integrated detection model of cluster-based wireless sensor network, aiming at enhancing detection rate and reducing false rate. Adaboost algorithm with hierarchical structures is used for anomaly detection of sensor nodes, cluster-head nodes and Sink nodes. Cultural-Algorithm and Artificial-Fish-Swarm-Algorithm optimized Back Propagation is applied to mis-use detection of Sink node. Plenty of simulation demonstrates that this integrated model has a strong performance of intrusion detection.

  8. Intrusion detection method based on nonlinear correlation measure

    NARCIS (Netherlands)

    Ambusaidi, Mohammed A.; Tan, Zhiyuan; He, Xiangjian; Nanda, Priyadarsi; Lu, Liang Fu; Jamdagni, Aruna

    2014-01-01

    Cyber crimes and malicious network activities have posed serious threats to the entire internet and its users. This issue is becoming more critical, as network-based services, are more widespread and closely related to our daily life. Thus, it has raised a serious concern in individual internet

  9. 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.

  10. Instant OSSEC host-based intrusion detection system

    CERN Document Server

    Lhotsky, Brad

    2013-01-01

    Filled with practical, step-by-step instructions and clear explanations for the most important and useful tasks. A fast-paced, practical guide to OSSEC-HIDS that will help you solve host-based security problems.This book is great for anyone concerned about the security of their servers-whether you are a system administrator, programmer, or security analyst, this book will provide you with tips to better utilize OSSEC-HIDS. Whether you're new to OSSEC-HIDS or a seasoned veteran, you'll find something in this book you can apply today!This book assumes some knowledge of basic security concepts an

  11. 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.

  12. 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.

  13. A Protocol Layer Trust-Based Intrusion Detection Scheme for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Jian Wang

    2017-05-01

    Full Text Available This article proposes a protocol layer trust-based intrusion detection scheme for wireless sensor networks. Unlike existing work, the trust value of a sensor node is evaluated according to the deviations of key parameters at each protocol layer considering the attacks initiated at different protocol layers will inevitably have impacts on the parameters of the corresponding protocol layers. For simplicity, the paper mainly considers three aspects of trustworthiness, namely physical layer trust, media access control layer trust and network layer trust. The per-layer trust metrics are then combined to determine the overall trust metric of a sensor node. The performance of the proposed intrusion detection mechanism is then analyzed using the t-distribution to derive analytical results of false positive and false negative probabilities. Numerical analytical results, validated by simulation results, are presented in different attack scenarios. It is shown that the proposed protocol layer trust-based intrusion detection scheme outperforms a state-of-the-art scheme in terms of detection probability and false probability, demonstrating its usefulness for detecting cross-layer attacks.

  14. 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.

  15. 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....

  16. 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

  17. 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...

  18. 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

  19. A New Intrusion Detection System Based on KNN Classification Algorithm in Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Wenchao Li

    2014-01-01

    abnormal nodes from normal nodes by observing their abnormal behaviors, and we analyse parameter selection and error rate of the intrusion detection system. The paper elaborates on the design and implementation of the detection system. This system has achieved efficient, rapid intrusion detection by improving the wireless ad hoc on-demand distance vector routing protocol (Ad hoc On-Demand Distance the Vector Routing, AODV. Finally, the test results show that: the system has high detection accuracy and speed, in accordance with the requirement of wireless sensor network intrusion detection.

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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

  6. 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.

  7. 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.

  8. 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...

  9. 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.

  10. Intrusion-Aware Alert Validation Algorithm for Cooperative Distributed Intrusion Detection Schemes of Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Young-Jae Song

    2009-07-01

    Full Text Available Existing anomaly and intrusion detection schemes of wireless sensor networks have mainly focused on the detection of intrusions. Once the intrusion is detected, an alerts or claims will be generated. However, any unidentified malicious nodes in the network could send faulty anomaly and intrusion claims about the legitimate nodes to the other nodes. Verifying the validity of such claims is a critical and challenging issue that is not considered in the existing cooperative-based distributed anomaly and intrusion detection schemes of wireless sensor networks. In this paper, we propose a validation algorithm that addresses this problem. This algorithm utilizes the concept of intrusion-aware reliability that helps to provide adequate reliability at a modest communication cost. In this paper, we also provide a security resiliency analysis of the proposed intrusion-aware alert validation algorithm.

  11. 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.

  12. 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.

  13. 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

  14. 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....

  15. 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.

  16. 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...

  17. Intrusion Detection System Based on Decision Tree over Big Data in Fog Environment

    Directory of Open Access Journals (Sweden)

    Kai Peng

    2018-01-01

    Full Text Available Fog computing, as the supplement of cloud computing, can provide low-latency services between mobile users and the cloud. However, fog devices may encounter security challenges as a result of the fog nodes being close to the end users and having limited computing ability. Traditional network attacks may destroy the system of fog nodes. Intrusion detection system (IDS is a proactive security protection technology and can be used in the fog environment. Although IDS in tradition network has been well investigated, unfortunately directly using them in the fog environment may be inappropriate. Fog nodes produce massive amounts of data at all times, and, thus, enabling an IDS system over big data in the fog environment is of paramount importance. In this study, we propose an IDS system based on decision tree. Firstly, we propose a preprocessing algorithm to digitize the strings in the given dataset and then normalize the whole data, to ensure the quality of the input data so as to improve the efficiency of detection. Secondly, we use decision tree method for our IDS system, and then we compare this method with Naïve Bayesian method as well as KNN method. Both the 10% dataset and the full dataset are tested. Our proposed method not only completely detects four kinds of attacks but also enables the detection of twenty-two kinds of attacks. The experimental results show that our IDS system is effective and precise. Above all, our IDS system can be used in fog computing environment over big data.

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

    DEFF Research Database (Denmark)

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

    2017-01-01

    Network intrusion detection systems (NIDSs) which aim to identify various attacks, have become an essential part of current security infrastructure. In particular, signature-based NIDSs are being widely implemented in industry due to their low rate of false alarms. However, the signature matching...... this problem, packet filtration is a promising solution to reduce unwanted traffic. Motivated by this, in this work, a list-based packet filter was designed and an engineering method of combining both blacklist and whitelist techniques was introduced. To further secure such filters against IP spoofing attacks...... in traffic filtration as well as workload reduction, and is robust against IP spoofing attacks....

  19. Proposed Network Intrusion Detection System ‎In Cloud Environment Based on Back ‎Propagation Neural Network

    Directory of Open Access Journals (Sweden)

    Shawq Malik Mehibs

    2017-12-01

    Full Text Available Cloud computing is distributed architecture, providing computing facilities and storage resource as a service over the internet. This low-cost service fulfills the basic requirements of users. Because of the open nature and services introduced by cloud computing intruders impersonate legitimate users and misuse cloud resource and services. To detect intruders and suspicious activities in and around the cloud computing environment, intrusion detection system used to discover the illegitimate users and suspicious action by monitors different user activities on the network .this work proposed based back propagation artificial neural network to construct t network intrusion detection in the cloud environment. The proposed module evaluated with kdd99 dataset the experimental results shows promising approach to detect attack with high detection rate and low false alarm rate

  20. 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.

  1. 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.

  2. 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

  3. 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...

  4. 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.

  5. 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

  6. 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.

  7. 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

  8. 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.

  9. 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...

  10. 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.

  11. 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.

  12. 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

  13. 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.

  14. 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

  15. 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.

  16. 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...

  17. 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

  18. 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

  19. 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…

  20. Unconventional applications of conventional intrusion detection sensors

    International Nuclear Information System (INIS)

    Williams, J.D.; Matter, J.C.

    1983-01-01

    A number of conventional intrusion detection sensors exists for the detection of persons entering buildings, moving within a given volume, and crossing a perimeter isolation zone. Unconventional applications of some of these sensors have recently been investigated. Some of the applications which are discussed include detection on the edges and tops of buildings, detection in storm sewers, detection on steam and other types of large pipes, and detection of unauthorized movement within secure enclosures. The enclosures can be used around complicated control valves, electrical control panels, emergency generators, etc

  1. Intrusion detection for IP-based multimedia communications over wireless networks

    CERN Document Server

    Tang, Jin

    2013-01-01

    IP-based multimedia communications have become increasingly popular in recent years. With the increasing coverage of the IEEE 802:11™ based wireless networks, IP-based multimedia communications over wireless networks are also drawing extensive attention in both academia and industry. Due to the openness and distributed nature of the protocols involved, such as the session initiation protocol (SIP) and the IEEE 802:11™ standard, it becomes easy for malicious users in the network to achieve their own gain or disrupt the service by deviating from the normal protocol behaviors. This SpringerBrief

  2. An Evolutionary, Agent-Based Model to Aid in Computer Intrusion Detection and Prevention

    National Research Council Canada - National Science Library

    Shargel, Ben; Bonabeau, Eric; Budynek, Julien; Gaudiano, Paolo

    2005-01-01

    We have developed a realistic agent-based simulation model of hacker behavior. In the model, hacker scripts are generated using a simple but powerful hacker grammar that has the potential to cover all possible hacker scripts...

  3. Smart sensor systems for outdoor intrusion detection

    International Nuclear Information System (INIS)

    Lynn, J.K.

    1988-01-01

    A major improvement in outdoor perimeter security system probability of detection (PD) and reduction in false alarm rate (FAR) and nuisance alarm rate (NAR) may be obtained by analyzing the indications immediately preceding an event which might be interpreted as an intrusion. Existing systems go into alarm after crossing a threshold. Very slow changes, which accumulate until the threshold is reached, may be assessed falsely as an intrusion. A hierarchial program has begun at Stellar to develop a modular, expandable Smart Sensor system which may be interfaced to most types of sensor and alarm reporting systems. A major upgrade to the SSI Test Site is in progress so that intrusions may be simulated in a controlled and repeatable manner. A test platform is being constructed which will operate in conduction with a mobile instrumentation center with CCTVB, lighting control, weather and data monitoring and remote control of the test platform and intrusion simulators. Additional testing was contracted with an independent test facility to assess the effects of severe winter weather conditions

  4. 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.

  5. 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.

  6. 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

  7. 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...

  8. 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.

  9. 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.

  10. 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.

  11. Windows Based Data Sets for Evaluation of Robustness of Host Based Intrusion Detection Systems (IDS to Zero-Day and Stealth Attacks

    Directory of Open Access Journals (Sweden)

    Waqas Haider

    2016-07-01

    Full Text Available The Windows Operating System (OS is the most popular desktop OS in the world, as it has the majority market share of both servers and personal computing necessities. However, as its default signature-based security measures are ineffectual for detecting zero-day and stealth attacks, it needs an intelligent Host-based Intrusion Detection System (HIDS. Unfortunately, a comprehensive data set that reflects the modern Windows OS’s normal and attack surfaces is not publicly available. To fill this gap, in this paper two open data sets generated by the cyber security department of the Australian Defence Force Academy (ADFA are introduced, namely: Australian Defence Force Academy Windows Data Set (ADFA-WD; and Australian Defence Force Academy Windows Data Set with a Stealth Attacks Addendum (ADFA-WD: SAA. Statistical analysis results based on these data sets show that, due to the low foot prints of modern attacks and high similarity of normal and attacked data, both these data sets are complex, and highly intelligent Host based Anomaly Detection Systems (HADS design will be required.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. 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

  18. 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.

  19. 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.

  20. 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.

  1. 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 ...

  2. 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 ...

  3. 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...

  4. 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.

  5. 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.

  6. 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...

  7. 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...

  8. 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...

  9. 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

  10. 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...

  11. 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.

  12. 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. ...

  13. 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...

  14. 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....

  15. 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.

  16. 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.

  17. 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

  18. 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.

  19. 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

  20. Full distributed fiber optical sensor for intrusion detection in application to buried pipelines

    Science.gov (United States)

    Gao, Jianzhong; Jiang, Zhuangde; Zhao, Yulong; Zhu, Li; Zhao, Guoxian

    2005-11-01

    Based on the microbend effect of optical fiber, a distributed sensor for real-time continuous monitoring of intrusion in application to buried pipelines is proposed. The sensing element is a long cable with a special structure made up of an elastic polymer wire, an optical fiber, and a metal wire. The damage point is located with an embedded optical time domain reflectometry (OTDR) instrument. The intrusion types can be indicated by the amplitude of output voltage. Experimental results show that the detection system can alarm adequately under abnormal load and can locate the intrusion point within 22.4 m for distance of 3.023 km.

  1. Abstracting audit data for lightweight intrusion detection

    KAUST Repository

    Wang, Wei; Zhang, Xiangliang; Pitsilis, Georgios

    2010-01-01

    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

  2. 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

  3. 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

  4. Investigating the influence of special on-off attacks on challenge-based collaborative intrusion detection networks

    DEFF Research Database (Denmark)

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

    2018-01-01

    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...

  5. 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 ...

  6. 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

  7. 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.

  8. Flow-based intrusion detection

    NARCIS (Netherlands)

    Sperotto, Anna

    2010-01-01

    The spread of 1-10Gbps technology has in recent years paved the way to a flourishing landscape of new, high-bandwidth Internet services. As users, we depend on the Internet in our daily life for simple tasks such as checking e-mails, but also for managing private and financial information. However,

  9. Flow-Based Intrusion Detection

    NARCIS (Netherlands)

    Sperotto, Anna; Pras, Aiko

    The spread of 1-10 Gbps technology has in recent years paved the way to a flourishing landscape of new, high-bandwidth Internet services.At the same time, we have also observed increasingly frequent and widely diversified attacks. To this threat, the research community has answered with a growing

  10. Intrusion Detection Systems with Live Knowledge System

    Science.gov (United States)

    2016-05-31

    people try to reveal sensitive information of Internet users, also called as phishing. Phishing detection has received great attention but there has...node. Figure 3 describes the result of modified nodes from the original RDR rule tree. Red- coloured ‘X’ sign represents the stopping rule, and the...green- coloured boxes describe the refined rule. However, when human knowledge is applied to those incorrectly classified data, not all of the

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

    Directory of Open Access Journals (Sweden)

    Eung Jun Cho

    2013-11-01

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

  12. 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.

  13. 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

  14. 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.

  15. LIDeA: A Distributed Lightweight Intrusion Detection Architecture for Sensor Networks

    DEFF Research Database (Denmark)

    Giannetsos, Athanasios; Krontiris, Ioannis; Dimitriou, Tassos

    2008-01-01

    to achieve a more autonomic and complete defense mechanism, even against attacks that have not been anticipated in advance. In this paper, we present a lightweight intrusion detection system, called LIDeA, designed for wireless sensor networks. LIDeA is based on a distributed architecture, in which nodes......Wireless sensor networks are vulnerable to adversaries as they are frequently deployed in open and unattended environments. Preventive mechanisms can be applied to protect them from an assortment of attacks. However, more sophisticated methods, like intrusion detection systems, are needed...

  16. 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)

  17. 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.

  18. Coplanar capacitance sensors for detecting water intrusion in composite structures

    International Nuclear Information System (INIS)

    Nassr, Amr A; El-Dakhakhni, Wael W; Ahmed, Wael H

    2008-01-01

    Composite materials are becoming more affordable and widely used for retrofitting, rehabilitating and repairing reinforced concrete structures designed and constructed under older specifications. However, the mechanical properties and long-term durability of composite materials may degrade severely in the presence of water intrusion. This study presents a new non-destructive evaluation (NDE) technique for detecting the water intrusion in composite structures by evaluating the dielectric properties of different composite system constituent materials. The variation in the dielectric signatures was employed to design a coplanar capacitance sensor with high sensitivity to detect such defects. An analytical model was used to study the effect of the sensor geometry on the output signal and to optimize sensor design. A finite element model was developed to validate analytical results and to evaluate other sensor design-related parameters. Experimental testing of a concrete specimen wrapped with composite laminate and containing a series of pre-induced water intrusion defects was conducted in order to validate the concept of the new technique. Experimental data showed excellent agreement with the finite element model predictions and confirmed sensor performance

  19. 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...

  20. 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.

  1. 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

  2. A Machine Learning Based Intrusion Impact Analysis Scheme for Clouds

    Directory of Open Access Journals (Sweden)

    Junaid Arshad

    2012-01-01

    Full Text Available Clouds represent a major paradigm shift, inspiring the contemporary approach to computing. They present fascinating opportunities to address dynamic user requirements with the provision of on demand expandable computing infrastructures. However, Clouds introduce novel security challenges which need to be addressed to facilitate widespread adoption. This paper is focused on one such challenge - intrusion impact analysis. In particular, we highlight the significance of intrusion impact analysis for the overall security of Clouds. Additionally, we present a machine learning based scheme to address this challenge in accordance with the specific requirements of Clouds for intrusion impact analysis. We also present rigorous evaluation performed to assess the effectiveness and feasibility of the proposed method to address this challenge for Clouds. The evaluation results demonstrate high degree of effectiveness to correctly determine the impact of an intrusion along with significant reduction with respect to the intrusion response time.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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...

  10. 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

  11. 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.

  12. 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...

  13. 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.

  14. 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.

  15. 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.

  16. 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)

  17. 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– ...

  18. 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...

  19. 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...

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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

  9. 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

  10. 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)

  11. 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

  12. 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.

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

    Directory of Open Access Journals (Sweden)

    Min-Joo Kang

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

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

    Science.gov (United States)

    Kang, Min-Joo; Kang, Je-Won

    2016-01-01

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

  15. 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.

  16. 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

  17. 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

  18. 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.

  19. 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.

  20. 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...

  1. 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

  2. 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.

  3. 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.

  4. 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

  5. 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.

  6. 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.

  7. 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)

  8. A Neuro-genetic Based Short-term Forecasting Framework for Network Intrusion Prediction System

    Institute of Scientific and Technical Information of China (English)

    Siva S. Sivatha Sindhu; S. Geetha; M. Marikannan; A. Kannan

    2009-01-01

    Information systems are one of the most rapidly changing and vulnerable systems, where security is a major issue. The number of security-breaking attempts originating inside organizations is increasing steadily. Attacks made in this way, usually done by "authorized" users of the system, cannot be immediately traced. Because the idea of filtering the traffic at the entrance door, by using firewalls and the like, is not completely successful, the use of intrusion detection systems should be considered to increase the defense capacity of an information system. An intrusion detection system (IDS) is usually working in a dynamically changing environment, which forces continuous tuning of the intrusion detection model, in order to maintain sufficient performance. The manual tuning process required by current IDS depends on the system operators in working out the tuning solution and in integrating it into the detection model. Furthermore, an extensive effort is required to tackle the newly evolving attacks and a deep study is necessary to categorize it into the respective classes. To reduce this dependence, an automatically evolving anomaly IDS using neuro-genetic algorithm is presented. The proposed system automatically tunes the detection model on the fly according to the feedback provided by the system operator when false predictions are encountered. The system has been evaluated using the Knowledge Discovery in Databases Conference (KDD 2009) intrusion detection dataset. Genetic paradigm is employed to choose the predominant features, which reveal the occurrence of intrusions. The neuro-genetic IDS (NGIDS) involves calculation of weightage value for each of the categorical attributes so that data of uniform representation can be processed by the neuro-genetic algorithm. In this system unauthorized invasion of a user are identified and newer types of attacks are sensed and classified respectively by the neuro-genetic algorithm. The experimental results obtained in this

  9. Abstracting massive data for lightweight intrusion detection in computer networks

    KAUST Repository

    Wang, Wei; Liu, Jiqiang; Pitsilis, Georgios; Zhang, Xiangliang

    2016-01-01

    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

  10. Dynamic Modeling of Internet Traffic for Intrusion Detection

    Directory of Open Access Journals (Sweden)

    Stephan Bohacek

    2007-01-01

    Full Text Available Computer network traffic is analyzed via mutual information techniques, implemented using linear and nonlinear canonical correlation analyses, with the specific objective of detecting UDP flooding attacks. NS simulation of HTTP, FTP, and CBR traffic shows that flooding attacks are accompanied by a change of mutual information, either at the link being flooded or at another upstream or downstream link. This observation appears to be topology independent, as the technique is demonstrated on the so-called parking-lot topology, random 50-node topology, and 100-node transit-stub topology. This technique is also employed to detect UDP flooding with low false alarm rate on a backbone link. These results indicate that a change in mutual information provides a useful detection criterion when no other signature of the attack is available.

  11. 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.

  12. Designing and Implementing a Family of Intrusion Detection Systems

    Science.gov (United States)

    2004-11-01

    configure (train), generates many false alarms – Misuse detection (signature analysis) (NFR, Emerald , Snort, STAT) • Generates few false alarms • Detects...to create .rhosts file in world-writable ftp home directory – rlogin using bogus .rhosts file S0 create_file read_rhosts S3S2 login S1 STAT KN-14...world-writable ftp home directory – rlogin using bogus .rhosts file S0 create_file read_rhosts S3S2 login S1 STAT KN-17 ftp-write in STATL use ustat

  13. An ontology-based intrusion patterns classification system | Shonubi ...

    African Journals Online (AJOL)

    Studies have shown that computer intrusions have been on the increase in recent times. Many techniques and patterns are being used by intruders to gain access to data on host computer networks. In this work, intrusion patterns were identified and classified and inherent knowledge were represented using an ontology of ...

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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)

  19. 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.

  20. Sensitive Data Protection Based on Intrusion Tolerance in Cloud Computing

    OpenAIRE

    Jingyu Wang; xuefeng Zheng; Dengliang Luo

    2011-01-01

    Service integration and supply on-demand coming from cloud computing can significantly improve the utilization of computing resources and reduce power consumption of per service, and effectively avoid the error of computing resources. However, cloud computing is still facing the problem of intrusion tolerance of the cloud computing platform and sensitive data of new enterprise data center. In order to address the problem of intrusion tolerance of cloud computing platform and sensitive data in...

  1. 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...

  2. 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...

  3. 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...

  4. 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). ...

  5. 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

  6. 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

  7. 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.

  8. 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.

  9. Attacks and intrusion detection in wireless sensor networks of industrial SCADA systems

    Science.gov (United States)

    Kamaev, V. A.; Finogeev, A. G.; Finogeev, A. A.; Parygin, D. S.

    2017-01-01

    The effectiveness of automated process control systems (APCS) and supervisory control and data acquisition systems (SCADA) information security depends on the applied protection technologies of transport environment data transmission components. This article investigates the problems of detecting attacks in wireless sensor networks (WSN) of SCADA systems. As a result of analytical studies, the authors developed the detailed classification of external attacks and intrusion detection in sensor networks and brought a detailed description of attacking impacts on components of SCADA systems in accordance with the selected directions of attacks.

  10. 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.

  11. Applying long short-term memory recurrent neural networks to intrusion detection

    Directory of Open Access Journals (Sweden)

    Ralf C. Staudemeyer

    2015-07-01

    Full Text Available We claim that modelling network traffic as a time series with a supervised learning approach, using known genuine and malicious behaviour, improves intrusion detection. To substantiate this, we trained long short-term memory (LSTM recurrent neural networks with the training data provided by the DARPA / KDD Cup ’99 challenge. To identify suitable LSTM-RNN network parameters and structure we experimented with various network topologies. We found networks with four memory blocks containing two cells each offer a good compromise between computational cost and detection performance. We applied forget gates and shortcut connections respectively. A learning rate of 0.1 and up to 1,000 epochs showed good results. We tested the performance on all features and on extracted minimal feature sets respectively. We evaluated different feature sets for the detection of all attacks within one network and also to train networks specialised on individual attack classes. Our results show that the LSTM classifier provides superior performance in comparison to results previously published results of strong static classifiers. With 93.82% accuracy and 22.13 cost, LSTM outperforms the winning entries of the KDD Cup ’99 challenge by far. This is due to the fact that LSTM learns to look back in time and correlate consecutive connection records. For the first time ever, we have demonstrated the usefulness of LSTM networks to intrusion detection.

  12. 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.

  13. 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%.

  14. Intrusion Detection Algorithm for Mitigating Sinkhole Attack on LEACH Protocol in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Ranjeeth Kumar Sundararajan

    2015-01-01

    Full Text Available In wireless sensor network (WSN, the sensors are deployed and placed uniformly to transmit the sensed data to a centralized station periodically. So, the major threat of the WSN network layer is sinkhole attack and it is still being a challenging issue on the sensor networks, where the malicious node attracts the packets from the other normal sensor nodes and drops the packets. Thus, this paper proposes an Intrusion Detection System (IDS mechanism to detect the intruder in the network which uses Low Energy Adaptive Clustering Hierarchy (LEACH protocol for its routing operation. In the proposed algorithm, the detection metrics, such as number of packets transmitted and received, are used to compute the intrusion ratio (IR by the IDS agent. The computed numeric or nonnumeric value represents the normal or malicious activity. As and when the sinkhole attack is captured, the IDS agent alerts the network to stop the data transmission. Thus, it can be a resilient to the vulnerable attack of sinkhole. Above all, the simulation result is shown for the proposed algorithm which is proven to be efficient compared with the existing work, namely, MS-LEACH, in terms of minimum computational complexity and low energy consumption. Moreover, the algorithm was numerically analyzed using TETCOS NETSIM.

  15. 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.

  16. 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)

  17. 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

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. 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 ...

  5. Constructing APT Attack Scenarios Based on Intrusion Kill Chain and Fuzzy Clustering

    Directory of Open Access Journals (Sweden)

    Ru Zhang

    2017-01-01

    Full Text Available The APT attack on the Internet is becoming more serious, and most of intrusion detection systems can only generate alarms to some steps of APT attack and cannot identify the pattern of the APT attack. To detect APT attack, many researchers established attack models and then correlated IDS logs with the attack models. However, the accuracy of detection deeply relied on the integrity of models. In this paper, we propose a new method to construct APT attack scenarios by mining IDS security logs. These APT attack scenarios can be further used for the APT detection. First, we classify all the attack events by purpose of phase of the intrusion kill chain. Then we add the attack event dimension to fuzzy clustering, correlate IDS alarm logs with fuzzy clustering, and generate the attack sequence set. Next, we delete the bug attack sequences to clean the set. Finally, we use the nonaftereffect property of probability transfer matrix to construct attack scenarios by mining the attack sequence set. Experiments show that the proposed method can construct the APT attack scenarios by mining IDS alarm logs, and the constructed scenarios match the actual situation so that they can be used for APT attack detection.

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

    National Research Council Canada - National Science Library

    Dunklee, David R

    2007-01-01

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

  7. 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.

  8. 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.

  9. Acceptance- and imagery-based strategies can reduce chocolate cravings: A test of the elaborated-intrusion theory of desire.

    Science.gov (United States)

    Schumacher, Sophie; Kemps, Eva; Tiggemann, Marika

    2017-06-01

    The elaborated-intrusion theory of desire proposes that craving is a two-stage process whereby initial intrusions about a desired target are subsequently elaborated with mental imagery. The present study tested whether the craving reduction strategies of cognitive defusion and guided imagery could differentially target the intrusion and elaboration stages, respectively, and thus differentially impact the craving process. Participants were randomly assigned to a cognitive defusion, a guided imagery or a mind-wandering control condition. Pre- and post-intervention chocolate-related thoughts, intrusiveness of thoughts, vividness of imagery, craving intensity, and chocolate consumption were compared. Experiment 1 recruited a general sample of young women (n = 94), whereas Experiment 2 recruited a sample of chocolate cravers who wanted to reduce their chocolate consumption (n = 97). Across both experiments, cognitive defusion lowered intrusiveness of thoughts, vividness of imagery and craving intensity. Guided imagery reduced chocolate-related thoughts, intrusiveness, vividness and craving intensity for chocolate cravers (Experiment 2), but not for the general sample (Experiment 1). There were no group differences in chocolate consumption in either experiment. Results add to existing evidence supporting the elaborated-intrusion theory of desire in the food domain, and suggest that acceptance- and imagery-based techniques have potential for use in combatting problematic cravings. Copyright © 2017 Elsevier Ltd. All rights reserved.

  10. 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

  11. Research on the technology of detecting the SQL injection attack and non-intrusive prevention in WEB system

    Science.gov (United States)

    Hu, Haibin

    2017-05-01

    Among numerous WEB security issues, SQL injection is the most notable and dangerous. In this study, characteristics and procedures of SQL injection are analyzed, and the method for detecting the SQL injection attack is illustrated. The defense resistance and remedy model of SQL injection attack is established from the perspective of non-intrusive SQL injection attack and defense. Moreover, the ability of resisting the SQL injection attack of the server has been comprehensively improved through the security strategies on operation system, IIS and database, etc.. Corresponding codes are realized. The method is well applied in the actual projects.

  12. A Multi Agent System for Flow-Based Intrusion Detection

    Science.gov (United States)

    2013-03-01

    Mechanics and its Applications, 340(1-3):388 – 394, 2004. ISSN 0378-4371. [46] Dawkins , Richard. The Selfish Gene . Oxford University Press, Oxford New...were considered but not chosen for this research assignment are: 31 • Gene Expression Programming [138] • Maximum Entropy Classifier Logistic...regression [70] • Naive Bayes Classifier [148] • Neural Networks [79] • Quadratic Discriminant Analysis • Binary Classifier Tree [100] Gene expression

  13. Thutmose - Investigation of Machine Learning-Based Intrusion Detection Systems

    Science.gov (United States)

    2016-06-01

    monitoring. This analyzed payload is within the application layer of the OSI model . The analysis tries to establish whether or not the payload is...24 3.2.5 Model Drift Experiments...ADVERSARIAL ENVIRONMENTS (SPIE DSS 2014) .................................................. 58 APPENDIX C - EVALUATING MODEL DRIFT IN MACHINE LEARNING

  14. Deception Based Intrusion Detection & Prevention for SCADA Environments -

    Science.gov (United States)

    types of cyber security, cutting-edge cyber security technologies that are being brought into the energy of a state sponsorship - for states, it's very easy just to open up a shell company and buy whatever security controls, things like FireEye or things like your various types of firewalls that are out there

  15. 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.

  16. Non-intrusive appliance load monitoring system based on a modern kWh-meter

    Energy Technology Data Exchange (ETDEWEB)

    Pihala, H. [VTT Energy, Espoo (Finland). Energy Systems

    1998-12-01

    Non-intrusive appliance load monitoring (NIALM) is a fairly new method to estimate load profiles of individual electric appliances in a small building, like a household, by monitoring the whole load at a single point with one recording device without sub-meters. Appliances have special electrical characteristics, the positive and negative active and reactive power changes during the time they are switched on or off. These changes are called events and are detected with a monitoring device called an event recorder. Different NIALM-concepts developed in Europe and in the United States are generally discussed. The NIALM-concept developed in this study is based on a 3-phase, power quality monitoring kWh-meter and unique load identification algorithms. This modern kWh-meter with a serial data bus to a laptop personal computer is used as die event recorder. The NIALM-concept of this presentation shows for the first time how a kWh-meter can be used at the same time for billing, power quality and appliance end-use monitoring. An essential part of the developed NIALM-system prototype is the software of load identification algorithms which runs in an off-line personal computer. These algorithms are able to identify, with a certain accuracy, both two-state and multi-state appliances. This prototype requires manual-setup in which the naming of appliances is performed. The results of the prototype NIALMS were verified in a large, single family detached house and they were compared to the results of other prototypes in France and the United States, although this comparison is difficult because of different supply systems, appliance stock and number of tested sites. Different applications of NIALM are discussed. Gathering of load research data, verification of DSM-programs, home automation, failure analysis of appliances and security surveillance of buildings are interesting areas of NIALM. Both utilities and customers can benefit from these applications. It is possible to

  17. Is mindfulness-based therapy an effective intervention for obsessive-intrusive thoughts: a case series.

    Science.gov (United States)

    Wilkinson-Tough, Megan; Bocci, Laura; Thorne, Kirsty; Herlihy, Jane

    2010-01-01

    Despite the efficacy of cognitive-behavioural interventions in improving the experience of obsessions and compulsions, some people do not benefit from this approach. The present research uses a case series design to establish whether mindfulness-based therapy could benefit those experiencing obsessive-intrusive thoughts by targeting thought-action fusion and thought suppression. Three participants received a relaxation control intervention followed by a six-session mindfulness-based intervention which emphasized daily practice. Following therapy all participants demonstrated reductions in Yale-Brown Obsessive-Compulsive Scale scores to below clinical levels, with two participants maintaining this at follow-up. Qualitative analysis of post-therapy feedback suggested that mindfulness skills such as observation, awareness and acceptance were seen as helpful in managing thought-action fusion and suppression. Despite being limited by small participant numbers, these results suggest that mindfulness may be beneficial to some people experiencing intrusive unwanted thoughts and that further research could establish the possible efficacy of this approach in larger samples. Copyright (c) 2009 John Wiley & Sons, Ltd.

  18. [Simultaneous intrusion and retraction of the anterior teeth using a three-piece base arch].

    Science.gov (United States)

    Liu, D; Bai, D; Wang, C; Sun, W; Guo, J; Xi, R

    2000-06-01

    To evaluate the effects of the three-piece base arch on overbite correction of Class II malocclusion. 20 patients with high angle, flared incisors were treated using a three-piece base arch appliance. The intrusion force of four upper incisors was adjusted to approximately 50 g. The line of force action was 2 mm distally to the resistant center(RC) and the retraction force was 20 g, the right and left posterior segments were joined by a palatal bar. Cephalograms were taken before treatment (T1) and six months after treatment (T2). The upper molars moved mesially 0.60 +/- 0.35 mm and the distance of the vertical extrusion was 0.80 +/- 0.52 mm. The distances of the upper central incisor retraction and intrusion were -4.20 +/- 2.12 mm and 3.10 +/- 0.54 mm respectively. The RC of the central incisor retracted -4.12 +/- 1.96 mm and intruded 3.20 +/- 0.66 mm. The axial inclination of the upper incisor-palatal plane changed from 123.21 degrees +/- 4.26 degrees to 116.00 degrees +/- 3.96 degrees. The three-piece segmented approach can effectively intrude and retract the upper anterior teeth for flared incisors and deep overbite.

  19. Trojan detection model based on network behavior analysis

    International Nuclear Information System (INIS)

    Liu Junrong; Liu Baoxu; Wang Wenjin

    2012-01-01

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

  20. Non-Intrusive Intelligibility Prediction Using a Codebook-Based Approach

    DEFF Research Database (Denmark)

    Sørensen, Charlotte; Kavalekalam, Mathew Shaji; Xenaki, Angeliki

    2017-01-01

    It could be beneficial for users of hearing aids if these were able to automatically adjust the processing according to the speech intelligibility in the specific acoustic environment. Most speech intelligibility metrics are intrusive, i.e., they require a clean reference signal, which is rarely...... a high correlation between the proposed non-intrusive codebookbased STOI (NIC-STOI) and the intrusive STOI indicating that NIC-STOI is a suitable metric for automatic classification of speech signals...

  1. 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.

  2. Deconstructing the Assessment of Anomaly-based Intrusion Detectors for Critical Applications

    Energy Technology Data Exchange (ETDEWEB)

    Viswanathan, Arun; Tan, Kymie; Neuman, Clifford

    2013-10-01

    Anomaly detection is a key strategy for cyber intrusion detection because it is conceptually capable of detecting novel attacks. This makes it an appealing defensive technique for environments such as the nation's critical infrastructures that is currently facing increased cyber adversarial activity. When considering deployment within the purview of such critical infrastructures it is imperative that the technology is well understood and reliable, where its performance is benchmarked on the results of principled assessments. This paper works towards such an imperative by analyzing the current state of anomaly detector assessments with a view toward mission critical deployments. We compile a framework of key evaluation constructs that identify how and where current assessment methods may fall short in providing sufficient insight into detector performance characteristics. Within the context of three case studies from literature, we show how error factors that influence the performance of detectors interact with different phases of a canonical evaluation strategy to compromise the integrity of the final results.

  3. Catalog of physical protection equipment. Book 1: Volume II. Intrusion detection components

    International Nuclear Information System (INIS)

    Haberman, W.

    1977-06-01

    This volume covers acoustic components, microwave/radar components, electro-optic barriers, electric field components, orientation components, ferrous metal detection components, proximity detection components, vibration detection components, seismic components, pressure-sensitive components, pressure mats, continuity components, electrical/magnetic switches, fire detection components, and mechanical contact switches

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

    Science.gov (United States)

    Ray, Loye Lynn

    2014-01-01

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

  5. 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

  6. 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...

  7. Non-invasive and non-intrusive gas flow measurement based on the dynamic thermal characteristics of a pipeline

    Science.gov (United States)

    Fan, Zichuan; Cai, Maolin; Xu, Weiqing

    2012-10-01

    This paper proposes a non-intrusive and non-invasive method for measuring the gas flow rate in pneumatic industry. A heater unit is fixed on the partial circumference of the external wall of a pipeline and emits specific thermal pulses in a predetermined mode. Two sensors attached to the external wall detect the upstream temperature, and the gas flow can be measured according to the relationship between the flow rate and the dynamic thermal characteristics of the pipeline. To determine the preferable relationship, the temperature field model of the measurement system is built. Then, based on the measurement modes and the corresponding simulations, the objective functions for the gas flow specified on different dynamic thermal characteristics are established. Additionally, the minimum measurement time of the method, named reference time scale, is proposed. Further, robustness tests of the measurement method are derived by considering the influences of multiple factors on the objective functions. The experiments confirm that this method does not need to open the pipeline and disturb the flow regime in order to obtain the data; this method also avoids the typical time-consuming and complex operations, resists ambient temperature disturbance and achieves approximately acceptable results.

  8. Non-invasive and non-intrusive gas flow measurement based on the dynamic thermal characteristics of a pipeline

    International Nuclear Information System (INIS)

    Fan, Zichuan; Cai, Maolin; Xu, Weiqing

    2012-01-01

    This paper proposes a non-intrusive and non-invasive method for measuring the gas flow rate in pneumatic industry. A heater unit is fixed on the partial circumference of the external wall of a pipeline and emits specific thermal pulses in a predetermined mode. Two sensors attached to the external wall detect the upstream temperature, and the gas flow can be measured according to the relationship between the flow rate and the dynamic thermal characteristics of the pipeline. To determine the preferable relationship, the temperature field model of the measurement system is built. Then, based on the measurement modes and the corresponding simulations, the objective functions for the gas flow specified on different dynamic thermal characteristics are established. Additionally, the minimum measurement time of the method, named reference time scale, is proposed. Further, robustness tests of the measurement method are derived by considering the influences of multiple factors on the objective functions. The experiments confirm that this method does not need to open the pipeline and disturb the flow regime in order to obtain the data; this method also avoids the typical time-consuming and complex operations, resists ambient temperature disturbance and achieves approximately acceptable results. (paper)

  9. Non-intrusive load monitoring based on low frequency active power measurements

    Directory of Open Access Journals (Sweden)

    Chinthaka Dinesh

    2016-03-01

    Full Text Available A Non-Intrusive Load Monitoring (NILM method for residential appliances based on active power signal is presented. This method works effectively with a single active power measurement taken at a low sampling rate (1 s. The proposed method utilizes the Karhunen Loéve (KL expansion to decompose windows of active power signals into subspace components in order to construct a unique set of features, referred to as signatures, from individual and aggregated active power signals. Similar signal windows were clustered in to one group prior to feature extraction. The clustering was performed using a modified mean shift algorithm. After the feature extraction, energy levels of signal windows and power levels of subspace components were utilized to reduce the number of possible appliance combinations and their energy level combinations. Then, the turned on appliance combination and the energy contribution from individual appliances were determined through the Maximum a Posteriori (MAP estimation. Finally, the proposed method was modified to adaptively accommodate the usage patterns of appliances at each residence. The proposed NILM method was validated using data from two public databases: tracebase and reference energy disaggregation data set (REDD. The presented results demonstrate the ability of the proposed method to accurately identify and disaggregate individual energy contributions of turned on appliance combinations in real households. Furthermore, the results emphasise the importance of clustering and the integration of the usage behaviour pattern in the proposed NILM method for real households.

  10. Assessment of groundwater vulnerability to anthropogenic pollution and seawater intrusion in a small tropical island using index-based methods.

    Science.gov (United States)

    Kura, Nura Umar; Ramli, Mohammad Firuz; Ibrahim, Shaharin; Sulaiman, Wan Nor Azmin; Aris, Ahmad Zaharin; Tanko, Adamu Idris; Zaudi, Muhammad Amar

    2015-01-01

    In this work, the DRASTIC and GALDIT models were employed to determine the groundwater vulnerability to contamination from anthropogenic activities and seawater intrusion in Kapas Island. In addition, the work also utilized sensitivity analysis to evaluate the influence of each individual parameter used in developing the final models. Based on these effects and variation indices of the said parameters, new effective weights were determined and were used to create modified DRASTIC and GALDIT models. The final DRASTIC model classified the island into five vulnerability classes: no risk (110-140), low (140-160), moderate (160-180), high (180-200), and very high (>200), covering 4, 26, 59, 4, and 7 % of the island, respectively. Likewise, for seawater intrusion, the modified GALDIT model delineates the island into four vulnerability classes: very low (130) covering 39, 33, 18, and 9 % of the island, respectively. Both models show that the areas that are likely to be affected by anthropogenic pollution and seawater intrusion are within the alluvial deposit at the western part of the island. Pearson correlation was used to verify the reliability of the two models in predicting their respective contaminants. The correlation matrix showed a good relationship between DRASTIC model and nitrate (r = 0.58). In a similar development, the correlation also reveals a very strong negative relationship between GALDIT model and seawater contaminant indicator (resistivity Ωm) values (r = -0.86) suggesting that the model predicts more than 86 % of seawater intrusion. In order to facilitate management strategy, suitable areas for artificial recharge were identified through modeling. The result suggested some areas within the alluvial deposit at the western part of the island as suitable for artificial recharge. This work can serve as a guide for a full vulnerability assessment to anthropogenic pollution and seawater intrusion in small islands and will help policy maker and

  11. An energy estimation framework for event-based methods in Non-Intrusive Load Monitoring

    International Nuclear Information System (INIS)

    Giri, Suman; Bergés, Mario

    2015-01-01

    Highlights: • Energy estimation is NILM has not yet accounted for complexity of appliance models. • We present a data-driven framework for appliance modeling in supervised NILM. • We test the framework on 3 houses and report average accuracies of 5.9–22.4%. • Appliance models facilitate the estimation of energy consumed by the appliance. - Abstract: Non-Intrusive Load Monitoring (NILM) is a set of techniques used to estimate the electricity consumed by individual appliances in a building from measurements of the total electrical consumption. Most commonly, NILM works by first attributing any significant change in the total power consumption (also known as an event) to a specific load and subsequently using these attributions (i.e. the labels for the events) to estimate energy for each load. For this last step, most published work in the field makes simplifying assumptions to make the problem more tractable. In this paper, we present a framework for creating appliance models based on classification labels and aggregate power measurements that can help to relax many of these assumptions. Our framework automatically builds models for appliances to perform energy estimation. The model relies on feature extraction, clustering via affinity propagation, perturbation of extracted states to ensure that they mimic appliance behavior, creation of finite state models, correction of any errors in classification that might violate the model, and estimation of energy based on corrected labels. We evaluate our framework on 3 houses from standard datasets in the field and show that the framework can learn data-driven models based on event labels and use that to estimate energy with lower error margins (e.g., 1.1–42.3%) than when using the heuristic models used by others

  12. Residential Demand Response Behaviour Modeling applied to Cyber-physical Intrusion Detection

    DEFF Research Database (Denmark)

    Heussen, Kai; Tyge, Emil; Kosek, Anna Magdalena

    2017-01-01

    by a mix of physical system parameters, exogenous influences, user behaviour and preferences, which can be characterized by unstructured models such as a time-varying finite impulse response. In this study, which is based on field data, it is shown how this characteristic response behaviours can...

  13. Intrusion Detection and Marking Transactions in a Cloud of Databases Environment

    OpenAIRE

    Syrine Chatti; Habib Ounelli

    2016-01-01

    The cloud computing is a paradigm for large scale distributed computing that includes several existing technologies. A database management is a collection of programs that enables you to store, modify and extract information from a database. Now, the database has moved to cloud computing, but it introduces at the same time a set of threats that target a cloud of database system. The unification of transaction based application in these environments present also a set of vulnerabilities and th...

  14. An Excel®-based visualization tool of 2-D soil gas concentration profiles in petroleum vapor intrusion.

    Science.gov (United States)

    Verginelli, Iason; Yao, Yijun; Suuberg, Eric M

    2016-01-01

    In this study we present a petroleum vapor intrusion tool implemented in Microsoft ® Excel ® using Visual Basic for Applications (VBA) and integrated within a graphical interface. The latter helps users easily visualize two-dimensional soil gas concentration profiles and indoor concentrations as a function of site-specific conditions such as source strength and depth, biodegradation reaction rate constant, soil characteristics and building features. This tool is based on a two-dimensional explicit analytical model that combines steady-state diffusion-dominated vapor transport in a homogeneous soil with a piecewise first-order aerobic biodegradation model, in which rate is limited by oxygen availability. As recommended in the recently released United States Environmental Protection Agency's final Petroleum Vapor Intrusion guidance, a sensitivity analysis and a simplified Monte Carlo uncertainty analysis are also included in the spreadsheet.

  15. Network Anomaly Detection Based on Wavelet Analysis

    Directory of Open Access Journals (Sweden)

    Ali A. Ghorbani

    2008-11-01

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

  16. Network Anomaly Detection Based on Wavelet Analysis

    Science.gov (United States)

    Lu, Wei; Ghorbani, Ali A.

    2008-12-01

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

  17. Stress reaction process-based hierarchical recognition algorithm for continuous intrusion events in optical fiber prewarning system

    Science.gov (United States)

    Qu, Hongquan; Yuan, Shijiao; Wang, Yanping; Yang, Dan

    2018-04-01

    To improve the recognition performance of optical fiber prewarning system (OFPS), this study proposed a hierarchical recognition algorithm (HRA). Compared with traditional methods, which employ only a complex algorithm that includes multiple extracted features and complex classifiers to increase the recognition rate with a considerable decrease in recognition speed, HRA takes advantage of the continuity of intrusion events, thereby creating a staged recognition flow inspired by stress reaction. HRA is expected to achieve high-level recognition accuracy with less time consumption. First, this work analyzed the continuity of intrusion events and then presented the algorithm based on the mechanism of stress reaction. Finally, it verified the time consumption through theoretical analysis and experiments, and the recognition accuracy was obtained through experiments. Experiment results show that the processing speed of HRA is 3.3 times faster than that of a traditional complicated algorithm and has a similar recognition rate of 98%. The study is of great significance to fast intrusion event recognition in OFPS.

  18. An Entropy-Based Network Anomaly Detection Method

    Directory of Open Access Journals (Sweden)

    Przemysław Bereziński

    2015-04-01

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

  19. Perimeter intrusion sensors

    International Nuclear Information System (INIS)

    Eaton, M.J.

    1977-01-01

    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

  20. 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

  1. Rate based failure detection

    Science.gov (United States)

    Johnson, Brett Emery Trabun; Gamage, Thoshitha Thanushka; Bakken, David Edward

    2018-01-02

    This disclosure describes, in part, a system management component and failure detection component for use in a power grid data network to identify anomalies within the network and systematically adjust the quality of service of data published by publishers and subscribed to by subscribers within the network. In one implementation, subscribers may identify a desired data rate, a minimum acceptable data rate, desired latency, minimum acceptable latency and a priority for each subscription. The failure detection component may identify an anomaly within the network and a source of the anomaly. Based on the identified anomaly, data rates and or data paths may be adjusted in real-time to ensure that the power grid data network does not become overloaded and/or fail.

  2. Resident Load Influence Analysis Method for Price Based on Non-intrusive Load Monitoring and Decomposition Data

    Science.gov (United States)

    Jiang, Wenqian; Zeng, Bo; Yang, Zhou; Li, Gang

    2018-01-01

    In the non-invasive load monitoring mode, the load decomposition can reflect the running state of each load, which will help the user reduce unnecessary energy costs. With the demand side management measures of time of using price, a resident load influence analysis method for time of using price (TOU) based on non-intrusive load monitoring data are proposed in the paper. Relying on the current signal of the resident load classification, the user equipment type, and different time series of self-elasticity and cross-elasticity of the situation could be obtained. Through the actual household load data test with the impact of TOU, part of the equipment will be transferred to the working hours, and users in the peak price of electricity has been reduced, and in the electricity at the time of the increase Electrical equipment, with a certain regularity.

  3. Distributed fiber optic moisture intrusion sensing system

    Science.gov (United States)

    Weiss, Jonathan D.

    2003-06-24

    Method and system for monitoring and identifying moisture intrusion in soil such as is contained in landfills housing radioactive and/or hazardous waste. The invention utilizes the principle that moist or wet soil has a higher thermal conductance than dry soil. The invention employs optical time delay reflectometry in connection with a distributed temperature sensing system together with heating means in order to identify discrete areas within a volume of soil wherein temperature is lower. According to the invention an optical element and, optionally, a heating element may be included in a cable or other similar structure and arranged in a serpentine fashion within a volume of soil to achieve efficient temperature detection across a large area or three dimensional volume of soil. Remediation, moisture countermeasures, or other responsive action may then be coordinated based on the assumption that cooler regions within a soil volume may signal moisture intrusion where those regions are located.

  4. 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.

  5. Petroleum Vapor Intrusion

    Science.gov (United States)

    One type of vapor intrusion is PVI, in which vapors from petroleum hydrocarbons such as gasoline, diesel, or jet fuel enter a building. Intrusion of contaminant vapors into indoor spaces is of concern.

  6. Technical bases for leak detection surveillance of waste storage tanks. Revision 1

    International Nuclear Information System (INIS)

    Johnson, M.G.; Badden, J.J.

    1995-01-01

    This document provides the technical bases for specification limits, monitoring frequencies and baselines used for leak detection and intrusion (for single shell tanks only) in all single and double shell radioactive waste storage tanks, waste transfer lines, and most catch tanks and receiver tanks in the waste tank farms and associated areas at Hanford

  7. 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)

  8. A web-based non-intrusive ambient system to measure and classify activities of daily living.

    Science.gov (United States)

    Stucki, Reto A; Urwyler, Prabitha; Rampa, Luca; Müri, René; Mosimann, Urs P; Nef, Tobias

    2014-07-21

    The number of older adults in the global population is increasing. This demographic shift leads to an increasing prevalence of age-associated disorders, such as Alzheimer's disease and other types of dementia. With the progression of the disease, the risk for institutional care increases, which contrasts with the desire of most patients to stay in their home environment. Despite doctors' and caregivers' awareness of the patient's cognitive status, they are often uncertain about its consequences on activities of daily living (ADL). To provide effective care, they need to know how patients cope with ADL, in particular, the estimation of risks associated with the cognitive decline. The occurrence, performance, and duration of different ADL are important indicators of functional ability. The patient's ability to cope with these activities is traditionally assessed with questionnaires, which has disadvantages (eg, lack of reliability and sensitivity). Several groups have proposed sensor-based systems to recognize and quantify these activities in the patient's home. Combined with Web technology, these systems can inform caregivers about their patients in real-time (e.g., via smartphone). We hypothesize that a non-intrusive system, which does not use body-mounted sensors, video-based imaging, and microphone recordings would be better suited for use in dementia patients. Since it does not require patient's attention and compliance, such a system might be well accepted by patients. We present a passive, Web-based, non-intrusive, assistive technology system that recognizes and classifies ADL. The components of this novel assistive technology system were wireless sensors distributed in every room of the participant's home and a central computer unit (CCU). The environmental data were acquired for 20 days (per participant) and then stored and processed on the CCU. In consultation with medical experts, eight ADL were classified. In this study, 10 healthy participants (6 women

  9. A 10-year Ground-Based Radar Climatology of Convective Penetration of Stratospheric Intrusions and Associated Large-Scale Transport over the CONUS

    Science.gov (United States)

    Homeyer, C. R.

    2017-12-01

    Deep convection reaching the upper troposphere and lower stratosphere (UTLS) and its impact on atmospheric composition through rapid vertical transport of lower troposphere air and stratosphere-troposphere exchange has received increasing attention in the past 5-10 years. Most efforts focused on convection have been directed toward storms that reach and/or penetrate the coincident environmental lapse-rate tropopause. However, convection has also been shown to reach into large-scale stratospheric intrusions (depressions of stratospheric air lying well below the lapse-rate tropopause on the cyclonic side of upper troposphere jet streams). Such convective penetration of stratospheric intrusions is not captured by studies of lapse-rate tropopause-penetrating convection. In this presentation, it will be shown using hourly, high-quality mergers of ground-based radar observations from 2004 to 2013 in the contiguous United States (CONUS) and forward large-scale trajectory analysis that convective penetration of stratospheric intrusions: 1) is more frequent than lapse-rate tropopause-penetrating convection, 2) occurs over a broader area of the CONUS than lapse-rate tropopause-penetrating convection, and 3) can influence the composition of the lower stratosphere through large-scale advection of convectively influenced air to altitudes above the lapse-rate tropopause, which we find to occur for about 8.5% of the intrusion volumes reached by convection.

  10. 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.

  11. A new detection method based on CFAR and DE for OFPS

    Science.gov (United States)

    Qiu, Zezheng; Zheng, Tong; Qu, Hongquan; Pang, Liping

    2016-09-01

    Optical fiber pre-warning system (OFPS) is widely utilized in pipeline transport fields. The intrusions of OFPS need to be located. In this system, the original signals consist of noises, interferences, and intrusion signals. Here, noises are background and harmless interferences possessing with high power, and the intrusion signals are the main target of detection in this system. Hence, the study stresses on extracting the intrusion signals from the total ones. The proposed method can be divided into two parts, constant false alarm rate (CFAR) and dilation and erosion (DE). The former is applied to eliminate noises, and the latter is to remove interferences. According to some researches, the feature of noise background accords with the CFAR spatial detection. Furthermore, the detection results after CFAR can be presented as a binary image of time and space. Besides, interferences are relatively disconnected. Consequently, they can be eliminated by DE which is introduced from the image processing. To sum up, this novel method is based on CFAR and DE which can eliminate noises and interferences effectively. Moreover, it performs a brilliant detection performance. A series of tests were developed in Men Tou Gou of Beijing, China, and the reliability of proposed method can be verified by these tests.

  12. An Artificial Immune System-Inspired Multiobjective Evolutionary Algorithm with Application to the Detection of Distributed Computer Network Intrusions

    Science.gov (United States)

    2007-03-01

    Optimization Coello, Van Veldhuizen , and Lamont define global optimization as, “the process of finding the global minimum4 within some search space S [CVL02...Technology, Shapes Markets, and Manages People, Simon & Schuster, New York, 1995. [CVL02] Coello, C., Van Veldhuizen , D., Lamont, G.B., Evolutionary...Anomaly Detection, Technical Report CS- 2003-02, Computer Science Department, Florida Institute of Technology, 2003. [Marmelstein99] Marmelstein, R., Van

  13. Real-Time, Non-Intrusive Detection of Liquid Nitrogen in Liquid Oxygen at High Pressure and High Flow

    Science.gov (United States)

    Singh, Jagdish P.; Yueh, Fang-Yu; Kalluru, Rajamohan R.; Harrison, Louie

    2012-01-01

    An integrated fiber-optic Raman sensor has been designed for real-time, nonintrusive detection of liquid nitrogen in liquid oxygen (LOX) at high pressures and high flow rates in order to monitor the quality of LOX used during rocket engine ground testing. The integrated sensor employs a high-power (3-W) Melles Griot diode-pumped, solid-state (DPSS), frequency-doubled Nd:YAG 532- nm laser; a modified Raman probe that has built-in Raman signal filter optics; two high-resolution spectrometers; and photomultiplier tubes (PMTs) with selected bandpass filters to collect both N2 and O2 Raman signals. The PMT detection units are interfaced with National Instruments Lab- VIEW for fast data acquisition. Studies of sensor performance with different detection systems (i.e., spectrometer and PMT) were carried out. The concentration ratio of N2 and O2 can be inferred by comparing the intensities of the N2 and O2 Raman signals. The final system was fabricated to measure N2 and O2 gas mixtures as well as mixtures of liquid N2 and LOX

  14. Options for human intrusion

    International Nuclear Information System (INIS)

    Bauser, M.; Williams, R.

    1993-01-01

    This paper addresses options for dealing with human intrusion in terms of performance requirements and repository siting and design requirements. Options are presented, along with the advantages and disadvantages of certain approaches. At the conclusion, a conceptual approach is offered emphasizing both the minimization of subjective judgements concerning future human activity, and specification of repository requirements to minimize the likelihood of human intrusion and any resulting, harmful effects should intrusion occur

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2005-11-01

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

  16. Detection of network attacks based on adaptive resonance theory

    Science.gov (United States)

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

    2018-05-01

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

  17. Fractal analysis of SEM images and mercury intrusion porosimetry data for the microstructural characterization of microcrystalline cellulose-based pellets

    International Nuclear Information System (INIS)

    Gomez-Carracedo, A.; Alvarez-Lorenzo, C.; Coca, R.; Martinez-Pacheco, R.; Concheiro, A.; Gomez-Amoza, J.L.

    2009-01-01

    The microstructure of theophylline pellets prepared from microcrystalline cellulose, carbopol and dicalcium phosphate dihydrate, according to a mixture design, was characterized using textural analysis of gray-level scanning electron microscopy (SEM) images and thermodynamic analysis of the cumulative pore volume distribution obtained by mercury intrusion porosimetry. Surface roughness evaluated in terms of gray-level non-uniformity and fractal dimension of pellet surface depended on agglomeration phenomena during extrusion/spheronization. Pores at the surface, mainly 1-15 μm in diameter, determined both the mechanism and the rate of theophylline release, and a strong negative correlation between the fractal geometry and the b parameter of the Weibull function was found for pellets containing >60% carbopol. Theophylline mean dissolution time from these pellets was about two to four times greater. Textural analysis of SEM micrographs and fractal analysis of mercury intrusion data are complementary techniques that enable complete characterization of multiparticulate drug dosage forms

  18. Volcano monitoring using GPS: Developing data analysis strategies based on the June 2007 Kīlauea Volcano intrusion and eruption

    Science.gov (United States)

    Larson, Kristine M.; Poland, Michael; Miklius, Asta

    2010-01-01

    The global positioning system (GPS) is one of the most common techniques, and the current state of the art, used to monitor volcano deformation. In addition to slow (several centimeters per year) displacement rates, GPS can be used to study eruptions and intrusions that result in much larger (tens of centimeters over hours-days) displacements. It is challenging to resolve precise positions using GPS at subdaily time intervals because of error sources such as multipath and atmospheric refraction. In this paper, the impact of errors due to multipath and atmospheric refraction at subdaily periods is examined using data from the GPS network on Kīlauea Volcano, Hawai'i. Methods for filtering position estimates to enhance precision are both simulated and tested on data collected during the June 2007 intrusion and eruption. Comparisons with tiltmeter records show that GPS instruments can precisely recover the timing of the activity.

  19. A fuzzy logic based network intrusion detection system for predicting the TCP SYN flooding attack

    CSIR Research Space (South Africa)

    Mkuzangwe, Nenekazi NP

    2017-04-01

    Full Text Available decision tree which is one of the well-known machine learning techniques. The results indicate that the performance difference, in terms of predicting the proportion of attacks in the data, of the proposed system with respect to the decision tree...

  20. A Survey of Visualization Tools Assessed for Anomaly-Based Intrusion Detection Analysis

    Science.gov (United States)

    2014-04-01

    includes Complex System SCILAB Toolbox, GraphViz, Igraph, NetDraw, Network Workbench, OpenDX, Prefuse, Sci² Tool, and Visualization Toolkit (VTK...Kits’ Capabilities Name Web Sites (all accessed 01/29/2014 Strengths Weaknesses Complex Systems SCILAB Tool http://www.randomfactory.com/openastro...osx/ scilab -info.html Measures graph parameters Academic Free License (AFL); works on UNIX and Windows; programming language is MATLAB; no

  1. Network Based Intrusion Detection and Prevention Systems in IP-Level Security Protocols

    OpenAIRE

    R. Kabila

    2008-01-01

    IPsec has now become a standard information security technology throughout the Internet society. It provides a well-defined architecture that takes into account confidentiality, authentication, integrity, secure key exchange and protection mechanism against replay attack also. For the connectionless security services on packet basis, IETF IPsec Working Group has standardized two extension headers (AH&ESP), key exchange and authentication protocols. It is also working on l...

  2. Building an intrusion detection system using a filter-based feature selection algorithm

    NARCIS (Netherlands)

    Ambusaidi, Mohammed A.; He, Xiangjian; Nanda, Priyadarsi; Tan, Zhiyuan

    2016-01-01

    Redundant and irrelevant features in data have caused a long-term problem in network traffic classification. These features not only slow down the process of classification but also prevent a classifier from making accurate decisions, especially when coping with big data. In this paper, we propose a

  3. MFIRE-2: A Multi Agent System for Flow-Based Intrusion Detection Using Stochastic Search

    Science.gov (United States)

    2012-03-01

    Algorithms to pattern recognition comes from Radtke et al. [72]. The authors apply Multi- Objective Genetic Algorithms (MOGAs) to two parts of a handwritten...Postel, J.B. “User Datagram Protocol. RFC 768”, 1980. [72] Radtke , Paulo V. W., Robert Sabourin, and Tony Wong. “Classification system optimization...Rennes 1, Suvisoft, La Baule (France), 10 2006. URL http://hal.inria.fr/inria-00104200/en/. [73] Radtke , P.V.W., T. Wong, and R. Sabourin. “A multi

  4. A Multi Agent System for Flow-Based Intrusion Detection Using Reputation and Evolutionary Computation

    Science.gov (United States)

    2011-03-01

    pertinent example of the application of Evolutionary Algorithms to pattern recognition comes from Radtke et al. [130]. The authors apply Multi- Objective...J., T. Zseby, and B. Claise. S. Zander,” Requirements for IP Flow Information Export (IPFIX). Technical report, RFC 3917, October 2004. [130] Radtke ...hal.inria.fr/inria-00104200/en/. [131] Radtke , P.V.W., T. Wong, and R. Sabourin. “A multi-objective memetic al- gorithm for intelligent feature extraction

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

    CSIR Research Space (South Africa)

    Naidoo, Tyrone

    2015-09-01

    Full Text Available data, which is rarely available in operational networks. It uses normalized cluster validity indices as an objective function that is optimized over the search space of candidate feature subsets via a genetic algorithm. Feature sets produced...

  6. Extremely Lightweight Intrusion Detection (ELIDe)

    Science.gov (United States)

    2013-12-01

    conventional computing platform (Dell Inspiron 15N laptop running Mint Maya as the operating system, dual-core Core i5 CPU, 8 GB RAM), Snort exhibited a peak...distribute network monitoring duties amongst the participants in the network with the goal of conserving the network’s overall computational demand

  7. Intrusion Detection in SCADA Networks

    NARCIS (Netherlands)

    Barbosa, R.R.R.; Pras, Aiko; Stiller, Burckhard; De Turck, Filip

    Supervisory Control and Data Acquisition (SCADA) sys- tems are a critical part of large industrial facilities, such as water dis- tribution infrastructures. With the goal of reducing costs and increas- ing efficiency, these systems are becoming increasingly interconnected. However, this has also

  8. Big Data Analytics for Flow-based Anomaly Detection in High-Speed Networks

    OpenAIRE

    Garofalo, Mauro

    2017-01-01

    The Cisco VNI Complete Forecast Highlights clearly states that the Internet traffic is growing in three different directions, Volume, Velocity, and Variety, bringing computer network into the big data era. At the same time, sophisticated network attacks are growing exponentially. Such growth making the existing signature-based security tools, like firewall and traditional intrusion detection systems, ineffective against new kind of attacks or variations of known attacks. In this dissertati...

  9. 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

  10. 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

  11. Comparison and Characterization of Android-Based Fall Detection Systems

    Directory of Open Access Journals (Sweden)

    Rafael Luque

    2014-10-01

    Full Text Available Falls are a foremost source of injuries and hospitalization for seniors. The adoption of automatic fall detection mechanisms can noticeably reduce the response time of the medical staff or caregivers when a fall takes place. Smartphones are being increasingly proposed as wearable, cost-effective and not-intrusive systems for fall detection. The exploitation of smartphones’ potential (and in particular, the Android Operating System can benefit from the wide implantation, the growing computational capabilities and the diversity of communication interfaces and embedded sensors of these personal devices. After revising the state-of-the-art on this matter, this study develops an experimental testbed to assess the performance of different fall detection algorithms that ground their decisions on the analysis of the inertial data registered by the accelerometer of the smartphone. Results obtained in a real testbed with diverse individuals indicate that the accuracy of the accelerometry-based techniques to identify the falls depends strongly on the fall pattern. The performed tests also show the difficulty to set detection acceleration thresholds that allow achieving a good trade-off between false negatives (falls that remain unnoticed and false positives (conventional movements that are erroneously classified as falls. In any case, the study of the evolution of the battery drain reveals that the extra power consumption introduced by the Android monitoring applications cannot be neglected when evaluating the autonomy and even the viability of fall detection systems.

  12. Comparison and characterization of Android-based fall detection systems.

    Science.gov (United States)

    Luque, Rafael; Casilari, Eduardo; Morón, María-José; Redondo, Gema

    2014-10-08

    Falls are a foremost source of injuries and hospitalization for seniors. The adoption of automatic fall detection mechanisms can noticeably reduce the response time of the medical staff or caregivers when a fall takes place. Smartphones are being increasingly proposed as wearable, cost-effective and not-intrusive systems for fall detection. The exploitation of smartphones' potential (and in particular, the Android Operating System) can benefit from the wide implantation, the growing computational capabilities and the diversity of communication interfaces and embedded sensors of these personal devices. After revising the state-of-the-art on this matter, this study develops an experimental testbed to assess the performance of different fall detection algorithms that ground their decisions on the analysis of the inertial data registered by the accelerometer of the smartphone. Results obtained in a real testbed with diverse individuals indicate that the accuracy of the accelerometry-based techniques to identify the falls depends strongly on the fall pattern. The performed tests also show the difficulty to set detection acceleration thresholds that allow achieving a good trade-off between false negatives (falls that remain unnoticed) and false positives (conventional movements that are erroneously classified as falls). In any case, the study of the evolution of the battery drain reveals that the extra power consumption introduced by the Android monitoring applications cannot be neglected when evaluating the autonomy and even the viability of fall detection systems.

  13. Comparison and Characterization of Android-Based Fall Detection Systems

    Science.gov (United States)

    Luque, Rafael; Casilari, Eduardo; Morón, María-José; Redondo, Gema

    2014-01-01

    Falls are a foremost source of injuries and hospitalization for seniors. The adoption of automatic fall detection mechanisms can noticeably reduce the response time of the medical staff or caregivers when a fall takes place. Smartphones are being increasingly proposed as wearable, cost-effective and not-intrusive systems for fall detection. The exploitation of smartphones' potential (and in particular, the Android Operating System) can benefit from the wide implantation, the growing computational capabilities and the diversity of communication interfaces and embedded sensors of these personal devices. After revising the state-of-the-art on this matter, this study develops an experimental testbed to assess the performance of different fall detection algorithms that ground their decisions on the analysis of the inertial data registered by the accelerometer of the smartphone. Results obtained in a real testbed with diverse individuals indicate that the accuracy of the accelerometry-based techniques to identify the falls depends strongly on the fall pattern. The performed tests also show the difficulty to set detection acceleration thresholds that allow achieving a good trade-off between false negatives (falls that remain unnoticed) and false positives (conventional movements that are erroneously classified as falls). In any case, the study of the evolution of the battery drain reveals that the extra power consumption introduced by the Android monitoring applications cannot be neglected when evaluating the autonomy and even the viability of fall detection systems. PMID:25299953

  14. Detecting Malicious Nodes in Medical Smartphone Networks Through Euclidean Distance-Based Behavioral Profiling

    DEFF Research Database (Denmark)

    Meng, Weizhi; Li, Wenjuan; Wang, Yu

    2017-01-01

    and healthcare personnel. The underlying network architecture to support such devices is also referred to as medical smartphone networks (MSNs). Similar to other networks, MSNs also suffer from various attacks like insider attacks (e.g., leakage of sensitive patient information by a malicious insider......). In this work, we focus on MSNs and design a trust-based intrusion detection approach through Euclidean distance-based behavioral profiling to detect malicious devices (or called nodes). In the evaluation, we collaborate with healthcare organizations and implement our approach in a real simulated MSN...

  15. 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

  16. A hierarchical detection method in external communication for self-driving vehicles based on TDMA

    Science.gov (United States)

    Al-ani, Muzhir Shaban; McDonald-Maier, Klaus

    2018-01-01

    Security is considered a major challenge for self-driving and semi self-driving vehicles. These vehicles depend heavily on communications to predict and sense their external environment used in their motion. They use a type of ad hoc network termed Vehicular ad hoc networks (VANETs). Unfortunately, VANETs are potentially exposed to many attacks on network and application level. This paper, proposes a new intrusion detection system to protect the communication system of self-driving cars; utilising a combination of hierarchical models based on clusters and log parameters. This security system is designed to detect Sybil and Wormhole attacks in highway usage scenarios. It is based on clusters, utilising Time Division Multiple Access (TDMA) to overcome some of the obstacles of VANETs such as high density, high mobility and bandwidth limitations in exchanging messages. This makes the security system more efficient, accurate and capable of real time detection and quick in identification of malicious behaviour in VANETs. In this scheme, each vehicle log calculates and stores different parameter values after receiving the cooperative awareness messages from nearby vehicles. The vehicles exchange their log data and determine the difference between the parameters, which is utilised to detect Sybil attacks and Wormhole attacks. In order to realize efficient and effective intrusion detection system, we use the well-known network simulator (ns-2) to verify the performance of the security system. Simulation results indicate that the security system can achieve high detection rates and effectively detect anomalies with low rate of false alarms. PMID:29315302

  17. A hierarchical detection method in external communication for self-driving vehicles based on TDMA.

    Science.gov (United States)

    Alheeti, Khattab M Ali; Al-Ani, Muzhir Shaban; McDonald-Maier, Klaus

    2018-01-01

    Security is considered a major challenge for self-driving and semi self-driving vehicles. These vehicles depend heavily on communications to predict and sense their external environment used in their motion. They use a type of ad hoc network termed Vehicular ad hoc networks (VANETs). Unfortunately, VANETs are potentially exposed to many attacks on network and application level. This paper, proposes a new intrusion detection system to protect the communication system of self-driving cars; utilising a combination of hierarchical models based on clusters and log parameters. This security system is designed to detect Sybil and Wormhole attacks in highway usage scenarios. It is based on clusters, utilising Time Division Multiple Access (TDMA) to overcome some of the obstacles of VANETs such as high density, high mobility and bandwidth limitations in exchanging messages. This makes the security system more efficient, accurate and capable of real time detection and quick in identification of malicious behaviour in VANETs. In this scheme, each vehicle log calculates and stores different parameter values after receiving the cooperative awareness messages from nearby vehicles. The vehicles exchange their log data and determine the difference between the parameters, which is utilised to detect Sybil attacks and Wormhole attacks. In order to realize efficient and effective intrusion detection system, we use the well-known network simulator (ns-2) to verify the performance of the security system. Simulation results indicate that the security system can achieve high detection rates and effectively detect anomalies with low rate of false alarms.

  18. A new intrusion prevention model using planning knowledge graph

    Science.gov (United States)

    Cai, Zengyu; Feng, Yuan; Liu, Shuru; Gan, Yong

    2013-03-01

    Intelligent plan is a very important research in artificial intelligence, which has applied in network security. This paper proposes a new intrusion prevention model base on planning knowledge graph and discuses the system architecture and characteristics of this model. The Intrusion Prevention based on plan knowledge graph is completed by plan recognition based on planning knowledge graph, and the Intrusion response strategies and actions are completed by the hierarchical task network (HTN) planner in this paper. Intrusion prevention system has the advantages of intelligent planning, which has the advantage of the knowledge-sharing, the response focused, learning autonomy and protective ability.

  19. Intrusion Prevention System Based on the Aççess Control Mechanism in the Operating System Miçrosoft Windows

    Directory of Open Access Journals (Sweden)

    V. S. Matveeva

    2012-03-01

    Full Text Available It is suggested to implement an intrusion prevention system based on the access control mechanism of Microsoft Windows operating system to restrict the execution of malicious code. Most of the existing computer security facilities use behavioral and heuristic analyses based on an undocumented method of system calls interception that is not an uniform approach in designing of proactive security mechanism. The IPS is portable among different versions of the OS because it is implemented with documented functions only, it does not need to be updated and uses less system resources in comparison with another protection systems. The system protects from zero-day malware and therefore prevents companies from online-banking fraud that is a very actual problem of information security nowadays.

  20. Network anomaly detection a machine learning perspective

    CERN Document Server

    Bhattacharyya, Dhruba Kumar

    2013-01-01

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

  1. Efficient Network Monitoring for Attack Detection

    OpenAIRE

    Limmer, Tobias

    2011-01-01

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

  2. Toolbox for non-intrusive structural and functional analysis of recombinant VLP based vaccines: a case study with hepatitis B vaccine.

    Directory of Open Access Journals (Sweden)

    Anke M Mulder

    Full Text Available BACKGROUND: Fundamental to vaccine development, manufacturing consistency, and product stability is an understanding of the vaccine structure-activity relationship. With the virus-like particle (VLP approach for recombinant vaccines gaining popularity, there is growing demand for tools that define their key characteristics. We assessed a suite of non-intrusive VLP epitope structure and function characterization tools by application to the Hepatitis B surface antigen (rHBsAg VLP-based vaccine. METHODOLOGY: The epitope-specific immune reactivity of rHBsAg epitopes to a given monoclonal antibody was monitored by surface plasmon resonance (SPR and quantitatively analyzed on rHBsAg VLPs in-solution or bound to adjuvant with a competitive enzyme-linked immunosorbent assay (ELISA. The structure of recombinant rHBsAg particles was examined by cryo transmission electron microscopy (cryoTEM and in-solution atomic force microscopy (AFM. PRINCIPAL FINDINGS: SPR and competitive ELISA determined relative antigenicity in solution, in real time, with rapid turn-around, and without the need of dissolving the particulate aluminum based adjuvant. These methods demonstrated the nature of the clinically relevant epitopes of HBsAg as being responsive to heat and/or redox treatment. In-solution AFM and cryoTEM determined vaccine particle size distribution, shape, and morphology. Redox-treated rHBsAg enabled 3D reconstruction from CryoTEM images--confirming the previously proposed octahedral structure and the established lipid-to-protein ratio of HBsAg particles. Results from these non-intrusive biophysical and immunochemical analyses coalesced into a comprehensive understanding of rHBsAg vaccine epitope structure and function that was important for assuring the desired epitope formation, determinants for vaccine potency, and particle stability during vaccine design, development, and manufacturing. SIGNIFICANCE: Together, the methods presented here comprise a novel

  3. OPNET/Simulink Based Testbed for Disturbance Detection in the Smart Grid

    Energy Technology Data Exchange (ETDEWEB)

    Sadi, Mohammad A. H. [University of Memphis; Dasgupta, Dipankar [ORNL; Ali, Mohammad Hassan [University of Memphis; Abercrombie, Robert K [ORNL

    2015-01-01

    The important backbone of the smart grid is the cyber/information infrastructure, which is primarily used to communicate with different grid components. A smart grid is a complex cyber physical system containing a numerous and variety number of sources, devices, controllers and loads. Therefore, the smart grid is vulnerable to grid related disturbances. For such dynamic system, disturbance and intrusion detection is a paramount issue. This paper presents a Simulink and Opnet based co-simulated platform to carry out a cyber-intrusion in cyber network for modern power systems and the smart grid. The IEEE 30 bus power system model is used to demonstrate the effectiveness of the simulated testbed. The experiments were performed by disturbing the circuit breakers reclosing time through a cyber-attack. Different disturbance situations in the considered test system are considered and the results indicate the effectiveness of the proposed co-simulated scheme.

  4. A Nuisance Alarm Data System for evaluation of intrusion detectors

    International Nuclear Information System (INIS)

    Ream, W.K.

    1990-01-01

    A Nuisance Alarm Data System (NADS) was developed to gather long-term background alarm data on exterior intrusion detectors as part of their evaluation. Since nuisance alarms play an important part in the selection of intrusion detectors for use at Department of Energy (DOE) facilities, an economical and reliable way to monitor and record these alarms was needed. NADS consists of an IBM personal computer and printer along with other commercial units to communicate with the detectors, to gather weather data, and to record video for assessment. Each alarm, its assessment, and the weather conditions occurring at alarm time are placed into a data base that is used in the evaluation of the detector. The operating software is written in Turbo Pascal for easy maintenance and modification. A portable system, based on the NADS design, has been built and shipped to other DOE locations to do on-site alarm monitoring. This has been valuable for the comparison of different detectors in the on-site environment and for testing new detectors when the appropriate conditions do not exist or cannot be simulated at the Exterior Intrusion Detection Testbed

  5. Reanalysis of the consequences of a beyond-design based brine intrusion in the Schachtanlage Asse II. Further development of the radio-ecological modeling

    International Nuclear Information System (INIS)

    Kueppers, Christian; Ustohalova, Veronika; Steinhoff, Mathias

    2011-01-01

    The reanalysis of the consequences of a beyond-design based brine intrusion in the Schachtanlage Asse includes the following chapters: (1) Scope and introduction. (2) Overview on the methodology. (3) Modeling of the transport in the overlying rock and radiation exposure: Modeling of the radionuclide migration in the overlying rock considering the solubility limits and different retention and retarding effects; description of the used model for the radiation exposure calculations, quality assurance of the model and the calculations. (4) Results and reanalysis for the scenario I (GRS scenario). (5) Radiation exposure considering the solubility limits and transport calculations (scenario II). (6) Possibilities of monitoring in the Asse environment. (7) Estimation of collective doses as a consequence of contaminated water release into the biosphere and use as drinking water. (8) Discussion of the radiation exposure calculations in the former study of GRS (2009).

  6. Domain similarity based orthology detection.

    Science.gov (United States)

    Bitard-Feildel, Tristan; Kemena, Carsten; Greenwood, Jenny M; Bornberg-Bauer, Erich

    2015-05-13

    Orthologous protein detection software mostly uses pairwise comparisons of amino-acid sequences to assert whether two proteins are orthologous or not. Accordingly, when the number of sequences for comparison increases, the number of comparisons to compute grows in a quadratic order. A current challenge of bioinformatic research, especially when taking into account the increasing number of sequenced organisms available, is to make this ever-growing number of comparisons computationally feasible in a reasonable amount of time. We propose to speed up the detection of orthologous proteins by using strings of domains to characterize the proteins. We present two new protein similarity measures, a cosine and a maximal weight matching score based on domain content similarity, and new software, named porthoDom. The qualities of the cosine and the maximal weight matching similarity measures are compared against curated datasets. The measures show that domain content similarities are able to correctly group proteins into their families. Accordingly, the cosine similarity measure is used inside porthoDom, the wrapper developed for proteinortho. porthoDom makes use of domain content similarity measures to group proteins together before searching for orthologs. By using domains instead of amino acid sequences, the reduction of the search space decreases the computational complexity of an all-against-all sequence comparison. We demonstrate that representing and comparing proteins as strings of discrete domains, i.e. as a concatenation of their unique identifiers, allows a drastic simplification of search space. porthoDom has the advantage of speeding up orthology detection while maintaining a degree of accuracy similar to proteinortho. The implementation of porthoDom is released using python and C++ languages and is available under the GNU GPL licence 3 at http://www.bornberglab.org/pages/porthoda .

  7. Interior intrusion alarm systems

    International Nuclear Information System (INIS)

    Prell, J.A.

    1978-01-01

    In meeting the requirements for the safeguarding of special nuclear material and the physical protection of licensed facilities, the licensee is required to design a physical security system that will meet minimum performance requirements. An integral part of any physical security system is the interior intrusion alarm system. The purpose of this report is to provide the potential user of an interior intrusion alarm system with information on the various types, components, and performance capabilities available so that he can design and install the optimum alarm system for his particular environment. In addition, maintenance and testing procedures are discussed and recommended which, if followed, will help the user obtain the optimum results from his system

  8. 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.

  9. Multi-lane detection based on multiple vanishing points detection

    Science.gov (United States)

    Li, Chuanxiang; Nie, Yiming; Dai, Bin; Wu, Tao

    2015-03-01

    Lane detection plays a significant role in Advanced Driver Assistance Systems (ADAS) for intelligent vehicles. In this paper we present a multi-lane detection method based on multiple vanishing points detection. A new multi-lane model assumes that a single lane, which has two approximately parallel boundaries, may not parallel to others on road plane. Non-parallel lanes associate with different vanishing points. A biological plausibility model is used to detect multiple vanishing points and fit lane model. Experimental results show that the proposed method can detect both parallel lanes and non-parallel lanes.

  10. Microcontroller based driver alertness detection systems to detect drowsiness

    Science.gov (United States)

    Adenin, Hasibah; Zahari, Rahimi; Lim, Tiong Hoo

    2018-04-01

    The advancement of embedded system for detecting and preventing drowsiness in a vehicle is a major challenge for road traffic accident systems. To prevent drowsiness while driving, it is necessary to have an alert system that can detect a decline in driver concentration and send a signal to the driver. Studies have shown that traffc accidents usually occur when the driver is distracted while driving. In this paper, we have reviewed a number of detection systems to monitor the concentration of a car driver and propose a portable Driver Alertness Detection System (DADS) to determine the level of concentration of the driver based on pixelated coloration detection technique using facial recognition. A portable camera will be placed at the front visor to capture facial expression and the eye activities. We evaluate DADS using 26 participants and have achieved 100% detection rate with good lighting condition and a low detection rate at night.

  11. Intrusive Images in Psychological Disorders

    Science.gov (United States)

    Brewin, Chris R.; Gregory, James D.; Lipton, Michelle; Burgess, Neil

    2010-01-01

    Involuntary images and visual memories are prominent in many types of psychopathology. Patients with posttraumatic stress disorder, other anxiety disorders, depression, eating disorders, and psychosis frequently report repeated visual intrusions corresponding to a small number of real or imaginary events, usually extremely vivid, detailed, and with highly distressing content. Both memory and imagery appear to rely on common networks involving medial prefrontal regions, posterior regions in the medial and lateral parietal cortices, the lateral temporal cortex, and the medial temporal lobe. Evidence from cognitive psychology and neuroscience implies distinct neural bases to abstract, flexible, contextualized representations (C-reps) and to inflexible, sensory-bound representations (S-reps). We revise our previous dual representation theory of posttraumatic stress disorder to place it within a neural systems model of healthy memory and imagery. The revised model is used to explain how the different types of distressing visual intrusions associated with clinical disorders arise, in terms of the need for correct interaction between the neural systems supporting S-reps and C-reps via visuospatial working memory. Finally, we discuss the treatment implications of the new model and relate it to existing forms of psychological therapy. PMID:20063969

  12. Potential fire detection based on Kalman-driven change detection

    CSIR Research Space (South Africa)

    Van Den Bergh, F

    2009-07-01

    Full Text Available A new active fire event detection algorithm for data collected with the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor, based on the extended Kalman filter, is introduced. Instead of using the observed temperatures of the spatial...

  13. Accelerator based techniques for contraband detection

    Science.gov (United States)

    Vourvopoulos, George

    1994-05-01

    It has been shown that narcotics, explosives, and other contraband materials, contain various chemical elements such as H, C, N, O, P, S, and Cl in quantities and ratios that differentiate them from each other and from other innocuous substances. Neutrons and γ-rays have the ability to penetrate through various materials at large depths. They are thus able, in a non-intrusive way, to interrogate volumes ranging from suitcases to Sea-Land containers, and have the ability to image the object with an appreciable degree of reliability. Neutron induced reactions such as (n, γ), (n, n') (n, p) or proton induced γ-resonance absorption are some of the reactions currently investigated for the identification of the chemical elements mentioned above. Various DC and pulsed techniques are discussed and their advantages, characteristics, and current progress are shown. Areas where use of these methods is currently under evaluation are detection of hidden explosives, illicit drug interdiction, chemical war agents identification, nuclear waste assay, nuclear weapons destruction and others.

  14. PENURUNAN TINGKAT INTRUSI AIR LAUT BERDASARKAN "CHLORIDE BICARBONATE RATIO" MENGGUNAKAN LUBANG RESAPAN BIOPORI : STUDI KASUS DI KOTA SEMARANG (Decreased of Sea Water Intrusion Based On “Chloride-Bicarbonater Ratio”Using the Biopore Absorption Hole

    Directory of Open Access Journals (Sweden)

    Siti Sundari Miswadi

    2010-11-01

    using the Biopore Absorption Hole (LRB as an absorbent showed during the third week after the construction of LRB, it was acquired that the sea water intrusion rates increased by 66.64% wells, and as many 33.33% wells decreased, whereas at the sixth week the sea water intrusion rates of 66.64% wells decreased whether 33.33% wells increased. Levels of salinity in the third week of 83.30% wells decreased, and 16.66% wells remained the same, whereas in the sixth week the salinity level of 49.90% wells increased, 33.33% wells decreased and 16.66% wells remained the same. Electrical conductivity (DHL in the third week was detected to increase in 16.66% wells, and decrease in 83.30% wells, while the sixth week, the DHL in 49.98% wells increased and 49.98% wells decreased. pH value in the third week was detected to decrease in 100% wells, while in the sixth week, the pH value decreased in 66.64% wells and 33.33% wells increased. Levels of NO3- in the third week and sixth week obtained the same results, which were 16.66% wells increased and 83.30% wells decreased. PO43- levels in the third week was detected for 100% of the wells decreased, while the sixth week 16.66% wells decreased and 83.30% wells increased. Turbidity value in the third week was detected to increase in 16.66% wells and 83.30% wells decreased, while the sixth week 49.98% wells increased in their turbidity and 49.98% wells decreased. Based on the research, it can be concluded that after the use of LRB in the third week, as many 82.41% wells decreased and 17.59% wells increased in the seven parameters, while in the sixth week as many 54.75% wells decreased and 45.25% wells increased in seven parameters. Optimal results of LRB usage as absorbent were obtained in the sixth week.

  15. Vision-based Vehicle Detection Survey

    Directory of Open Access Journals (Sweden)

    Alex David S

    2016-03-01

    Full Text Available Nowadays thousands of drivers and passengers were losing their lives every year on road accident, due to deadly crashes between more than one vehicle. There are number of many research focuses were dedicated to the development of intellectual driver assistance systems and autonomous vehicles over the past decade, which reduces the danger by monitoring the on-road environment. In particular, researchers attracted towards the on-road detection of vehicles in recent years. Different parameters have been analyzed in this paper which includes camera placement and the various applications of monocular vehicle detection, common features and common classification methods, motion- based approaches and nighttime vehicle detection and monocular pose estimation. Previous works on the vehicle detection listed based on camera poisons, feature based detection and motion based detection works and night time detection.

  16. Population-based screening versus case detection.

    Directory of Open Access Journals (Sweden)

    Thomas Ravi

    2002-01-01

    Full Text Available India has a large burden of blindness and population-based screening is a strategy commonly employed to detect disease and prevent morbidity. However, not all diseases are amenable to screening. This communication examines the issue of "population-based screening" versus "case detection" in the Indian scenario. Using the example of glaucoma, it demonstrates that given the poor infrastructure, for a "rare" disease, case detection is more effective than population-based screening.

  17. Intrusive and Non-Intrusive Load Monitoring (A Survey

    Directory of Open Access Journals (Sweden)

    Marco Danilo Burbano Acuña

    2015-05-01

    Full Text Available There is not discussion about the need of energyconservation, it is well known that energy resources are limitedmoreover the global energy demands will double by the end of2030, which certainly will bring implications on theenvironment and hence to all of us.Non-Intrusive load monitoring (NILM is the process ofrecognize electrical devices and its energy consumption basedon whole home electric signals, where this aggregated load datais acquired from a single point of measurement outside thehousehold. The aim of this approach is to get optimal energyconsumption and avoid energy wastage. Intrusive loadmonitoring (ILM is the process of identify and locate singledevices through the use of sensing systems to support control,monitor and intervention of such devices. The aim of thisapproach is to offer a base for the development of importantapplications for remote and automatic intervention of energyconsumption inside buildings and homes as well. For generalpurposes this paper states a general framework of NILM andILM approaches.Appliance discerns can be tackled using approaches fromdata mining and machine learning, finding out the techniquesthat fit the best this requirements, is a key factor for achievingfeasible and suitable appliance load monitoring solutions. Thispaper presents common and interesting methods used.Privacy concerns have been one of the bigger obstacles forimplementing a widespread adoption of these solutions; despitethis fact, developed countries like those inside the EU and theUK have established a deadline for the implementation ofsmart meters in the whole country, whereas USA governmentstill struggles with the acceptance of this solution by itscitizens.The implementation of security over these approachesalong with fine-grained energy monitoring would lead to abetter public agreement of these solutions and hence a fasteradoption of such approaches. This paper reveals a lack ofsecurity over these approaches with a real scenario.

  18. Radar-based hail detection

    Czech Academy of Sciences Publication Activity Database

    Skripniková, Kateřina; Řezáčová, Daniela

    2014-01-01

    Roč. 144, č. 1 (2014), s. 175-185 ISSN 0169-8095 R&D Projects: GA ČR(CZ) GAP209/11/2045; GA MŠk LD11044 Institutional support: RVO:68378289 Keywords : hail detection * weather radar * hail damage risk Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 2.844, year: 2014 http://www.sciencedirect.com/science/article/pii/S0169809513001804

  19. Domain similarity based orthology detection

    OpenAIRE

    Bitard-Feildel, Tristan; Kemena, Carsten; Greenwood, Jenny M; Bornberg-Bauer, Erich

    2015-01-01

    Background Orthologous protein detection software mostly uses pairwise comparisons of amino-acid sequences to assert whether two proteins are orthologous or not. Accordingly, when the number of sequences for comparison increases, the number of comparisons to compute grows in a quadratic order. A current challenge of bioinformatic research, especially when taking into account the increasing number of sequenced organisms available, is to make this ever-growing number of comparisons computationa...

  20. Approach for Assessing Human Intrusion into a Radwaste Repository

    International Nuclear Information System (INIS)

    Cho, Dong Keun; Kim, Jung Woo; Jeong, Jong Tae; Baik, Min Hoon

    2016-01-01

    An approach to assess human intrusion into radwaste repository resulting from future human actions was proposed based on the common principals, requirements, and recommendations from IAEA, ICRP, and OECD/NEA, with the assumption that the intrusion occurs after loss of knowledge of the hazardous nature of the disposal facility. At first, the essential boundary conditions were derived on the basis of international recommendations, followed by overall approach to deal with inadvertent human intrusion. The essential premises were derived on the basis of international recommendations, followed by overall approach to deal with inadvertent human intrusion. The procedure to derive protective measures was also explained with four steps regarding how to derive safety framework, general measures, potential measures, and eventual protective measures on the basis of stylized scenarios. It is expected that the approach proposed in this study will be effectively used to reduce the potential for and/or consequence of human intrusion during entire processes of realization of disposal facility.

  1. Audiovisual laughter detection based on temporal features

    NARCIS (Netherlands)

    Petridis, Stavros; Nijholt, Antinus; Nijholt, A.; Pantic, M.; Pantic, Maja; Poel, Mannes; Poel, M.; Hondorp, G.H.W.

    2008-01-01

    Previous research on automatic laughter detection has mainly been focused on audio-based detection. In this study we present an audiovisual approach to distinguishing laughter from speech based on temporal features and we show that the integration of audio and visual information leads to improved

  2. Protocol vulnerability detection based on network traffic analysis and binary reverse engineering.

    Science.gov (United States)

    Wen, Shameng; Meng, Qingkun; Feng, Chao; Tang, Chaojing

    2017-01-01

    Network protocol vulnerability detection plays an important role in many domains, including protocol security analysis, application security, and network intrusion detection. In this study, by analyzing the general fuzzing method of network protocols, we propose a novel approach that combines network traffic analysis with the binary reverse engineering method. For network traffic analysis, the block-based protocol description language is introduced to construct test scripts, while the binary reverse engineering method employs the genetic algorithm with a fitness function designed to focus on code coverage. This combination leads to a substantial improvement in fuzz testing for network protocols. We build a prototype system and use it to test several real-world network protocol implementations. The experimental results show that the proposed approach detects vulnerabilities more efficiently and effectively than general fuzzing methods such as SPIKE.

  3. Protocol vulnerability detection based on network traffic analysis and binary reverse engineering.

    Directory of Open Access Journals (Sweden)

    Shameng Wen

    Full Text Available Network protocol vulnerability detection plays an important role in many domains, including protocol security analysis, application security, and network intrusion detection. In this study, by analyzing the general fuzzing method of network protocols, we propose a novel approach that combines network traffic analysis with the binary reverse engineering method. For network traffic analysis, the block-based protocol description language is introduced to construct test scripts, while the binary reverse engineering method employs the genetic algorithm with a fitness function designed to focus on code coverage. This combination leads to a substantial improvement in fuzz testing for network protocols. We build a prototype system and use it to test several real-world network protocol implementations. The experimental results show that the proposed approach detects vulnerabilities more efficiently and effectively than general fuzzing methods such as SPIKE.

  4. Experiences with non-intrusive monitoring of distribution transformers based on the on-line frequency response

    Directory of Open Access Journals (Sweden)

    Eduardo Gomez Luna

    2015-01-01

    Full Text Available The following article presents the results obtained in experiences that use the Impulse Frequency Response Analysis (IFRA method with a transformer in service. The IFRA method has been implemented in order to transform the transient signals to the frequency domain using Discrete Fourier Transform (DFT. However, it can be considered that the DFT is not the most suitable tool for this type of analysis, since, by definition, this tool is useful for processing stationary signals. Taking that into consideration, the analysis of transient signals could be hypothetically improved by using continuous wavelet transform (CWT, given their variable time/frequency resolution. The analysis of transient signals in Wavelet domain has improved the repeatability of the frequency response curves, as it has been ob-served in experimental results. The proposed on-line IFRA method, based on Wavelet transform, was validated under load and no-load conditions on a 150 kVA three-phase transformer 13200/225 Volts, in the Campus of the Universidad del Valle, Cali, Colombia.

  5. Laser-based optical detection of explosives

    CERN Document Server

    Pellegrino, Paul M; Farrell, Mikella E

    2015-01-01

    Laser-Based Optical Detection of Explosives offers a comprehensive review of past, present, and emerging laser-based methods for the detection of a variety of explosives. This book: Considers laser propagation safety and explains standard test material preparation for standoff optical-based detection system evaluation Explores explosives detection using deep ultraviolet native fluorescence, Raman spectroscopy, laser-induced breakdown spectroscopy, reflectometry, and hyperspectral imaging Examines photodissociation followed by laser-induced fluorescence, photothermal methods, cavity-enhanced absorption spectrometry, and short-pulse laser-based techniques Describes the detection and recognition of explosives using terahertz-frequency spectroscopic techniques Each chapter is authored by a leading expert on the respective technology, and is structured to supply historical perspective, address current advantages and challenges, and discuss novel research and applications. Readers are left with an in-depth understa...

  6. Does location congruence matter? A field study on the effects of location-based advertising on perceived AD intrusiveness, relevance & value

    NARCIS (Netherlands)

    Hühn, A.E.; Khan, V.J.; Ketelaar, P.E.; Riet, J.P. van 't; Konig, R.P.; Rozendaal, E.; Batalas, N.; Markopoulos, P.

    2017-01-01

    We investigate the effect of location-congruent mobile messages on perceived intrusiveness, value, and relevance through a field experiment using the Experience Sampling Method (ESM). We developed a mobile application for undergraduate students, featuring campus news and information concerning class

  7. SQL injection detection system

    OpenAIRE

    Vargonas, Vytautas

    2017-01-01

    SQL injection detection system Programmers do not always ensure security of developed systems. That is why it is important to look for solutions outside being reliant on developers. In this work SQL injection detection system is proposed. The system analyzes HTTP request parameters and detects intrusions. It is based on unsupervised machine learning. Trained by regular request data system detects outlier user parameters. Since training is not reliant on previous knowledge of SQL injections, t...

  8. QOS and Control-Theoretic Techniques for Intrusion Tolerance

    National Research Council Canada - National Science Library

    Ye, Nong

    2004-01-01

    ...), even in the face of intrusions. This report examines two host-based resources, a router and a web server, and presents simulated models of modifications that can be made to these resources to make them QoS-capable...

  9. Simulation of sea water intrusion in coastal aquifers

    Indian Academy of Sciences (India)

    dependent miscible flow and transport modelling approach for simulation of seawater intrusion in coastal aquifers. A nonlinear optimization-based simulation methodology was used in this study. Various steady state simulations are performed for a ...

  10. Daytime Water Detection Based on Sky Reflections

    Science.gov (United States)

    Rankin, Arturo; Matthies, Larry; Bellutta, Paolo

    2011-01-01

    A water body s surface can be modeled as a horizontal mirror. Water detection based on sky reflections and color variation are complementary. A reflection coefficient model suggests sky reflections dominate the color of water at ranges > 12 meters. Water detection based on sky reflections: (1) geometrically locates the pixel in the sky that is reflecting on a candidate water pixel on the ground (2) predicts if the ground pixel is water based on color similarity and local terrain features. Water detection has been integrated on XUVs.

  11. Photonic crystal fiber based antibody detection

    DEFF Research Database (Denmark)

    Duval, A; Lhoutellier, M; Jensen, J B

    2004-01-01

    An original approach for detecting labeled antibodies based on strong penetration photonic crystal fibers is introduced. The target antibody is immobilized inside the air-holes of a photonic crystal fiber and the detection is realized by the means of evanescent-wave fluorescence spectroscopy...

  12. Collaborative regression-based anatomical landmark detection

    International Nuclear Information System (INIS)

    Gao, Yaozong; Shen, Dinggang

    2015-01-01

    Anatomical landmark detection plays an important role in medical image analysis, e.g. for registration, segmentation and quantitative analysis. Among the various existing methods for landmark detection, regression-based methods have recently attracted much attention due to their robustness and efficiency. In these methods, landmarks are localised through voting from all image voxels, which is completely different from the classification-based methods that use voxel-wise classification to detect landmarks. Despite their robustness, the accuracy of regression-based landmark detection methods is often limited due to (1) the inclusion of uninformative image voxels in the voting procedure, and (2) the lack of effective ways to incorporate inter-landmark spatial dependency into the detection step. In this paper, we propose a collaborative landmark detection framework to address these limitations. The concept of collaboration is reflected in two aspects. (1) Multi-resolution collaboration. A multi-resolution strategy is proposed to hierarchically localise landmarks by gradually excluding uninformative votes from faraway voxels. Moreover, for informative voxels near the landmark, a spherical sampling strategy is also designed at the training stage to improve their prediction accuracy. (2) Inter-landmark collaboration. A confidence-based landmark detection strategy is proposed to improve the detection accuracy of ‘difficult-to-detect’ landmarks by using spatial guidance from ‘easy-to-detect’ landmarks. To evaluate our method, we conducted experiments extensively on three datasets for detecting prostate landmarks and head and neck landmarks in computed tomography images, and also dental landmarks in cone beam computed tomography images. The results show the effectiveness of our collaborative landmark detection framework in improving landmark detection accuracy, compared to other state-of-the-art methods. (paper)

  13. Power Consumption Based Android Malware Detection

    Directory of Open Access Journals (Sweden)

    Hongyu Yang

    2016-01-01

    Full Text Available In order to solve the problem that Android platform’s sand-box mechanism prevents security protection software from accessing effective information to detect malware, this paper proposes a malicious software detection method based on power consumption. Firstly, the mobile battery consumption status information was obtained, and the Gaussian mixture model (GMM was built by using Mel frequency cepstral coefficients (MFCC. Then, the GMM was used to analyze power consumption; malicious software can be classified and detected through classification processing. Experiment results demonstrate that the function of an application and its power consumption have a close relationship, and our method can detect some typical malicious application software accurately.

  14. A Proposal for Kelly CriterionBased Lossy Network Compression

    Science.gov (United States)

    2016-03-01

    detection applications. Most of these applications only send alerts to the central analysis servers. These alerts do not provide the forensic capability...based intrusion detection systems. These systems tend to examine the indi- vidual system’s audit logs looking for intrusive activity. The notable

  15. SODA-IIoT4Factory: Blockchain to keep the A.I. of your Intrusion Detection System up-to-date

    OpenAIRE

    Planchon , Frederic; Costa , Fernand; Nicaise , Vincent; Bouzerna , Nabil

    2017-01-01

    International audience; Co-designed with FPC Ingénierie, SODA-IIoT4Factory offers a secure way to update CyPRES rule engines & cyber security/attack models.CyPRES is an intelligent IDS that strengthens industrial information systems. It learns then verifies the operation and behaviour of the system to the lowest level of detail. It detects the first signs of attacks before damage is incurred.

  16. Semi-non-intrusive objective intelligibility measure using spatial filtering in hearing aids

    DEFF Research Database (Denmark)

    Sørensen, Charlotte; Boldt, Jesper Bünsow; Gran, Frederik

    2016-01-01

    -intrusive metrics have not been able to achieve acceptable intelligibility predictions. This paper presents a new semi-non-intrusive intelligibility measure based on an existing intrusive measure, STOI, where an estimate of the clean speech is extracted using spatial filtering in the hearing aid. The results......Reliable non-intrusive online assessment of speech intelligibility can play a key role for the functioning of hearing aids, e.g. as guidance for adjusting the hearing aid settings to the environment. While existing intrusive metrics can provide a precise and reliable measure, the current non...

  17. VISION BASED OBSTACLE DETECTION IN UAV IMAGING

    Directory of Open Access Journals (Sweden)

    S. Badrloo

    2017-08-01

    Full Text Available Detecting and preventing incidence with obstacles is crucial in UAV navigation and control. Most of the common obstacle detection techniques are currently sensor-based. Small UAVs are not able to carry obstacle detection sensors such as radar; therefore, vision-based methods are considered, which can be divided into stereo-based and mono-based techniques. Mono-based methods are classified into two groups: Foreground-background separation, and brain-inspired methods. Brain-inspired methods are highly efficient in obstacle detection; hence, this research aims to detect obstacles using brain-inspired techniques, which try to enlarge the obstacle by approaching it. A recent research in this field, has concentrated on matching the SIFT points along with, SIFT size-ratio factor and area-ratio of convex hulls in two consecutive frames to detect obstacles. This method is not able to distinguish between near and far obstacles or the obstacles in complex environment, and is sensitive to wrong matched points. In order to solve the above mentioned problems, this research calculates the dist-ratio of matched points. Then, each and every point is investigated for Distinguishing between far and close obstacles. The results demonstrated the high efficiency of the proposed method in complex environments.

  18. Pedestrian detection based on redundant wavelet transform

    Science.gov (United States)

    Huang, Lin; Ji, Liping; Hu, Ping; Yang, Tiejun

    2016-10-01

    Intelligent video surveillance is to analysis video or image sequences captured by a fixed or mobile surveillance camera, including moving object detection, segmentation and recognition. By using it, we can be notified immediately in an abnormal situation. Pedestrian detection plays an important role in an intelligent video surveillance system, and it is also a key technology in the field of intelligent vehicle. So pedestrian detection has very vital significance in traffic management optimization, security early warn and abnormal behavior detection. Generally, pedestrian detection can be summarized as: first to estimate moving areas; then to extract features of region of interest; finally to classify using a classifier. Redundant wavelet transform (RWT) overcomes the deficiency of shift variant of discrete wavelet transform, and it has better performance in motion estimation when compared to discrete wavelet transform. Addressing the problem of the detection of multi-pedestrian with different speed, we present an algorithm of pedestrian detection based on motion estimation using RWT, combining histogram of oriented gradients (HOG) and support vector machine (SVM). Firstly, three intensities of movement (IoM) are estimated using RWT and the corresponding areas are segmented. According to the different IoM, a region proposal (RP) is generated. Then, the features of a RP is extracted using HOG. Finally, the features are fed into a SVM trained by pedestrian databases and the final detection results are gained. Experiments show that the proposed algorithm can detect pedestrians accurately and efficiently.

  19. Power Consumption Based Android Malware Detection

    OpenAIRE

    Hongyu Yang; Ruiwen Tang

    2016-01-01

    In order to solve the problem that Android platform’s sand-box mechanism prevents security protection software from accessing effective information to detect malware, this paper proposes a malicious software detection method based on power consumption. Firstly, the mobile battery consumption status information was obtained, and the Gaussian mixture model (GMM) was built by using Mel frequency cepstral coefficients (MFCC). Then, the GMM was used to analyze power consumption; malicious software...

  20. Plagiarism Detection Based on SCAM Algorithm

    DEFF Research Database (Denmark)

    Anzelmi, Daniele; Carlone, Domenico; Rizzello, Fabio

    2011-01-01

    Plagiarism is a complex problem and considered one of the biggest in publishing of scientific, engineering and other types of documents. Plagiarism has also increased with the widespread use of the Internet as large amount of digital data is available. Plagiarism is not just direct copy but also...... paraphrasing, rewording, adapting parts, missing references or wrong citations. This makes the problem more difficult to handle adequately. Plagiarism detection techniques are applied by making a distinction between natural and programming languages. Our proposed detection process is based on natural language...... document. Our plagiarism detection system, like many Information Retrieval systems, is evaluated with metrics of precision and recall....

  1. Development of a Layered Conditional Random Field Based ...

    African Journals Online (AJOL)

    PROF. OLIVER OSUAGWA

    2014-12-01

    Dec 1, 2014 ... The recent denial of service attacks on major Internet sites has shown that no open ..... of a single record, which further degrades attack detection accuracy. ... distributed intrusion detection framework based on mobile agents.

  2. From intrusive to oscillating thoughts.

    Science.gov (United States)

    Peirce, Anne Griswold

    2007-10-01

    This paper focused on the possibility that intrusive thoughts (ITs) are a form of an evolutionary, adaptive, and complex strategy to prepare for and resolve stressful life events through schema formation. Intrusive thoughts have been studied in relation to individual conditions, such as traumatic stress disorder and obsessive-compulsive disorder. They have also been documented in the average person experiencing everyday stress. In many descriptions of thought intrusion, it is accompanied by thought suppression. Several theories have been put forth to describe ITs, although none provides a satisfactory explanation as to whether ITs are a normal process, a normal process gone astray, or a sign of pathology. There is also no consistent view of the role that thought suppression plays in the process. I propose that thought intrusion and thought suppression may be better understood by examining them together as a complex and adaptive mechanism capable of escalating in times of need. The ability of a biological mechanism to scale up in times of need is one hallmark of a complex and adaptive system. Other hallmarks of complexity, including self-similarity across scales, sensitivity to initial conditions, presence of feedback loops, and system oscillation, are also discussed in this article. Finally, I propose that thought intrusion and thought suppression are better described together as an oscillatory cycle.

  3. Saltwater intrusion monitoring in Florida

    Science.gov (United States)

    Prinos, Scott T.

    2016-01-01

    Florida's communities are largely dependent on freshwater from groundwater aquifers. Existing saltwater in the aquifers, or seawater that intrudes parts of the aquifers that were fresh, can make the water unusable without additional processing. The quality of Florida's saltwater intrusion monitoring networks varies. In Miami-Dade and Broward Counties, for example, there is a well-designed network with recently constructed short open-interval monitoring wells that bracket the saltwater interface in the Biscayne aquifer. Geochemical analyses of water samples from the network help scientists evaluate pathways of saltwater intrusion and movement of the saltwater interface. Geophysical measurements, collected in these counties, aid the mapping of the saltwater interface and the design of monitoring networks. In comparison, deficiencies in the Collier County monitoring network include the positioning of monitoring wells, reliance on wells with long open intervals that when sampled might provide questionable results, and the inability of existing analyses to differentiate between multiple pathways of saltwater intrusion. A state-wide saltwater intrusion monitoring network is being planned; the planned network could improve saltwater intrusion monitoring by adopting the applicable strategies of the networks of Miami-Dade and Broward Counties, and by addressing deficiencies such as those described for the Collier County network.

  4. Seizure detection algorithms based on EMG signals

    DEFF Research Database (Denmark)

    Conradsen, Isa

    Background: the currently used non-invasive seizure detection methods are not reliable. Muscle fibers are directly connected to the nerves, whereby electric signals are generated during activity. Therefore, an alarm system on electromyography (EMG) signals is a theoretical possibility. Objective...... on the amplitude of the signal. The other algorithm was based on information of the signal in the frequency domain, and it focused on synchronisation of the electrical activity in a single muscle during the seizure. Results: The amplitude-based algorithm reliably detected seizures in 2 of the patients, while...... the frequency-based algorithm was efficient for detecting the seizures in the third patient. Conclusion: Our results suggest that EMG signals could be used to develop an automatic seizuredetection system. However, different patients might require different types of algorithms /approaches....

  5. Image denoising based on noise detection

    Science.gov (United States)

    Jiang, Yuanxiang; Yuan, Rui; Sun, Yuqiu; Tian, Jinwen

    2018-03-01

    Because of the noise points in the images, any operation of denoising would change the original information of non-noise pixel. A noise detection algorithm based on fractional calculus was proposed to denoise in this paper. Convolution of the image was made to gain direction gradient masks firstly. Then, the mean gray was calculated to obtain the gradient detection maps. Logical product was made to acquire noise position image next. Comparisons in the visual effect and evaluation parameters after processing, the results of experiment showed that the denoising algorithms based on noise were better than that of traditional methods in both subjective and objective aspects.

  6. Improved biosensor-based detection system

    DEFF Research Database (Denmark)

    2015-01-01

    Described is a new biosensor-based detection system for effector compounds, useful for in vivo applications in e.g. screening and selecting of cells which produce a small molecule effector compound or which take up a small molecule effector compound from its environment. The detection system...... comprises a protein or RNA-based biosensor for the effector compound which indirectly regulates the expression of a reporter gene via two hybrid proteins, providing for fewer false signals or less 'noise', tuning of sensitivity or other advantages over conventional systems where the biosensor directly...

  7. Acoustic emission intrusion detector

    International Nuclear Information System (INIS)

    Carver, D.W.

    1978-01-01

    In order to improve the security of handling special nuclear materials at the Oak Ridge Y-12 Plant, a sensitive acoustic emission detector has been developed that will detect forcible entry through block or tile walls, concrete floors, or concrete/steel vault walls. A small, low-powered processor was designed to convert the output from a sensitive, crystal-type acoustic transducer to an alarm relay signal for use with a supervised alarm loop. The unit may be used to detect forcible entry through concrete, steel, block, tile, and/or glass

  8. Water Detection Based on Object Reflections

    Science.gov (United States)

    Rankin, Arturo L.; Matthies, Larry H.

    2012-01-01

    Water bodies are challenging terrain hazards for terrestrial unmanned ground vehicles (UGVs) for several reasons. Traversing through deep water bodies could cause costly damage to the electronics of UGVs. Additionally, a UGV that is either broken down due to water damage or becomes stuck in a water body during an autonomous operation will require rescue, potentially drawing critical resources away from the primary operation and increasing the operation cost. Thus, robust water detection is a critical perception requirement for UGV autonomous navigation. One of the properties useful for detecting still water bodies is that their surface acts as a horizontal mirror at high incidence angles. Still water bodies in wide-open areas can be detected by geometrically locating the exact pixels in the sky that are reflecting on candidate water pixels on the ground, predicting if ground pixels are water based on color similarity to the sky and local terrain features. But in cluttered areas where reflections of objects in the background dominate the appearance of the surface of still water bodies, detection based on sky reflections is of marginal value. Specifically, this software attempts to solve the problem of detecting still water bodies on cross-country terrain in cluttered areas at low cost.

  9. Study of Ground water Groundwater Potentiality and Sea Water Intrusion Along along the Coastal Plain, Wadi Thuwal, KSA- A Case Study Based on DC Resistivity

    Directory of Open Access Journals (Sweden)

    Mansour A. Al-Garni

    2010-12-01

    Full Text Available The present study mainly aims to outline zones that have groundwater potentiality with good quality and those which are affected by sea water intrusion. The electrical resistivity data were acquired over an area of about 170 km2 of a coastal plain, Wadi Thuwal, which is bounded by the Red Sea in the west and the volcanic hills in the east.  In such an area, resistivity measurements, using n-layering model, reveal generally reveal a wide range of resistivity values which do not reflect the reality. Hence, the statistical analysis has to be involved to overcome this problem and to make the final interpretation reliable.  In our case, the n-layer models were modified to another statistical geoelectric models (SLM, consisting of  a number of layers equivalent to the stratigraphic layering beneath each VES site. The modified models were used to outline the depth to the bed rock, groundwater accumulation zones and  water table as well as to define the effect of sea water intrusion through the study area. Check alignment above

  10. 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.

  11. A software tool for network intrusion detection

    CSIR Research Space (South Africa)

    Van der Walt, C

    2012-10-01

    Full Text Available ? Online services include internet banking, e-commerce, video streaming, Gmail ? Data services include Dropbox, Google Docs, Google Drive ? Threats: hacking, Denial of Service (DoS) attacks ? Victims of DoS attacks include Yahoo, eBay, e-trade, CNN...S attacks use the TCP protocol ? SYN flood is the most commonly-used TCP attack ? Exploits the limitation of the three-way hand shake , that maintains half-open connections for a certain time period ? Neptune - SYN flood denial of service on one or more...

  12. Intrusion Detection in Bluetooth Enabled Mobile Phones

    CSIR Research Space (South Africa)

    Nair, Kishor Krishnan

    2015-11-23

    Full Text Available . Bluetooth Logging Agent (BLA) is a mechanism that has been developed for this purpose. It alleviates the current security issues by making the users aware of their incoming Bluetooth connections and gives them an option to either accept or reject...

  13. Intrusion detection and monitoring for wireless networks.

    Energy Technology Data Exchange (ETDEWEB)

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

    2005-11-01

    Wireless computer networks are increasing exponentially around the world. They are being implemented in both the unlicensed radio frequency (RF) spectrum (IEEE 802.11a/b/g) and the licensed spectrum (e.g., Firetide [1] and Motorola Canopy [2]). Wireless networks operating in the unlicensed spectrum are by far the most popular wireless computer networks in existence. The open (i.e., proprietary) nature of the IEEE 802.11 protocols and the availability of ''free'' RF spectrum have encouraged many producers of enterprise and common off-the-shelf (COTS) computer networking equipment to jump into the wireless arena. Competition between these companies has driven down the price of 802.11 wireless networking equipment and has improved user experiences with such equipment. The end result has been an increased adoption of the equipment by businesses and consumers, the establishment of the Wi-Fi Alliance [3], and widespread use of the Alliance's ''Wi-Fi'' moniker to describe these networks. Consumers use 802.11 equipment at home to reduce the burden of running wires in existing construction, facilitate the sharing of broadband Internet services with roommates or neighbors, and increase their range of ''connectedness''. Private businesses and government entities (at all levels) are deploying wireless networks to reduce wiring costs, increase employee mobility, enable non-employees to access the Internet, and create an added revenue stream to their existing business models (coffee houses, airports, hotels, etc.). Municipalities (Philadelphia; San Francisco; Grand Haven, MI) are deploying wireless networks so they can bring broadband Internet access to places lacking such access; offer limited-speed broadband access to impoverished communities; offer broadband in places, such as marinas and state parks, that are passed over by traditional broadband providers; and provide themselves with higher quality, more complete network coverage for use by emergency responders and other municipal agencies. In short, these Wi-Fi networks are being deployed everywhere. Much thought has been and is being put into evaluating cost-benefit analyses of wired vs. wireless networks and issues such as how to effectively cover an office building or municipality, how to efficiently manage a large network of wireless access points (APs), and how to save money by replacing an Internet service provider (ISP) with 802.11 technology. In comparison, very little thought and money are being focused on wireless security and monitoring for security purposes.

  14. Intrusion Detection using Open Source Tools

    OpenAIRE

    Jack TIMOFTE

    2008-01-01

    We have witnessed in the recent years that open source tools have gained popularity among all types of users, from individuals or small businesses to large organizations and enterprises. In this paper we will present three open source IDS tools: OSSEC, Prelude and SNORT.

  15. Cloud Intrusion Detection and Repair (CIDAR)

    Science.gov (United States)

    2016-02-01

    form for VLC , Swftools-png2swf, Swftools-jpeg2swf, Dillo and GIMP. The superscript indicates the bit width of each expression atom. “sext(v, w... challenges in input rectification is the need to deal with nested fields. In general, input formats are in tree structures containing arbitrarily...length indicator constraints is challeng - ing, because of the presence of nested fields in hierarchical input format. For example, an integer field may

  16. Nanomaterials based biosensors for cancer biomarker detection

    International Nuclear Information System (INIS)

    Malhotra, Bansi D; Kumar, Saurabh; Pandey, Chandra Mouli

    2016-01-01

    Biosensors have enormous potential to contribute to the evolution of new molecular diagnostic techniques for patients suffering with cancerous diseases. A major obstacle preventing faster development of biosensors pertains to the fact that cancer is a highly complex set of diseases. The oncologists currently rely on a few biomarkers and histological characterization of tumors. Some of the signatures include epigenetic and genetic markers, protein profiles, changes in gene expression, and post-translational modifications of proteins. These molecular signatures offer new opportunities for development of biosensors for cancer detection. In this context, conducting paper has recently been found to play an important role towards the fabrication of a biosensor for cancer biomarker detection. In this paper we will focus on results of some of the recent studies obtained in our laboratories relating to fabrication and application of nanomaterial modified paper based biosensors for cancer biomarker detection. (paper)

  17. Intrusive luxation of 60 permanent incisors

    DEFF Research Database (Denmark)

    Tsilingaridis, Georgios; Malmgren, Barbro; Andreasen, Jens O

    2012-01-01

    Intrusive luxation in the permanent dentition is an uncommon injury but it is considered one of the most severe types of dental trauma because of the risk for damage to the periodontal ligament, pulp and alveolar bone. Management of intrusive luxation in the permanent dentition is controversial....... The purpose of this study was to evaluate pulp survival and periodontal healing in intrusive luxated permanent teeth in relation to treatment alternatives, degree of intrusion and root development....

  18. Water Detection Based on Color Variation

    Science.gov (United States)

    Rankin, Arturo L.

    2012-01-01

    This software has been designed to detect water bodies that are out in the open on cross-country terrain at close range (out to 30 meters), using imagery acquired from a stereo pair of color cameras mounted on a terrestrial, unmanned ground vehicle (UGV). This detector exploits the fact that the color variation across water bodies is generally larger and more uniform than that of other naturally occurring types of terrain, such as soil and vegetation. Non-traversable water bodies, such as large puddles, ponds, and lakes, are detected based on color variation, image intensity variance, image intensity gradient, size, and shape. At ranges beyond 20 meters, water bodies out in the open can be indirectly detected by detecting reflections of the sky below the horizon in color imagery. But at closer range, the color coming out of a water body dominates sky reflections, and the water cue from sky reflections is of marginal use. Since there may be times during UGV autonomous navigation when a water body does not come into a perception system s field of view until it is at close range, the ability to detect water bodies at close range is critical. Factors that influence the perceived color of a water body at close range are the amount and type of sediment in the water, the water s depth, and the angle of incidence to the water body. Developing a single model of the mixture ratio of light reflected off the water surface (to the camera) to light coming out of the water body (to the camera) for all water bodies would be fairly difficult. Instead, this software detects close water bodies based on local terrain features and the natural, uniform change in color that occurs across the surface from the leading edge to the trailing edge.

  19. Skeleton-Based Abnormal Gait Detection

    Directory of Open Access Journals (Sweden)

    Trong-Nguyen Nguyen

    2016-10-01

    Full Text Available Human gait analysis plays an important role in musculoskeletal disorder diagnosis. Detecting anomalies in human walking, such as shuffling gait, stiff leg or unsteady gait, can be difficult if the prior knowledge of such a gait pattern is not available. We propose an approach for detecting abnormal human gait based on a normal gait model. Instead of employing the color image, silhouette, or spatio-temporal volume, our model is created based on human joint positions (skeleton in time series. We decompose each sequence of normal gait images into gait cycles. Each human instant posture is represented by a feature vector which describes relationships between pairs of bone joints located in the lower body. Such vectors are then converted into codewords using a clustering technique. The normal human gait model is created based on multiple sequences of codewords corresponding to different gait cycles. In the detection stage, a gait cycle with normality likelihood below a threshold, which is determined automatically in the training step, is assumed as an anomaly. The experimental results on both marker-based mocap data and Kinect skeleton show that our method is very promising in distinguishing normal and abnormal gaits with an overall accuracy of 90.12%.

  20. Ionizing particle detection based on phononic crystals

    Energy Technology Data Exchange (ETDEWEB)

    Aly, Arafa H., E-mail: arafa16@yahoo.com, E-mail: arafa.hussien@science.bsu.edu.eg; Mehaney, Ahmed; Eissa, Mostafa F. [Physics Department, Faculty of Science, Beni-Suef University, Beni-Suef (Egypt)

    2015-08-14

    Most conventional radiation detectors are based on electronic or photon collections. In this work, we introduce a new and novel type of ionizing particle detector based on phonon collection. Helium ion radiation treats tumors with better precision. There are nine known isotopes of helium, but only helium-3 and helium-4 are stable. Helium-4 is formed in fusion reactor technology and in enormous quantities during Big Bang nucleo-synthesis. In this study, we introduce a technique for helium-4 ion detection (sensing) based on the innovative properties of the new composite materials known as phononic crystals (PnCs). PnCs can provide an easy and cheap technique for ion detection compared with conventional methods. PnC structures commonly consist of a periodic array of two or more materials with different elastic properties. The two materials are polymethyl-methacrylate and polyethylene polymers. The calculations showed that the energies lost to target phonons are maximized at 1 keV helium-4 ion energy. There is a correlation between the total phonon energies and the transmittance of PnC structures. The maximum transmission for phonons due to the passage of helium-4 ions was found in the case of making polyethylene as a first layer in the PnC structure. Therefore, the concept of ion detection based on PnC structure is achievable.

  1. An FPGA-Based People Detection System

    Directory of Open Access Journals (Sweden)

    James J. Clark

    2005-05-01

    Full Text Available This paper presents an FPGA-based system for detecting people from video. The system is designed to use JPEG-compressed frames from a network camera. Unlike previous approaches that use techniques such as background subtraction and motion detection, we use a machine-learning-based approach to train an accurate detector. We address the hardware design challenges involved in implementing such a detector, along with JPEG decompression, on an FPGA. We also present an algorithm that efficiently combines JPEG decompression with the detection process. This algorithm carries out the inverse DCT step of JPEG decompression only partially. Therefore, it is computationally more efficient and simpler to implement, and it takes up less space on the chip than the full inverse DCT algorithm. The system is demonstrated on an automated video surveillance application and the performance of both hardware and software implementations is analyzed. The results show that the system can detect people accurately at a rate of about 2.5 frames per second on a Virtex-II 2V1000 using a MicroBlaze processor running at 75 MHz, communicating with dedicated hardware over FSL links.

  2. IMAGE ANALYSIS BASED ON EDGE DETECTION TECHNIQUES

    Institute of Scientific and Technical Information of China (English)

    纳瑟; 刘重庆

    2002-01-01

    A method that incorporates edge detection technique, Markov Random field (MRF), watershed segmentation and merging techniques was presented for performing image segmentation and edge detection tasks. It first applies edge detection technique to obtain a Difference In Strength (DIS) map. An initial segmented result is obtained based on K-means clustering technique and the minimum distance. Then the region process is modeled by MRF to obtain an image that contains different intensity regions. The gradient values are calculated and then the watershed technique is used. DIS calculation is used for each pixel to define all the edges (weak or strong) in the image. The DIS map is obtained. This help as priority knowledge to know the possibility of the region segmentation by the next step (MRF), which gives an image that has all the edges and regions information. In MRF model,gray level l, at pixel location i, in an image X, depends on the gray levels of neighboring pixels. The segmentation results are improved by using watershed algorithm. After all pixels of the segmented regions are processed, a map of primitive region with edges is generated. The edge map is obtained using a merge process based on averaged intensity mean values. A common edge detectors that work on (MRF) segmented image are used and the results are compared. The segmentation and edge detection result is one closed boundary per actual region in the image.

  3. Machine learning approach to detect intruders in database based on hexplet data structure

    Directory of Open Access Journals (Sweden)

    Saad M. Darwish

    2016-09-01

    Full Text Available Most of valuable information resources for any organization are stored in the database; it is a serious subject to protect this information against intruders. However, conventional security mechanisms are not designed to detect anomalous actions of database users. An intrusion detection system (IDS, delivers an extra layer of security that cannot be guaranteed by built-in security tools, is the ideal solution to defend databases from intruders. This paper suggests an anomaly detection approach that summarizes the raw transactional SQL queries into a compact data structure called hexplet, which can model normal database access behavior (abstract the user's profile and recognize impostors specifically tailored for role-based access control (RBAC database system. This hexplet lets us to preserve the correlation among SQL statements in the same transaction by exploiting the information in the transaction-log entry with the aim to improve detection accuracy specially those inside the organization and behave strange behavior. The model utilizes naive Bayes classifier (NBC as the simplest supervised learning technique for creating profiles and evaluating the legitimacy of a transaction. Experimental results show the performance of the proposed model in the term of detection rate.

  4. Research on Abnormal Detection Based on Improved Combination of K - means and SVDD

    Science.gov (United States)

    Hao, Xiaohong; Zhang, Xiaofeng

    2018-01-01

    In order to improve the efficiency of network intrusion detection and reduce the false alarm rate, this paper proposes an anomaly detection algorithm based on improved K-means and SVDD. The algorithm first uses the improved K-means algorithm to cluster the training samples of each class, so that each class is independent and compact in class; Then, according to the training samples, the SVDD algorithm is used to construct the minimum superspheres. The subordinate relationship of the samples is determined by calculating the distance of the minimum superspheres constructed by SVDD. If the test sample is less than the center of the hypersphere, the test sample belongs to this class, otherwise it does not belong to this class, after several comparisons, the final test of the effective detection of the test sample.In this paper, we use KDD CUP99 data set to simulate the proposed anomaly detection algorithm. The results show that the algorithm has high detection rate and low false alarm rate, which is an effective network security protection method.

  5. Tensor-based spatiotemporal saliency detection

    Science.gov (United States)

    Dou, Hao; Li, Bin; Deng, Qianqian; Zhang, LiRui; Pan, Zhihong; Tian, Jinwen

    2018-03-01

    This paper proposes an effective tensor-based spatiotemporal saliency computation model for saliency detection in videos. First, we construct the tensor representation of video frames. Then, the spatiotemporal saliency can be directly computed by the tensor distance between different tensors, which can preserve the complete temporal and spatial structure information of object in the spatiotemporal domain. Experimental results demonstrate that our method can achieve encouraging performance in comparison with the state-of-the-art methods.

  6. A Novel Immune-Inspired Shellcode Detection Algorithm Based on Hyperellipsoid Detectors

    Directory of Open Access Journals (Sweden)

    Tianliang Lu

    2018-01-01

    Full Text Available Shellcodes are machine language codes injected into target programs in the form of network packets or malformed files. Shellcodes can trigger buffer overflow vulnerability and execute malicious instructions. Signature matching technology used by antivirus software or intrusion detection system has low detection rate for unknown or polymorphic shellcodes; to solve such problem, an immune-inspired shellcode detection algorithm was proposed, named ISDA. Static analysis and dynamic analysis were both applied. The shellcodes were disassembled to assembly instructions during static analysis and, for dynamic analysis, the API function sequences of shellcodes were obtained by simulation execution to get the behavioral features of polymorphic shellcodes. The extracted features of shellcodes were encoded to antigens based on n-gram model. Immature detectors become mature after immune tolerance based on negative selection algorithm. To improve nonself space coverage rate, the immune detectors were encoded to hyperellipsoids. To generate better antibody offspring, the detectors were optimized through clonal selection algorithm with genetic mutation. Finally, shellcode samples were collected and tested, and result shows that the proposed method has higher detection accuracy for both nonencoded and polymorphic shellcodes.

  7. Automated image based prominent nucleoli detection.

    Science.gov (United States)

    Yap, Choon K; Kalaw, Emarene M; Singh, Malay; Chong, Kian T; Giron, Danilo M; Huang, Chao-Hui; Cheng, Li; Law, Yan N; Lee, Hwee Kuan

    2015-01-01

    Nucleolar changes in cancer cells are one of the cytologic features important to the tumor pathologist in cancer assessments of tissue biopsies. However, inter-observer variability and the manual approach to this work hamper the accuracy of the assessment by pathologists. In this paper, we propose a computational method for prominent nucleoli pattern detection. Thirty-five hematoxylin and eosin stained images were acquired from prostate cancer, breast cancer, renal clear cell cancer and renal papillary cell cancer tissues. Prostate cancer images were used for the development of a computer-based automated prominent nucleoli pattern detector built on a cascade farm. An ensemble of approximately 1000 cascades was constructed by permuting different combinations of classifiers such as support vector machines, eXclusive component analysis, boosting, and logistic regression. The output of cascades was then combined using the RankBoost algorithm. The output of our prominent nucleoli pattern detector is a ranked set of detected image patches of patterns of prominent nucleoli. The mean number of detected prominent nucleoli patterns in the top 100 ranked detected objects was 58 in the prostate cancer dataset, 68 in the breast cancer dataset, 86 in the renal clear cell cancer dataset, and 76 in the renal papillary cell cancer dataset. The proposed cascade farm performs twice as good as the use of a single cascade proposed in the seminal paper by Viola and Jones. For comparison, a naive algorithm that randomly chooses a pixel as a nucleoli pattern would detect five correct patterns in the first 100 ranked objects. Detection of sparse nucleoli patterns in a large background of highly variable tissue patterns is a difficult challenge our method has overcome. This study developed an accurate prominent nucleoli pattern detector with the potential to be used in the clinical settings.

  8. Automated image based prominent nucleoli detection

    Directory of Open Access Journals (Sweden)

    Choon K Yap

    2015-01-01

    Full Text Available Introduction: Nucleolar changes in cancer cells are one of the cytologic features important to the tumor pathologist in cancer assessments of tissue biopsies. However, inter-observer variability and the manual approach to this work hamper the accuracy of the assessment by pathologists. In this paper, we propose a computational method for prominent nucleoli pattern detection. Materials and Methods: Thirty-five hematoxylin and eosin stained images were acquired from prostate cancer, breast cancer, renal clear cell cancer and renal papillary cell cancer tissues. Prostate cancer images were used for the development of a computer-based automated prominent nucleoli pattern detector built on a cascade farm. An ensemble of approximately 1000 cascades was constructed by permuting different combinations of classifiers such as support vector machines, eXclusive component analysis, boosting, and logistic regression. The output of cascades was then combined using the RankBoost algorithm. The output of our prominent nucleoli pattern detector is a ranked set of detected image patches of patterns of prominent nucleoli. Results: The mean number of detected prominent nucleoli patterns in the top 100 ranked detected objects was 58 in the prostate cancer dataset, 68 in the breast cancer dataset, 86 in the renal clear cell cancer dataset, and 76 in the renal papillary cell cancer dataset. The proposed cascade farm performs twice as good as the use of a single cascade proposed in the seminal paper by Viola and Jones. For comparison, a naive algorithm that randomly chooses a pixel as a nucleoli pattern would detect five correct patterns in the first 100 ranked objects. Conclusions: Detection of sparse nucleoli patterns in a large background of highly variable tissue patterns is a difficult challenge our method has overcome. This study developed an accurate prominent nucleoli pattern detector with the potential to be used in the clinical settings.

  9. Advances in neutron based bulk explosive detection

    Science.gov (United States)

    Gozani, Tsahi; Strellis, Dan

    2007-08-01

    Neutron based explosive inspection systems can detect a wide variety of national security threats. The inspection is founded on the detection of characteristic gamma rays emitted as the result of neutron interactions with materials. Generally these are gamma rays resulting from thermal neutron capture and inelastic scattering reactions in most materials and fast and thermal neutron fission in fissile (e.g.235U and 239Pu) and fertile (e.g.238U) materials. Cars or trucks laden with explosives, drugs, chemical agents and hazardous materials can be detected. Cargo material classification via its main elements and nuclear materials detection can also be accomplished with such neutron based platforms, when appropriate neutron sources, gamma ray spectroscopy, neutron detectors and suitable decision algorithms are employed. Neutron based techniques can be used in a variety of scenarios and operational modes. They can be used as stand alones for complete scan of objects such as vehicles, or for spot-checks to clear (or validate) alarms indicated by another inspection system such as X-ray radiography. The technologies developed over the last two decades are now being implemented with good results. Further advances have been made over the last few years that increase the sensitivity, applicability and robustness of these systems. The advances range from the synchronous inspection of two sides of vehicles, increasing throughput and sensitivity and reducing imparted dose to the inspected object and its occupants (if any), to taking advantage of the neutron kinetic behavior of cargo to remove systematic errors, reducing background effects and improving fast neutron signals.

  10. 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.

  11. QRS detection based ECG quality assessment

    International Nuclear Information System (INIS)

    Hayn, Dieter; Jammerbund, Bernhard; Schreier, Günter

    2012-01-01

    Although immediate feedback concerning ECG signal quality during recording is useful, up to now not much literature describing quality measures is available. We have implemented and evaluated four ECG quality measures. Empty lead criterion (A), spike detection criterion (B) and lead crossing point criterion (C) were calculated from basic signal properties. Measure D quantified the robustness of QRS detection when applied to the signal. An advanced Matlab-based algorithm combining all four measures and a simplified algorithm for Android platforms, excluding measure D, were developed. Both algorithms were evaluated by taking part in the Computing in Cardiology Challenge 2011. Each measure's accuracy and computing time was evaluated separately. During the challenge, the advanced algorithm correctly classified 93.3% of the ECGs in the training-set and 91.6 % in the test-set. Scores for the simplified algorithm were 0.834 in event 2 and 0.873 in event 3. Computing time for measure D was almost five times higher than for other measures. Required accuracy levels depend on the application and are related to computing time. While our simplified algorithm may be accurate for real-time feedback during ECG self-recordings, QRS detection based measures can further increase the performance if sufficient computing power is available. (paper)

  12. Monitoring CO2 Intrusion in shallow aquifer using complex electrical methods and a novel CO2 sensitive Lidar-based sensor

    Science.gov (United States)

    Leger, E.; Dafflon, B.; Thorpe, M.; Kreitinger, A.; Laura, D.; Haivala, J.; Peterson, J.; Spangler, L.; Hubbard, S. S.

    2016-12-01

    While subsurface storage of CO2 in geological formations offers significant potential to mitigate atmospheric greenhouse gasses, approaches are needed to monitor the efficacy of the strategy as well as possible negative consequences, such as leakage of CO2 or brine into groundwater or release of fugitive gaseous CO2. Groundwater leakages can cause subsequent reactions that may also be deleterious. For example, a release of dissolved CO2 into shallow groundwatersystems can decrease groundwater pH which can potentiallymobilize naturally occurring trace metals and ions. In this perspective, detecting and assessing potential leak requires development of novel monitoring techniques.We present the results of using surface electrical resistivity tomography (ERT) and a novel CO2 sensitive Lidar-based sensor to monitor a controlled CO2 release at the ZeroEmission Research and Technology Center (Bozeman, Montana). Soil temperature and moisture sensors, wellbore water quality measurements as well as chamber-based CO2 flux measurements were used in addition to the ERT and a novel Lidar-based sensor to detect and assess potential leakage into groundwater, vadose zone and atmosphere. The three-week release wascarried out in the vadose and the saturated zones. Well sampling of pH and conductivity and surface CO2 fluxes and concentrations measurements were acquired during the release and are compared with complex electricalresistivity time-lapse measurements. The novel Lidar-based image of the CO2 plume were compared to chamber-based CO2 flux and concentration measurements. While a continuous increase in subsurface ERT and above ground CO2 was documented, joint analysis of the above and below ground data revealed distinct transport behavior in the vadose and saturated zones. Two type of transport were observed, one in the vadoze zone, monitored by CO2 flux chamber and ERT, and the other one in the saturated zone, were ERT and wellsampling were carried. The experiment suggests how

  13. Intrusive Images in Psychological Disorders

    OpenAIRE

    Brewin, Chris R.; Gregory, James D.; Lipton, Michelle; Burgess, Neil

    2010-01-01

    Involuntary images and visual memories are prominent in many types of psychopathology. Patients with posttraumatic stress disorder, other anxiety disorders, depression, eating disorders, and psychosis frequently report repeated visual intrusions corresponding to a small number of real or imaginary events, usually extremely vivid, detailed, and with highly distressing content. Both memory and imagery appear to rely on common networks involving medial prefrontal regions, posterior regions in th...

  14. Reset Tree-Based Optical Fault Detection

    Directory of Open Access Journals (Sweden)

    Howon Kim

    2013-05-01

    Full Text Available In this paper, we present a new reset tree-based scheme to protect cryptographic hardware against optical fault injection attacks. As one of the most powerful invasive attacks on cryptographic hardware, optical fault attacks cause semiconductors to misbehave by injecting high-energy light into a decapped integrated circuit. The contaminated result from the affected chip is then used to reveal secret information, such as a key, from the cryptographic hardware. Since the advent of such attacks, various countermeasures have been proposed. Although most of these countermeasures are strong, there is still the possibility of attack. In this paper, we present a novel optical fault detection scheme that utilizes the buffers on a circuit’s reset signal tree as a fault detection sensor. To evaluate our proposal, we model radiation-induced currents into circuit components and perform a SPICE simulation. The proposed scheme is expected to be used as a supplemental security tool.

  15. Frequency Based Fault Detection in Wind Turbines

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Stoustrup, Jakob

    2014-01-01

    In order to obtain lower cost of energy for wind turbines fault detection and accommodation is important. Expensive condition monitoring systems are often used to monitor the condition of rotating and vibrating system parts. One example is the gearbox in a wind turbine. This system is operated...... in parallel to the control system, using different computers and additional often expensive sensors. In this paper a simple filter based algorithm is proposed to detect changes in a resonance frequency in a system, exemplified with faults resulting in changes in the resonance frequency in the wind turbine...... gearbox. Only the generator speed measurement which is available in even simple wind turbine control systems is used as input. Consequently this proposed scheme does not need additional sensors and computers for monitoring the condition of the wind gearbox. The scheme is evaluated on a wide-spread wind...

  16. Biological intrusion barriers for large-volume waste-disposal sites

    International Nuclear Information System (INIS)

    Hakonson, T.E.; Cline, J.F.; Rickard, W.H.

    1982-01-01

    intrusion of plants and animals into shallow land burial sites with subsequent mobilization of toxic and radiotoxic materials has occured. Based on recent pathway modeling studies, such intrusions can contribute to the dose received by man. This paper describes past work on developing biological intrusion barrier systems for application to large volume waste site stabilization. State-of-the-art concepts employing rock and chemical barriers are discussed relative to long term serviceability and cost of application. The interaction of bio-intrusion barrier systems with other processes affecting trench cover stability are discussed to ensure that trench cover designs minimize the potential dose to man. 3 figures, 6 tables

  17. 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.

  18. The Effects of Saltwater Intrusion to Flood Mitigation Project

    Science.gov (United States)

    Azida Abu Bakar, Azinoor; Khairudin Khalil, Muhammad

    2018-03-01

    The objective of this study is to determine the effects of saltwater intrusion to flood mitigation project located in the flood plains in the district of Muar, Johor. Based on the studies and designs carried out, one of the effective flood mitigation options identified is the Kampung Tanjung Olak bypass and Kampung Belemang bypass at the lower reaches of Sungai Muar. But, the construction of the Kampung Belemang and Tanjung Olak bypass, while speeding up flood discharges, may also increase saltwater intrusion during drought low flows. Establishing the dynamics of flooding, including replicating the existing situation and the performance with prospective flood mitigation interventions, is most effectively accomplished using computer-based modelling tools. The finding of this study shows that to overcome the problem, a barrage should be constructed at Sungai Muar to solve the saltwater intrusion and low yield problem of the river.

  19. Global contrast based salient region detection

    KAUST Repository

    Cheng, Ming-Ming

    2011-08-25

    Reliable estimation of visual saliency allows appropriate processing of images without prior knowledge of their contents, and thus remains an important step in many computer vision tasks including image segmentation, object recognition, and adaptive compression. We propose a regional contrast based saliency extraction algorithm, which simultaneously evaluates global contrast differences and spatial coherence. The proposed algorithm is simple, efficient, and yields full resolution saliency maps. Our algorithm consistently outperformed existing saliency detection methods, yielding higher precision and better recall rates, when evaluated using one of the largest publicly available data sets. We also demonstrate how the extracted saliency map can be used to create high quality segmentation masks for subsequent image processing.

  20. Global contrast based salient region detection

    KAUST Repository

    Cheng, Ming-Ming; Zhang, Guo-Xin; Mitra, Niloy J.; Huang, Xiaolei; Hu, Shi-Min

    2011-01-01

    Reliable estimation of visual saliency allows appropriate processing of images without prior knowledge of their contents, and thus remains an important step in many computer vision tasks including image segmentation, object recognition, and adaptive compression. We propose a regional contrast based saliency extraction algorithm, which simultaneously evaluates global contrast differences and spatial coherence. The proposed algorithm is simple, efficient, and yields full resolution saliency maps. Our algorithm consistently outperformed existing saliency detection methods, yielding higher precision and better recall rates, when evaluated using one of the largest publicly available data sets. We also demonstrate how the extracted saliency map can be used to create high quality segmentation masks for subsequent image processing.

  1. 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...

  2. Waveguide-Based Biosensors for Pathogen Detection

    Directory of Open Access Journals (Sweden)

    Nile Hartman

    2009-07-01

    Full Text Available Optical phenomena such as fluorescence, phosphorescence, polarization, interference and non-linearity have been extensively used for biosensing applications. Optical waveguides (both planar and fiber-optic are comprised of a material with high permittivity/high refractive index surrounded on all sides by materials with lower refractive indices, such as a substrate and the media to be sensed. This arrangement allows coupled light to propagate through the high refractive index waveguide by total internal reflection and generates an electromagnetic wave—the evanescent field—whose amplitude decreases exponentially as the distance from the surface increases. Excitation of fluorophores within the evanescent wave allows for sensitive detection while minimizing background fluorescence from complex, “dirty” biological samples. In this review, we will describe the basic principles, advantages and disadvantages of planar optical waveguide-based biodetection technologies. This discussion will include already commercialized technologies (e.g., Corning’s EPIC® Ô, SRU Biosystems’ BIND™, Zeptosense®, etc. and new technologies that are under research and development. We will also review differing assay approaches for the detection of various biomolecules, as well as the thin-film coatings that are often required for waveguide functionalization and effective detection. Finally, we will discuss reverse-symmetry waveguides, resonant waveguide grating sensors and metal-clad leaky waveguides as alternative signal transducers in optical biosensing.

  3. DNA & Protein detection based on microbead agglutination

    KAUST Repository

    Kodzius, Rimantas

    2012-06-06

    We report a simple and rapid room temperature assay for point-of-care (POC) testing that is based on specific agglutination. Agglutination tests are based on aggregation of microparticles in the presence of a specific analyte thus enabling the macroscopic observation. Agglutination-based tests are most often used to explore the antibody-antigen reactions. Agglutination has been used for mode protein assays using a biotin/streptavidin two-component system, as well as a hybridization based two-component assay; however, as our work shows, two-component systems are prone to self-termination of the linking analyte and thus have a lower sensitivity. Three component systems have also been used with DNA hybridization, as in our work; however, their assay requires 48 hours for incubation, while our assay is performed in 5 minutes making it a real candidate for POC testing. We demonstrate three assays: a two-component biotin/streptavidin assay, a three-component hybridization assay using single stranded DNA (ssDNA) molecules and a stepped three-component hybridization assay. The comparison of these three assays shows our simple stepped three-component agglutination assay to be rapid at room temperature and more sensitive than the two-component version by an order of magnitude. An agglutination assay was also performed in a PDMS microfluidic chip where agglutinated beads were trapped by filter columns for easy observation. We developed a rapid (5 minute) room temperature assay, which is based on microbead agglutination. Our three-component assay solves the linker self-termination issue allowing an order of magnitude increase in sensitivity over two–component assays. Our stepped version of the three-component assay solves the issue with probe site saturation thus enabling a wider range of detection. Detection of the agglutinated beads with the naked eye by trapping in microfluidic channels has been shown.

  4. 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

  5. Attribute and topology based change detection in a constellation of previously detected objects

    Science.gov (United States)

    Paglieroni, David W.; Beer, Reginald N.

    2016-01-19

    A system that applies attribute and topology based change detection to networks of objects that were detected on previous scans of a structure, roadway, or area of interest. The attributes capture properties or characteristics of the previously detected objects, such as location, time of detection, size, elongation, orientation, etc. The topology of the network of previously detected objects is maintained in a constellation database that stores attributes of previously detected objects and implicitly captures the geometrical structure of the network. A change detection system detects change by comparing the attributes and topology of new objects detected on the latest scan to the constellation database of previously detected objects.

  6. 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.

  7. A buffer overflow detection based on inequalities solution

    International Nuclear Information System (INIS)

    Xu Guoai; Zhang Miao; Yang Yixian

    2007-01-01

    A new buffer overflow detection model based on Inequalities Solution was designed, which is based on analyzing disadvantage of the old buffer overflow detection technique and successfully converting buffer overflow detection to Inequalities Solution. The new model can conquer the disadvantage of the old technique and improve efficiency of buffer overflow detection. (authors)

  8. Intrusion scenarios in fusion waste disposal sites

    International Nuclear Information System (INIS)

    Zucchetti, M.; Zucchetti, M.; Rocco, P.

    1998-01-01

    Results of analyses on human intrusions into repositories of fusion radioactive waste are presented. The main topics are: duration of the institutional control, occurrence of intrusion, intrusion scenarios, acceptable risk limits and probabilistic data. Application to fusion waste repositories is implemented with a computational model: wells drilling is considered as the possible scenario. Doses and risks to intruder for different SEAFP-2 cases turn out to be very small. No intervention to reduce the hazard is necessary. (authors)

  9. Intrusion scenarios in fusion waste disposal sites

    Energy Technology Data Exchange (ETDEWEB)

    Zucchetti, M. [European Commission, JRC, Institute for Advanced Material, Ispra, Vatican City State, Holy See (Italy); Zucchetti, M.; Rocco, P. [Energetics Dept., Polytechnic of Turin (Italy)

    1998-07-01

    Results of analyses on human intrusions into repositories of fusion radioactive waste are presented. The main topics are: duration of the institutional control, occurrence of intrusion, intrusion scenarios, acceptable risk limits and probabilistic data. Application to fusion waste repositories is implemented with a computational model: wells drilling is considered as the possible scenario. Doses and risks to intruder for different SEAFP-2 cases turn out to be very small. No intervention to reduce the hazard is necessary. (authors)

  10. Comic image understanding based on polygon detection

    Science.gov (United States)

    Li, Luyuan; Wang, Yongtao; Tang, Zhi; Liu, Dong

    2013-01-01

    Comic image understanding aims to automatically decompose scanned comic page images into storyboards and then identify the reading order of them, which is the key technique to produce digital comic documents that are suitable for reading on mobile devices. In this paper, we propose a novel comic image understanding method based on polygon detection. First, we segment a comic page images into storyboards by finding the polygonal enclosing box of each storyboard. Then, each storyboard can be represented by a polygon, and the reading order of them is determined by analyzing the relative geometric relationship between each pair of polygons. The proposed method is tested on 2000 comic images from ten printed comic series, and the experimental results demonstrate that it works well on different types of comic images.

  11. Low complexity pixel-based halftone detection

    Science.gov (United States)

    Ok, Jiheon; Han, Seong Wook; Jarno, Mielikainen; Lee, Chulhee

    2011-10-01

    With the rapid advances of the internet and other multimedia technologies, the digital document market has been growing steadily. Since most digital images use halftone technologies, quality degradation occurs when one tries to scan and reprint them. Therefore, it is necessary to extract the halftone areas to produce high quality printing. In this paper, we propose a low complexity pixel-based halftone detection algorithm. For each pixel, we considered a surrounding block. If the block contained any flat background regions, text, thin lines, or continuous or non-homogeneous regions, the pixel was classified as a non-halftone pixel. After excluding those non-halftone pixels, the remaining pixels were considered to be halftone pixels. Finally, documents were classified as pictures or photo documents by calculating the halftone pixel ratio. The proposed algorithm proved to be memory-efficient and required low computation costs. The proposed algorithm was easily implemented using GPU.

  12. Aptamer Based Microsphere Biosensor for Thrombin Detection

    Directory of Open Access Journals (Sweden)

    Xudong Fan

    2006-08-01

    Full Text Available We have developed an optical microsphere resonator biosensor using aptamer asreceptor for the measurement of the important biomolecule thrombin. The sphere surface ismodified with anti-thrombin aptamer, which has excellent binding affinity and selectivityfor thrombin. Binding of the thrombin at the sphere surface is monitored by the spectralposition of the microsphere’s whispering gallery mode resonances. A detection limit on theorder of 1 NIH Unit/mL is demonstrated. Control experiments with non-aptameroligonucleotide and BSA are also carried out to confirm the specific binding betweenaptamer and thrombin. We expect that this demonstration will lead to the development ofhighly sensitive biomarker sensors based on aptamer with lower cost and higher throughputthan current technology.

  13. Detecting Soft Errors in Stencil based Computations

    Energy Technology Data Exchange (ETDEWEB)

    Sharma, V. [Univ. of Utah, Salt Lake City, UT (United States); Gopalkrishnan, G. [Univ. of Utah, Salt Lake City, UT (United States); Bronevetsky, G. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2015-05-06

    Given the growing emphasis on system resilience, it is important to develop software-level error detectors that help trap hardware-level faults with reasonable accuracy while minimizing false alarms as well as the performance overhead introduced. We present a technique that approaches this idea by taking stencil computations as our target, and synthesizing detectors based on machine learning. In particular, we employ linear regression to generate computationally inexpensive models which form the basis for error detection. Our technique has been incorporated into a new open-source library called SORREL. In addition to reporting encouraging experimental results, we demonstrate techniques that help reduce the size of training data. We also discuss the efficacy of various detectors synthesized, as well as our future plans.

  14. [Analysis of intrusion errors in free recall].

    Science.gov (United States)

    Diesfeldt, H F A

    2017-06-01

    Extra-list intrusion errors during five trials of the eight-word list-learning task of the Amsterdam Dementia Screening Test (ADST) were investigated in 823 consecutive psychogeriatric patients (87.1% suffering from major neurocognitive disorder). Almost half of the participants (45.9%) produced one or more intrusion errors on the verbal recall test. Correct responses were lower when subjects made intrusion errors, but learning slopes did not differ between subjects who committed intrusion errors and those who did not so. Bivariate regression analyses revealed that participants who committed intrusion errors were more deficient on measures of eight-word recognition memory, delayed visual recognition and tests of executive control (the Behavioral Dyscontrol Scale and the ADST-Graphical Sequences as measures of response inhibition). Using hierarchical multiple regression, only free recall and delayed visual recognition retained an independent effect in the association with intrusion errors, such that deficient scores on tests of episodic memory were sufficient to explain the occurrence of intrusion errors. Measures of inhibitory control did not add significantly to the explanation of intrusion errors in free recall, which makes insufficient strength of memory traces rather than a primary deficit in inhibition the preferred account for intrusion errors in free recall.

  15. An international perspective on Facebook intrusion.

    Science.gov (United States)

    Błachnio, Agata; Przepiorka, Aneta; Benvenuti, Martina; Cannata, Davide; Ciobanu, Adela Magdalena; Senol-Durak, Emre; Durak, Mithat; Giannakos, Michail N; Mazzoni, Elvis; Pappas, Ilias O; Popa, Camelia; Seidman, Gwendolyn; Yu, Shu; Wu, Anise M S; Ben-Ezra, Menachem

    2016-08-30

    Facebook has become one of the most popular social networking websites in the world. The main aim of the study was to present an international comparison of Facebook intrusion and Internet penetration while examining possible gender differences. The study consisted of 2589 participants from eight countries: China, Greece, Israel, Italy, Poland, Romania, Turkey, USA. Facebook intrusion and Internet penetration were taken into consideration. In this study the relationship between Facebook intrusion and Internet penetration was demonstrated. Facebook intrusion was slightly negatively related to Internet penetration in each country. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  16. 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.

  17. Single electron based binary multipliers with overflow detection ...

    African Journals Online (AJOL)

    electron based device. Multipliers with overflow detection based on serial and parallel prefix computation algorithm are elaborately discussed analytically and designed. The overflow detection circuits works in parallel with a simplified multiplier to ...

  18. Trauma Films, Information Processing, and Intrusive Memory Development

    Science.gov (United States)

    Holmes, Emily A.; Brewin, Chris R.; Hennessy, Richard G.

    2004-01-01

    Three experiments indexed the effect of various concurrent tasks, while watching a traumatic film, on intrusive memory development. Hypotheses were based on the dual-representation theory of posttraumatic stress disorder (C. R. Brewin, T. Dalgleish, & S. Joseph, 1996). Nonclinical participants viewed a trauma film under various encoding conditions…

  19. Note on Studying Change Point of LRD Traffic Based on Li's Detection of DDoS Flood Attacking

    Directory of Open Access Journals (Sweden)

    Zhengmin Xia

    2010-01-01

    Full Text Available Distributed denial-of-service (DDoS flood attacks remain great threats to the Internet. To ensure network usability and reliability, accurate detection of these attacks is critical. Based on Li's work on DDoS flood attack detection, we propose a DDoS detection method by monitoring the Hurst variation of long-range dependant traffic. Specifically, we use an autoregressive system to estimate the Hurst parameter of normal traffic. If the actual Hurst parameter varies significantly from the estimation, we assume that DDoS attack happens. Meanwhile, we propose two methods to determine the change point of Hurst parameter that indicates the occurrence of DDoS attacks. The detection rate associated with one method and false alarm rate for the other method are also derived. The test results on DARPA intrusion detection evaluation data show that the proposed approaches can achieve better detection performance than some well-known self-similarity-based detection methods.

  20. Non-intrusive optical study of gas and its exchange in human maxillary sinuses

    Science.gov (United States)

    Persson, L.; Andersson, M.; Svensson, T.; Cassel-Engquist, M.; Svanberg, K.; Svanberg, S.

    2007-07-01

    We demonstrate a novel non-intrusive technique based on tunable diode laser absorption spectroscopy to investigate human maxillary sinuses in vivo. The technique relies on the fact that free gases have much sharper absorption features (typical a few GHz) than the surrounding tissue. Molecular oxygen was detected at 760 nm. Volunteers have been investigated by injecting near-infrared light fibre-optically in contact with the palate inside the mouth. The multiply scattered light was detected externally by a handheld probe on and around the cheek bone. A significant signal difference in oxygen imprint was observed when comparing volunteers with widely different anamnesis regarding maxillary sinus status. Control measurements through the hand and through the cheek below the cheekbone were also performed to investigate any possible oxygen offset in the setup. These provided a consistently non-detectable signal level. The passages between the nasal cavity and the maxillary sinuses were also non-intrusively optically studied, to the best of our knowledge for the first time. These measurements provide information on the channel conductivity which may prove useful in facial sinus diagnostics. The results suggest that a clinical trial together with an ear-nose-throat (ENT) clinic should be carried out to investigate the clinical use of the new technique.

  1. Cellular telephone-based radiation detection instrument

    Science.gov (United States)

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

    2011-06-14

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

  2. Episodic intrusion, internal differentiation, and hydrothermal alteration of the miocene tatoosh intrusive suite south of Mount Rainier, Washington

    Science.gov (United States)

    du Bray, E.A.; Bacon, C.R.; John, D.A.; Wooden, J.L.; Mazdab, F.K.

    2011-01-01

    The Miocene Tatoosh intrusive suite south of Mount Rainier is composed of three broadly granodioritic plutons that are manifestations of ancestral Cascades arc magmatism. Tatoosh intrusive suite plutons have individually diagnostic characteristics, including texture, mineralogy, and geochemistry, and apparently lack internal contacts. New ion-microprobe U-Pb zircon ages indicate crystallization of the Stevens pluton ca. 19.2 Ma, Reflection-Pyramid pluton ca. 18.5 Ma, and Nisqually pluton ca. 17.5 Ma. The Stevens pluton includes rare, statistically distinct ca. 20.1 Ma zircon antecrysts. Wide-ranging zircon rare earth element (REE), Hf, U, and Th concentrations suggest late crystallization from variably evolved residual liquids. Zircon Eu/Eu*-Hf covariation is distinct for each of the Reflection-Pyramid, Nisqually, and Stevens plutons. Although most Tatoosh intrusive suite rocks have been affected by weak hydrothermal alteration, and sparse mineralized veins cut some of these rocks, significant base or precious metal mineralization is absent. At the time of shallow emplacement, each of these magma bodies was largely homogeneous in bulk composition and petrographic features, but, prior to final solidification, each of the Tatoosh intrusive suite plutons developed internal compositional variation. Geochemical and petrographic trends within each pluton are most consistent with differential loss of residual melt, possibly represented by late aplite dikes or erupted as rhyolite, from crystal-rich magma. Crystal-rich magma that formed each pluton evidently accumulated in reservoirs below the present level of exposure and then intruded to a shallow depth. Assembled by episodic intrusion, the Tatoosh intrusive suite may be representative of midsized composite plutonic complexes beneath arc volcanoes. ?? 2011 Geological Society of America.

  3. Hydrodynamic modeling of the intrusion phenomenon in water distribution systems; Modelacion hidrodinamica del fenomeno de intrusion en tuberia de abastecimiento

    Energy Technology Data Exchange (ETDEWEB)

    Lopez-Jimenez, Petra Amparo; Mora-Rodriguez, Jose de Jesus; Perez-Garcia, Rafael; Martinez-Solano, F. Javier [Universidad Politecnica de Valencia (Spain)

    2008-10-15

    This paper describes a strategy for the hydrodynamic modeling of the pathogen intrusion phenomenon in water distribution systems by the combination of a breakage with a depression situation. This scenario will be modeled computationally and experimentally. The phenomenon to be represented by both simulations is the same: the entrance of an external volume into the circulation of a main volume, known as a pathogen intrusion, as long as the main volume is potable water. To this end, a prototype and a computational model based on Computational Fluid Dynamics (CFD) are used, which allow visualizing the fields of speeds and pressures in a simulated form. With the comparison of the results of both models, conclusions will be drawn on the detail of the studied pathogen intrusion phenomenon. [Spanish] En el presente documento se describe una estrategia de modelacion del fenomeno hidrodinamico de la intrusion patogena en redes de distribucion de agua por combinacion de una rotura con una situacion de depresion. Este escenario sera modelado computacional y experimentalmente. El fenomeno que se desea representar con ambas simulaciones es el mismo: la entrada de un caudal externo a una conduccion para la que circula un caudal principal, denominado intrusion patogena, siempre y cuando el caudal principal sea agua potable. Para ello se dispone de un prototipo y un modelo computacional basado en la Dinamica de Fluidos Computacional (DFC de aqui en adelante), que permite visualizar los campos de velocidades y presiones de forma simulada. Con la comparacion de los resultados de ambos modelos se extraeran conclusiones sobre el detalle del fenomeno de la intrusion patogena estudiado.

  4. USBcat - Towards an Intrusion Surveillance Toolset

    Directory of Open Access Journals (Sweden)

    Chris Chapman

    2014-10-01

    Full Text Available This paper identifies an intrusion surveillance framework which provides an analyst with the ability to investigate and monitor cyber-attacks in a covert manner. Where cyber-attacks are perpetrated for the purposes of espionage the ability to understand an adversary's techniques and objectives are an important element in network and computer security. With the appropriate toolset, security investigators would be permitted to perform both live and stealthy counter-intelligence operations by observing the behaviour and communications of the intruder. Subsequently a more complete picture of the attacker's identity, objectives, capabilities, and infiltration could be formulated than is possible with present technologies. This research focused on developing an extensible framework to permit the covert investigation of malware. Additionally, a Universal Serial Bus (USB Mass Storage Device (MSD based covert channel was designed to enable remote command and control of the framework. The work was validated through the design, implementation and testing of a toolset.

  5. Human intrusion: issues concerning its assessment

    International Nuclear Information System (INIS)

    Grimwood, P.D.; Smith, G.M.

    1989-01-01

    The potential significance of human intrusion in the performance assessment of radioactive waste repositories has been increasingly recognized in recent years. It is however an area of assessment in which subjective judgments dominate. This paper identifies some of the issues involved. These include regulatory criteria, scenario development, probability assignment, consequence assessment and measures to mitigate human intrusion

  6. Lagrangian based methods for coherent structure detection

    Energy Technology Data Exchange (ETDEWEB)

    Allshouse, Michael R., E-mail: mallshouse@chaos.utexas.edu [Center for Nonlinear Dynamics and Department of Physics, University of Texas at Austin, Austin, Texas 78712 (United States); Peacock, Thomas, E-mail: tomp@mit.edu [Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 (United States)

    2015-09-15

    There has been a proliferation in the development of Lagrangian analytical methods for detecting coherent structures in fluid flow transport, yielding a variety of qualitatively different approaches. We present a review of four approaches and demonstrate the utility of these methods via their application to the same sample analytic model, the canonical double-gyre flow, highlighting the pros and cons of each approach. Two of the methods, the geometric and probabilistic approaches, are well established and require velocity field data over the time interval of interest to identify particularly important material lines and surfaces, and influential regions, respectively. The other two approaches, implementing tools from cluster and braid theory, seek coherent structures based on limited trajectory data, attempting to partition the flow transport into distinct regions. All four of these approaches share the common trait that they are objective methods, meaning that their results do not depend on the frame of reference used. For each method, we also present a number of example applications ranging from blood flow and chemical reactions to ocean and atmospheric flows.

  7. Intelligent-based Structural Damage Detection Model

    International Nuclear Information System (INIS)

    Lee, Eric Wai Ming; Yu, K.F.

    2010-01-01

    This paper presents the application of a novel Artificial Neural Network (ANN) model for the diagnosis of structural damage. The ANN model, denoted as the GRNNFA, is a hybrid model combining the General Regression Neural Network Model (GRNN) and the Fuzzy ART (FA) model. It not only retains the important features of the GRNN and FA models (i.e. fast and stable network training and incremental growth of network structure) but also facilitates the removal of the noise embedded in the training samples. Structural damage alters the stiffness distribution of the structure and so as to change the natural frequencies and mode shapes of the system. The measured modal parameter changes due to a particular damage are treated as patterns for that damage. The proposed GRNNFA model was trained to learn those patterns in order to detect the possible damage location of the structure. Simulated data is employed to verify and illustrate the procedures of the proposed ANN-based damage diagnosis methodology. The results of this study have demonstrated the feasibility of applying the GRNNFA model to structural damage diagnosis even when the training samples were noise contaminated.

  8. Intelligent-based Structural Damage Detection Model

    Science.gov (United States)

    Lee, Eric Wai Ming; Yu, Kin Fung

    2010-05-01

    This paper presents the application of a novel Artificial Neural Network (ANN) model for the diagnosis of structural damage. The ANN model, denoted as the GRNNFA, is a hybrid model combining the General Regression Neural Network Model (GRNN) and the Fuzzy ART (FA) model. It not only retains the important features of the GRNN and FA models (i.e. fast and stable network training and incremental growth of network structure) but also facilitates the removal of the noise embedded in the training samples. Structural damage alters the stiffness distribution of the structure and so as to change the natural frequencies and mode shapes of the system. The measured modal parameter changes due to a particular damage are treated as patterns for that damage. The proposed GRNNFA model was trained to learn those patterns in order to detect the possible damage location of the structure. Simulated data is employed to verify and illustrate the procedures of the proposed ANN-based damage diagnosis methodology. The results of this study have demonstrated the feasibility of applying the GRNNFA model to structural damage diagnosis even when the training samples were noise contaminated.

  9. 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.

  10. Time to face it! Facebook intrusion and the implications for romantic jealousy and relationship satisfaction.

    Science.gov (United States)

    Elphinston, Rachel A; Noller, Patricia

    2011-11-01

    Young people's exposure to social network sites such as Facebook is increasing, along with the potential for such use to complicate romantic relationships. Yet, little is known about the overlaps between the online and offline worlds. We extended previous research by investigating the links between Facebook intrusion, jealousy in romantic relationships, and relationship outcomes in a sample of undergraduates currently in a romantic relationship. A Facebook Intrusion Questionnaire was developed based on key features of technological (behavioral) addictions. An eight-item Facebook Intrusion Questionnaire with a single-factor structure was supported; internal consistency was high. Facebook intrusion was linked to relationship dissatisfaction, via jealous cognitions and surveillance behaviors. The results highlight the possibility of high levels of Facebook intrusion spilling over into romantic relationships, resulting in problems such as jealousy and dissatisfaction. The results have implications for romantic relationships and for Facebook users in general.

  11. Salt Intrusion in the Tweed Estuary

    Science.gov (United States)

    Uncles, R. J.; Stephens, J. A.

    1996-09-01

    Results are presented from a 2-week field programme in the Tweed Estuary, U.K. Maximum values of the empirically based Estuarine Richardson Number, Ri E, occurred during neap tides, and minimum values occurred during spring tides. Estimated values of Ri Evaried between 0·3 and 2·3, suggesting the occurrence of partially mixed to stratified conditions, depending on tidal state and freshwater inflow. These relatively large values of Ri Ewere consistent with both observed strong salinity stratification and large salt fluxes due to vertical shear transport. Low values (0·5) values in the halocline. A velocity maximum occurred within the halocline during the early flood. Wave-like spatial oscillations of the halocline occurred on the ebb. The oscillation troughs were situated above deep holes located just down-estuary of the rail and old road bridges. There was an indication that the constricted flow between the bridges' arches resulted in enhanced mixing of near-surface waters and a thickening of the halocline. It is also possible that these wave-like structures were stationary, near-critical internal lee waves, triggered by the deep holes. Trapping of high-salinity waters occurred on the ebb. Saline pools were isolated within a deep hole or deeper section of bed by the falling halocline. When the salt wedge moved further down-estuary, the ' trapped ' waters were subjected to strongly ebbing, overlying freshwater, and were subsequently entrained and flushed. The salinity intrusion was a strong function of spring-neap tidal state and a weaker function of freshwater inflow. The estimated salinity intrusion varied from about 4·7 to 7·6 km during the fieldwork period. The strong dependence on tidal range followed from the comparable lengths of the tidal excursion and salinity intrusion. Long excursion lengths were also partly responsible for the short residence (or flushing) times and their strong dependence on spring-neap tidal state. For typical summer freshwater

  12. Vision Based Displacement Detection for Stabilized UAV Control on Cloud Server

    Directory of Open Access Journals (Sweden)

    Hyeok-June Jeong

    2016-01-01

    Full Text Available Nowadays, image processing solution is used in many fields such as traffic information systems and illegal intrusion detection systems. Now, to assist with the control of camera-equipped devices, appropriate image processing techniques are needed for moving rather than fixed observers. For achieving this goal, an algorithm should derive the desired results quickly and accurately; thus, this paper considers two characteristics: functional performance (reliability and temporal performance (efficiency. Reliability means how well the desired results can be achieved, and efficiency means how quickly the result can be calculated. This paper suggests an optimized real-time image algorithm based on the integration of the optical flow and Speeded-Up Robust Features (SURF algorithms. This algorithm determines horizontal or vertical movement of the camera and then extracts its displacement. The proposed algorithm can be used to stabilize an Unmanned Aerial Vehicle (UAV in situations where it is drifting due to inertia and external forces, like wind, in parallel. The proposed algorithm is efficient in achieving drift stabilization by movement detection; however, it is not appropriate for image processing in small UAVs. To solve this problem, this study proposes an image processing method that uses a high-performance computer.

  13. Nitric oxide in a diesel engine : laser-based detection and interpretation

    NARCIS (Netherlands)

    Stoffels, G.G.M.

    1999-01-01

    Nitric oxide (NO) is one of the most polluting components in the exhaust gases of a diesel engines. Therefore, knowledge of the time and place where it is produced during the combustion process is of interest to find a way to reduce diesel engine emissions. Non-intrusive optical diagnostics, based

  14. Dangerous gas detection based on infrared video

    Science.gov (United States)

    Ding, Kang; Hong, Hanyu; Huang, Likun

    2018-03-01

    As the gas leak infrared imaging detection technology has significant advantages of high efficiency and remote imaging detection, in order to enhance the detail perception of observers and equivalently improve the detection limit, we propose a new type of gas leak infrared image detection method, which combines background difference methods and multi-frame interval difference method. Compared to the traditional frame methods, the multi-frame interval difference method we proposed can extract a more complete target image. By fusing the background difference image and the multi-frame interval difference image, we can accumulate the information of infrared target image of the gas leak in many aspect. The experiment demonstrate that the completeness of the gas leakage trace information is enhanced significantly, and the real-time detection effect can be achieved.

  15. Improving Seroreactivity-Based Detection of Glioma

    Directory of Open Access Journals (Sweden)

    Nicole Ludwig

    2009-12-01

    Full Text Available Seroreactivity profiling emerges as valuable technique for minimal invasive cancer detection. Recently, we provided first evidence for the applicability of serum profiling of glioma using a limited number of immunogenic antigens. Here, we screened 57 glioma and 60 healthy sera for autoantibodies against 1827 Escherichia coli expressed clones, including 509 in-frame peptide sequences. By a linear support vector machine approach, we calculated mean specificity, sensitivity, and accuracy of 100 repetitive classifications. We were able to differentiate glioma sera from sera of the healthy controls with a specificity of 90.28%, a sensitivity of 87.31% and an accuracy of 88.84%. We were also able to differentiate World Health Organization grade IV glioma sera from healthy sera with a specificity of 98.45%, a sensitivity of 80.93%, and an accuracy of 92.88%. To rank the antigens according to their information content, we computed the area under the receiver operator characteristic curve value for each clone. Altogether, we found 46 immunogenic clones including 16 in-frame clones that were informative for the classification of glioma sera versus healthy sera. For the separation of glioblastoma versus healthy sera, we found 91 informative clones including 26 in-frame clones. The best-suited in-frame clone for the classification glioma sera versus healthy sera corresponded to the vimentin gene (VIM that was previously associated with glioma. In the future, autoantibody signatures in glioma not only may prove useful for diagnosis but also offer the prospect for a personalized immune-based therapy.

  16. Saline water intrusion toward groundwater: Issues and its control

    Directory of Open Access Journals (Sweden)

    Purnama S

    2012-10-01

    Full Text Available Nowadays, saline water pollution has been gaining its importance as the major issue around the world, especially in the urban coastal area. Saline water pollution has major impact on human life and livelihood. It ́s mainly a result from static fossil water and the dynamics of sea water intrusion. The problem of saline water pollution caused by seawater intrusion has been increasing since the beginning of urban population. The problem of sea water intrusion in the urban coastal area must be anticipated as soon as possible especially in the urban areas developed in coastal zones,. This review article aims to; (i analyze the distribution of saline water pollution on urban coastal area in Indonesia and (ii analyze some methods in controlling saline water pollution, especially due to seawater intrusion in urban coastal area. The strength and weakness of each method have been compared, including (a applying different pumping patterns, (b artificial recharge, (c extraction barrier, (d injection barrier and (e subsurface barrier. The best method has been selected considering its possible development in coastal areas of developing countries. The review is based considering the location of Semarang coastal area, Indonesia. The results have shown that artificial recharge and extraction barrier are the most suitable methods to be applied in the area.

  17. Igneous Intrusion Impacts on Waste Packages and Waste Forms

    International Nuclear Information System (INIS)

    P. Bernot

    2004-01-01

    The purpose of this model report is to assess the potential impacts of igneous intrusion on waste packages and waste forms in the emplacement drifts at the Yucca Mountain Repository. The model is based on conceptual models and includes an assessment of deleterious dynamic, thermal, hydrologic, and chemical impacts. This constitutes the waste package and waste form impacts submodel of the Total System Performance Assessment for the License Application (TSPA-LA) model assessing the impacts of a hypothetical igneous intrusion event on the repository total system performance. This submodel is carried out in accordance with Technical Work Plan for Waste Form Degradation Modeling, Testing, and Analyses in Support of SR and LA (BSC 2003a) and Total System Performance Assessment-License Application Methods and Approaches (BSC 2002a). The technical work plan is governed by the procedures of AP-SIII.10Q, Models. Any deviations from the technical work plan are documented in the TSPA-LA approach to implementing the models for waste package and waste form response during igneous intrusion is based on identification of damage zones. Zone 1 includes all emplacement drifts intruded by the basalt dike, and Zone 2 includes all other emplacement drifts in the repository that are not in Zone 1. This model report will document the following model: (1) Impacts of magma intrusion on the components of engineered barrier system (e.g., drip shields and cladding) of emplacement drifts in Zone 1, and the fate of waste forms. (2) Impacts of conducting magma heat and diffusing magma gases on the drip shields, waste packages, and cladding in the Zone 2 emplacement drifts adjacent to the intruded drifts. (3) Impacts of intrusion on Zone 1 in-drift thermal and geochemical environments, including seepage hydrochemistry. The scope of this model only includes impacts to the components stated above, and does not include impacts to other engineered barrier system (EBS) components such as the invert and

  18. Igneous Intrusion Impacts on Waste Packages and Waste Forms

    Energy Technology Data Exchange (ETDEWEB)

    P. Bernot

    2004-08-16

    The purpose of this model report is to assess the potential impacts of igneous intrusion on waste packages and waste forms in the emplacement drifts at the Yucca Mountain Repository. The model is based on conceptual models and includes an assessment of deleterious dynamic, thermal, hydrologic, and chemical impacts. This constitutes the waste package and waste form impacts submodel of the Total System Performance Assessment for the License Application (TSPA-LA) model assessing the impacts of a hypothetical igneous intrusion event on the repository total system performance. This submodel is carried out in accordance with Technical Work Plan for Waste Form Degradation Modeling, Testing, and Analyses in Support of SR and LA (BSC 2003a) and Total System Performance Assessment-License Application Methods and Approaches (BSC 2002a). The technical work plan is governed by the procedures of AP-SIII.10Q, Models. Any deviations from the technical work plan are documented in the TSPA-LA approach to implementing the models for waste package and waste form response during igneous intrusion is based on identification of damage zones. Zone 1 includes all emplacement drifts intruded by the basalt dike, and Zone 2 includes all other emplacement drifts in the repository that are not in Zone 1. This model report will document the following model: (1) Impacts of magma intrusion on the components of engineered barrier system (e.g., drip shields and cladding) of emplacement drifts in Zone 1, and the fate of waste forms. (2) Impacts of conducting magma heat and diffusing magma gases on the drip shields, waste packages, and cladding in the Zone 2 emplacement drifts adjacent to the intruded drifts. (3) Impacts of intrusion on Zone 1 in-drift thermal and geochemical environments, including seepage hydrochemistry. The scope of this model only includes impacts to the components stated above, and does not include impacts to other engineered barrier system (EBS) components such as the invert and

  19. QRS Detection Based on Improved Adaptive Threshold

    Directory of Open Access Journals (Sweden)

    Xuanyu Lu

    2018-01-01

    Full Text Available Cardiovascular disease is the first cause of death around the world. In accomplishing quick and accurate diagnosis, automatic electrocardiogram (ECG analysis algorithm plays an important role, whose first step is QRS detection. The threshold algorithm of QRS complex detection is known for its high-speed computation and minimized memory storage. In this mobile era, threshold algorithm can be easily transported into portable, wearable, and wireless ECG systems. However, the detection rate of the threshold algorithm still calls for improvement. An improved adaptive threshold algorithm for QRS detection is reported in this paper. The main steps of this algorithm are preprocessing, peak finding, and adaptive threshold QRS detecting. The detection rate is 99.41%, the sensitivity (Se is 99.72%, and the specificity (Sp is 99.69% on the MIT-BIH Arrhythmia database. A comparison is also made with two other algorithms, to prove our superiority. The suspicious abnormal area is shown at the end of the algorithm and RR-Lorenz plot drawn for doctors and cardiologists to use as aid for diagnosis.

  20. The Monitoring, Detection, Isolation and Assessment of Information Warfare Attacks Through Multi-Level, Multi-Scale System Modeling and Model Based Technology

    National Research Council Canada - National Science Library

    Ye, Nong

    2004-01-01

    With the goal of protecting computer and networked systems from various attacks, the following intrusion detection techniques were developed and tested using the 1998 and 2000 MIT Lincoln Lab Evaluation Data...

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

    Science.gov (United States)

    Seo, Jung Woo; Lee, Sang Jin

    2016-01-01

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

  2. Hybrid Intrusion Forecasting Framework for Early Warning System

    Science.gov (United States)

    Kim, Sehun; Shin, Seong-Jun; Kim, Hyunwoo; Kwon, Ki Hoon; Han, Younggoo

    Recently, cyber attacks have become a serious hindrance to the stability of Internet. These attacks exploit interconnectivity of networks, propagate in an instant, and have become more sophisticated and evolutionary. Traditional Internet security systems such as firewalls, IDS and IPS are limited in terms of detecting recent cyber attacks in advance as these systems respond to Internet attacks only after the attacks inflict serious damage. In this paper, we propose a hybrid intrusion forecasting system framework for an early warning system. The proposed system utilizes three types of forecasting methods: time-series analysis, probabilistic modeling, and data mining method. By combining these methods, it is possible to take advantage of the forecasting technique of each while overcoming their drawbacks. Experimental results show that the hybrid intrusion forecasting method outperforms each of three forecasting methods.

  3. Vision-based vehicle detection and tracking algorithm design

    Science.gov (United States)

    Hwang, Junyeon; Huh, Kunsoo; Lee, Donghwi

    2009-12-01

    The vision-based vehicle detection in front of an ego-vehicle is regarded as promising for driver assistance as well as for autonomous vehicle guidance. The feasibility of vehicle detection in a passenger car requires accurate and robust sensing performance. A multivehicle detection system based on stereo vision has been developed for better accuracy and robustness. This system utilizes morphological filter, feature detector, template matching, and epipolar constraint techniques in order to detect the corresponding pairs of vehicles. After the initial detection, the system executes the tracking algorithm for the vehicles. The proposed system can detect front vehicles such as the leading vehicle and side-lane vehicles. The position parameters of the vehicles located in front are obtained based on the detection information. The proposed vehicle detection system is implemented on a passenger car, and its performance is verified experimentally.

  4. Detection and response to unauthorized access to a communication device

    Science.gov (United States)

    Smith, Rhett; Gordon, Colin

    2015-09-08

    A communication gateway consistent with the present disclosure may detect unauthorized physical or electronic access and implement security actions in response thereto. A communication gateway may provide a communication path to an intelligent electronic device (IED) using an IED communications port configured to communicate with the IED. The communication gateway may include a physical intrusion detection port and a network port. The communication gateway may further include control logic configured to evaluate physical intrusion detection signal. The control logic may be configured to determine that the physical intrusion detection signal is indicative of an attempt to obtain unauthorized access to one of the communication gateway, the IED, and a device in communication with the gateway; and take a security action based upon the determination that the indication is indicative of the attempt to gain unauthorized access.

  5. Intrusion mechanics according to Burstone with the NiTi-SE-steel uprighting spring.

    Science.gov (United States)

    Sander, F G; Wichelhaus, A; Schiemann, C

    1996-08-01

    Intrusion mechanics according to Burstone can be regarded as a practicable method for the intrusion of incisors. 1. By applying the NiTi-SE-steel uprighting spring, relatively constant forces can be exerted over a large range of intrusion on both sides of the anterior tooth archwire. 2. By bending a 150 degrees tip-back bend or a curvature into the steel portion, the uprighting spring presented here is brought into the plastic range of the characteristic curve of force. 3. Application of sliding hooks on the intrusion spring permits readjustment for force transfer onto the anterior archwire. 4. Connecting the anterior archwire with the posterior elements by means of a steel ligature can be recommended only in some cases, because sagittally directed forces may be produced. 5. The adult patients presented showed an average intrusion of 0.6 mm/month, if a linear connection was presupposed. 6. An intrusive effect on the incisors could first be detected clinically after 6 to 8 weeks. 7. Application of a torque-key proves especially useful in controlling the incisor position during intrusion in order to avoid unnecessary radiography. 8. Actual prediction of the centre of resistance with the help of a cephalometric radiograph proved not to be feasible. 9. The calculated maximal intrusion of the mandibular incisors was 7 mm. 10. The torque-segmented archwire with crimped hooks and pseudoelastic springs between the molars and the crimped hooks proved very effective for retrusion and intrusion of maxillary incisors. The maxillary anterior teeth can be retruded by a total of 7 mm without readjustment. 11. Constant moments and forces could be transferred by applying preformed arch wires and segmented arch wires.

  6. Adaptive skin detection based on online training

    Science.gov (United States)

    Zhang, Ming; Tang, Liang; Zhou, Jie; Rong, Gang

    2007-11-01

    Skin is a widely used cue for porn image classification. Most conventional methods are off-line training schemes. They usually use a fixed boundary to segment skin regions in the images and are effective only in restricted conditions: e.g. good lightness and unique human race. This paper presents an adaptive online training scheme for skin detection which can handle these tough cases. In our approach, skin detection is considered as a classification problem on Gaussian mixture model. For each image, human face is detected and the face color is used to establish a primary estimation of skin color distribution. Then an adaptive online training algorithm is used to find the real boundary between skin color and background color in current image. Experimental results on 450 images showed that the proposed method is more robust in general situations than the conventional ones.

  7. Laser spot detection based on reaction diffusion

    Czech Academy of Sciences Publication Activity Database

    Vázquez-Otero, Alejandro; Khikhlukha, Danila; Solano-Altamirano, J. M.; Dormido, R.; Duro, N.

    2016-01-01

    Roč. 16, č. 3 (2016), s. 1-11, č. článku 315. ISSN 1424-8220 R&D Projects: GA MŠk EF15_008/0000162 Grant - others:ELI Beamlines(XE) CZ.02.1.01/0.0/0.0/15_008/0000162 Institutional support: RVO:68378271 Keywords : laser spot detection * laser beam detection * reaction diffusion models * Fitzhugh-Nagumo model * reaction diffusion computation * Turing patterns Subject RIV: BL - Plasma and Gas Discharge Physics OBOR OECD: Fluids and plasma physics (including surface physics) Impact factor: 2.677, year: 2016

  8. Alpha intrusion on ovenight polysomnogram

    Directory of Open Access Journals (Sweden)

    Nahapetian R

    2014-06-01

    Full Text Available No abstract available. Article truncated after 150 words. A 30 year-old Army veteran with a past medical history significant for chronic lumbar back pain stemming from a fall-from-height injury sustained in 2006 was referred to the sleep laboratory for evaluation of chronic fatigue and excessive daytime hypersomnolence. His Epworth sleepiness scale score was 16. He denied a history of snoring and witnessed apnea. Body Mass Index (BMI was 25.7 kg/m2. His main sleep related complaints were frequent nocturnal arousals, poor sleep quality, un-refreshing sleep, prolonged latency to sleep onset, and nightmares. An In-lab attended diagnostic polysomnogram was performed. Sleep efficiency was reduced (73% and overall arousal index was not significantly elevated (3.2 events/hour. The sleep study showed rapid eye movement (REM related sleep disordered breathing that did not meet diagnostic criteria for sleep apnea. There was no evidence for period limb movement disorder. However, the study was significant for alpha wave intrusion in stage N2 non-REM and stage ...

  9. The design method and research status of vehicle detection system based on geomagnetic detection principle

    Science.gov (United States)

    Lin, Y. H.; Bai, R.; Qian, Z. H.

    2018-03-01

    Vehicle detection systems are applied to obtain real-time information of vehicles, realize traffic control and reduce traffic pressure. This paper reviews geomagnetic sensors as well as the research status of the vehicle detection system. Presented in the paper are also our work on the vehicle detection system, including detection algorithms and experimental results. It is found that the GMR based vehicle detection system has a detection accuracy up to 98% with a high potential for application in the road traffic control area.

  10. Flow-Based Detection of DNS Tunnels

    NARCIS (Netherlands)

    Ellens, W.; Żuraniewski, P.; Sperotto, A.; Schotanus, H.; Mandjes, M.; Meeuwissen, E.

    2013-01-01

    DNS tunnels allow circumventing access and security policies in firewalled networks. Such a security breach can be misused for activities like free web browsing, but also for command & control traffic or cyber espionage, thus motivating the search for effective automated DNS tunnel detection

  11. Flow-based detection of DNS tunnels

    NARCIS (Netherlands)

    Ellens, W.; Zuraniewski, P.; Schotanus, H.; Mandjes, M.R.H.; Meeuwissen, E.; Doyen, Guillaume; Waldburger, Martin; Celeda, Pavel; Sperotto, Anna; Stiller, Burkhard

    DNS tunnels allow circumventing access and security policies in firewalled networks. Such a security breach can be misused for activities like free web browsing, but also for command & control traffic or cyber espionage, thus motivating the search for effective automated DNS tunnel detection

  12. Flow-based detection of DNS tunnels

    NARCIS (Netherlands)

    Ellens, W.; Zuraniewski, P.W.; Sperotto, A.; Schotanus, H.A.; Mandjes, M.; Meeuwissen, H.B.

    2013-01-01

    DNS tunnels allow circumventing access and security policies in firewalled networks. Such a security breach can be misused for activities like free web browsing, but also for command & control traffic or cyber espionage, thus motivating the search for effective automated DNS tunnel detection

  13. Memory detection 2.0: the first web-based memory detection test.

    Science.gov (United States)

    Kleinberg, Bennett; Verschuere, Bruno

    2015-01-01

    There is accumulating evidence that reaction times (RTs) can be used to detect recognition of critical (e.g., crime) information. A limitation of this research base is its reliance upon small samples (average n = 24), and indications of publication bias. To advance RT-based memory detection, we report upon the development of the first web-based memory detection test. Participants in this research (Study1: n = 255; Study2: n = 262) tried to hide 2 high salient (birthday, country of origin) and 2 low salient (favourite colour, favourite animal) autobiographical details. RTs allowed to detect concealed autobiographical information, and this, as predicted, more successfully so than error rates, and for high salient than for low salient items. While much remains to be learned, memory detection 2.0 seems to offer an interesting new platform to efficiently and validly conduct RT-based memory detection research.

  14. Memory detection 2.0: the first web-based memory detection test.

    Directory of Open Access Journals (Sweden)

    Bennett Kleinberg

    Full Text Available There is accumulating evidence that reaction times (RTs can be used to detect recognition of critical (e.g., crime information. A limitation of this research base is its reliance upon small samples (average n = 24, and indications of publication bias. To advance RT-based memory detection, we report upon the development of the first web-based memory detection test. Participants in this research (Study1: n = 255; Study2: n = 262 tried to hide 2 high salient (birthday, country of origin and 2 low salient (favourite colour, favourite animal autobiographical details. RTs allowed to detect concealed autobiographical information, and this, as predicted, more successfully so than error rates, and for high salient than for low salient items. While much remains to be learned, memory detection 2.0 seems to offer an interesting new platform to efficiently and validly conduct RT-based memory detection research.

  15. Underwater electric field detection system based on weakly electric fish

    Science.gov (United States)

    Xue, Wei; Wang, Tianyu; Wang, Qi

    2018-04-01

    Weakly electric fish sense their surroundings in complete darkness by their active electric field detection system. However, due to the insufficient detection capacity of the electric field, the detection distance is not enough, and the detection accuracy is not high. In this paper, a method of underwater detection based on rotating current field theory is proposed to improve the performance of underwater electric field detection system. First of all, we built underwater detection system based on the theory of the spin current field mathematical model with the help of the results of previous researchers. Then we completed the principle prototype and finished the metal objects in the water environment detection experiments, laid the foundation for the further experiments.

  16. Periodontal changes following molar intrusion with miniscrews

    Directory of Open Access Journals (Sweden)

    Shahin Bayani

    2015-01-01

    Conclusion: Within the limitations of this study, these results suggest that not only periodontal status was not negatively affected by intrusion, but also there were signs of periodontal improvement including attachment gain and shortening of clinical crown height.

  17. A Citizen's Guide to Vapor Intrusion Mitigation

    Science.gov (United States)

    This guide describes how vapor intrusion is the movement of chemical vapors from contaminated soil and groundwater into nearby buildings.Vapors primarily enter through openings in the building foundation or basement walls.

  18. Acknowledging the dilemmas of intrusive media

    DEFF Research Database (Denmark)

    Mathieu, David; Finger, Juliane; Dias, Patrcia

    2017-01-01

    Part of the stakeholder consultation addressed strategies that media audiences are developing to cope with pressures and intrusions in a changing media environment, characterised by digitalisation and interactive possibilities. We interviewed ten stakeholders representing interests such as content...... production, media literacy, media regulation, and activism. Consulting with these stakeholders left the impression that pressures and intrusions from media lack widespread acknowledgement, and that little is known about audiences’ strategies to cope with media. Even when intrusions are acknowledged, we find...... no consensual motivation, nor any clear avenue for action. Therefore, we have analysed different discursive positions that prevent acknowledging or taking action upon the pressures and intrusions that we presented to these stakeholders. The discursive positions are outlined below....

  19. Prevention and analysis of hacker's intrusion

    International Nuclear Information System (INIS)

    Liu Baoxu; An Dehai; Xu Rongsheng

    2000-01-01

    The author analyzes the behavior characteristics and relevant technologies about the hacker's intrusion, and gives some corresponding solutions pertinently. To the recent events about hackers, the author gives detailed introduction and puts forward the relevant advice and valuable consideration

  20. Why seawater intrusion has not yet occurred in the Kaluvelli-Pondicherry basin, Tamil Nadu, India

    Science.gov (United States)

    Vincent, Aude; Violette, Sophie

    2017-09-01

    Worldwide, coastal aquifers are threatened by seawater intrusion. The threat is greatest when aquifers are overexploited or when recharge is low due to a semi-arid or arid climate. The Kaluvelli-Pondicherry sedimentary basin in Tamil Nadu (India) presents both these characteristics. Groundwater levels in the Vanur aquifer can reach 50 m below sea level at less than 20 km inland. This groundwater depletion is due to an exponential increase in extraction for irrigation over 35 years. No seawater intrusion has yet been detected, but a sulphate-rich mineralization is observed, the result of upward vertical leakage from the underlying Ramanathapuram aquifer. To characterize the mechanisms involved, and to facilitate effective water management, hydrogeological numerical modelling of this multi-layered system has been conducted. Existing and acquired geological and hydrodynamic data have been applied to a quasi-3D hydrogeological model, NEWSAM. Recharge had been previously quantified through the inter-comparison of hydrological models, based on climatological and surface-flow field measurements. Sensitivity tests on parameters and boundary conditions associated with the sea were performed. The resulting water balances for each aquifer led to hypotheses of (1) an offshore fresh groundwater stock, and (2) a reversal and increase of the upward leakage from the Ramanathapuram aquifer, thus corroborating the hypothesis proposed to explain geochemical results of the previous study, and denying a seawater intrusion. Palaeo-climate review supports the existence of favourable hydro-climatological conditions to replenish an offshore groundwater stock of the Vanur aquifer in the past. The extent of this fresh groundwater stock was calculated using the Kooi and Groen method.

  1. Intrusion problematic during water supply systems’ operation

    OpenAIRE

    Jesus Mora-Rodriguez, P. Amparo López-Jimenez, Helena M. Ramos

    2011-01-01

    Intrusion through leaks occurrence is a phenomenon when external fluid comes into water pipe systems. This phenomenon can cause contamination problems in drinking pipe systems. Hence, this paper focuses on the entry of external fluids across small leaks during normal operation conditions. This situation is especially important in elevated points of the pipe profile. Pressure variations can origin water volume losses and intrusion of contaminants into the drinking water pipes. This work focuse...

  2. Cable-Based Water Leak Detection Technology

    OpenAIRE

    ECT Team, Purdue

    2007-01-01

    Water leaks can be considered as a serious problem from many sources such as water supply and return chains, air conditioning units, cold-water chillers, clogged drains, damaged skylights or windows, or even construction errors. The new water leak detection technologies can provide significant advantages in cost, reliability, and easy adoption have continued since the traditional technology mainly focusing on a spot detector revealed several limitations.

  3. Parkinson's disease detection based on dysphonia measurements

    Science.gov (United States)

    Lahmiri, Salim

    2017-04-01

    Assessing dysphonic symptoms is a noninvasive and effective approach to detect Parkinson's disease (PD) in patients. The main purpose of this study is to investigate the effect of different dysphonia measurements on PD detection by support vector machine (SVM). Seven categories of dysphonia measurements are considered. Experimental results from ten-fold cross-validation technique demonstrate that vocal fundamental frequency statistics yield the highest accuracy of 88 % ± 0.04. When all dysphonia measurements are employed, the SVM classifier achieves 94 % ± 0.03 accuracy. A refinement of the original patterns space by removing dysphonia measurements with similar variation across healthy and PD subjects allows achieving 97.03 % ± 0.03 accuracy. The latter performance is larger than what is reported in the literature on the same dataset with ten-fold cross-validation technique. Finally, it was found that measures of ratio of noise to tonal components in the voice are the most suitable dysphonic symptoms to detect PD subjects as they achieve 99.64 % ± 0.01 specificity. This finding is highly promising for understanding PD symptoms.

  4. Memory detection 2.0: The first web-based memory detection test

    NARCIS (Netherlands)

    Kleinberg, B.; Verschuere, B.

    2015-01-01

    There is accumulating evidence that reaction times (RTs) can be used to detect recognition of critical (e.g., crime) information. A limitation of this research base is its reliance upon small samples (average n = 24), and indications of publication bias. To advance RT-based memory detection, we

  5. Automatic hearing loss detection system based on auditory brainstem response

    International Nuclear Information System (INIS)

    Aldonate, J; Mercuri, C; Reta, J; Biurrun, J; Bonell, C; Gentiletti, G; Escobar, S; Acevedo, R

    2007-01-01

    Hearing loss is one of the pathologies with the highest prevalence in newborns. If it is not detected in time, it can affect the nervous system and cause problems in speech, language and cognitive development. The recommended methods for early detection are based on otoacoustic emissions (OAE) and/or auditory brainstem response (ABR). In this work, the design and implementation of an automated system based on ABR to detect hearing loss in newborns is presented. Preliminary evaluation in adults was satisfactory

  6. DNA based methods used for characterization and detection of food ...

    African Journals Online (AJOL)

    Detection of food borne pathogen is of outmost importance in the food industries and related agencies. For the last few decades conventional methods were used to detect food borne pathogens based on phenotypic characters. At the advent of complementary base pairing and amplification of DNA, the diagnosis of food ...

  7. Contributions of non-intrusive coupling in nonlinear structural mechanics

    International Nuclear Information System (INIS)

    Duval, Mickael

    2016-01-01

    This PhD thesis, part of the ANR ICARE project, aims at developing methods for complex analysis of large scale structures. The scientific challenge is to investigate very localised areas, but potentially critical as of mechanical systems resilience. Classically, representation models, discretizations, mechanical behaviour models and numerical tools are used at both global and local scales for simulation needs of graduated complexity. Global problem is handled by a generic code with topology (plate formulation, geometric approximation...) and behaviour (homogenization) simplifications while local analysis needs implementation of specialized tools (routines, dedicated codes) for an accurate representation of the geometry and behaviour. The main goal of this thesis is to develop an efficient non-intrusive coupling tool for multi-scale and multi-model structural analysis. Constraints of non-intrusiveness result in the non-modification of the stiffness operator, connectivity and the global model solver, allowing to work in a closed source software environment. First, we provide a detailed study of global/local non-intrusive coupling algorithm. Making use of several relevant examples (cracking, elastic-plastic behaviour, contact...), we show the efficiency and the flexibility of such coupling method. A comparative analysis of several optimisation tools is also carried on, and the interacting multiple patches situation is handled. Then, non-intrusive coupling is extended to globally non-linear cases, and a domain decomposition method with non-linear re-localization is proposed. Such methods allowed us to run a parallel computation using only sequential software, on a high performance computing cluster. Finally, we apply the coupling algorithm to mesh refinement with patches of finite elements. We develop an explicit residual based error estimator suitable for multi-scale solutions arising from the non-intrusive coupling, and apply it inside an error driven local mesh

  8. DNA & Protein detection based on microbead agglutination

    KAUST Repository

    Kodzius, Rimantas; Castro, David; Foulds, Ian G.; Parameswaran, Ash M.; Sumanpreet, K. Chhina

    2012-01-01

    the macroscopic observation. Agglutination-based tests are most often used to explore the antibody-antigen reactions. Agglutination has been used for mode protein assays using a biotin/streptavidin two-component system, as well as a hybridization based two

  9. Wireless Falling Detection System Based on Community.

    Science.gov (United States)

    Xia, Yun; Wu, Yanqi; Zhang, Bobo; Li, Zhiyang; He, Nongyue; Li, Song

    2015-06-01

    The elderly are more likely to suffer the aches or pains from the accidental falls, and both the physiology and psychology of patients would subject to a long-term disturbance, especially when the emergency treatment was not given timely and properly. Although many methods and devices have been developed creatively and shown their efficiency in experiments, few of them are suitable for commercial applications routinely. Here, we design a wearable falling detector as a mobile terminal, and utilize the wireless technology to transfer and monitor the activity data of the host in a relatively small community. With the help of the accelerometer sensor and the Google Mapping service, information of the location and the activity data will be send to the remote server for the downstream processing. The experimental result has shown that SA (Sum-vector of all axes) value of 2.5 g is the threshold value to distinguish the falling from other activities. A three-stage detection algorithm was adopted to increase the accuracy of the real alarm, and the accuracy rate of our system was more than 95%. With the further improvement, the falling detecting device which is low-cost, accurate and user-friendly would become more and more common in everyday life.

  10. Geospatial scenario based modelling of urban and agricultural intrusions in Ramsar wetland Deepor Beel in Northeast India using a multi-layer perceptron neural network

    Science.gov (United States)

    Mozumder, Chitrini; Tripathi, Nitin K.

    2014-10-01

    In recent decades, the world has experienced unprecedented urban growth which endangers the green environment in and around urban areas. In this work, an artificial neural network (ANN) based model is developed to predict future impacts of urban and agricultural expansion on the uplands of Deepor Beel, a Ramsar wetland in the city area of Guwahati, Assam, India, by 2025 and 2035 respectively. Simulations were carried out for three different transition rates as determined from the changes during 2001-2011, namely simple extrapolation, Markov Chain (MC), and system dynamic (SD) modelling, using projected population growth, which were further investigated based on three different zoning policies. The first zoning policy employed no restriction while the second conversion restriction zoning policy restricted urban-agricultural expansion in the Guwahati Municipal Development Authority (GMDA) proposed green belt, extending to a third zoning policy providing wetland restoration in the proposed green belt. The prediction maps were found to be greatly influenced by the transition rates and the allowed transitions from one class to another within each sub-model. The model outputs were compared with GMDA land demand as proposed for 2025 whereby the land demand as produced by MC was found to best match the projected demand. Regarding the conservation of Deepor Beel, the Landscape Development Intensity (LDI) Index revealed that wetland restoration zoning policies may reduce the impact of urban growth on a local scale, but none of the zoning policies was found to minimize the impact on a broader base. The results from this study may assist the planning and reviewing of land use allocation within Guwahati city to secure ecological sustainability of the wetlands.

  11. The Sonju Lake layered intrusion, northeast Minnesota: Internal structure and emplacement history inferred from magnetic fabrics

    Science.gov (United States)

    Maes, S.M.; Tikoff, B.; Ferre, E.C.; Brown, P.E.; Miller, J.D.

    2007-01-01

    The Sonju Lake intrusion (SLI), in northeastern Minnesota, is a layered mafic complex of Keweenawan age (1096.1 ?? 0.8 Ma) related to the Midcontinent rift. The cumulate paragenesis of the intrusion is recognized as broadly similar to the Skaergaard intrusion, a classic example of closed-system differentiation of a tholeiitic mafic magma. The SLI represents nearly closed-system differentiation through bottom-up fractional crystallization. Geochemical studies have identified the presence of a stratabound, 50-100 m thick zone anomalously enriched in Au + PGE. Similar to the PGE reefs of the Skaergaard intrusion, this PGE-enriched zone is hosted within oxide gabbro cumulates, about two-third of the way up from the base of the intrusion. We present a petrofabric study using the anisotropy of magnetic susceptibility (AMS) to investigate the emplacement and flow patterns within the Sonju Lake intrusion. Petrographic and electron microprobe studies, combined with AMS and hysteresis measurements indicate the primary source of the magnetic signal is pseudo-single domain (PSD) magnetite or titanomagnetite. Low field AMS was measured at 32 sites within the Sonju Lake intrusion, which provided information about primary igneous fabrics. The magnetic fabrics in the layered series of the Sonju Lake intrusion are consistent with sub-horizontal to inclined emplacement of the intrusion and show evidence that the cumulate layers were deposited in a dynamic environment. Well-aligned magnetic lineations, consistently plunging shallowly toward the southwest, indicate the source of the magma is a vertical sill-like feeder, presumably located beneath the Finland granite. The Finland granite acted as a density trap for the Sonju Lake magmas, forcing lateral flow of magma to the northeast. The strongly oblate magnetic shape fabrics indicate the shallowly dipping planar fabrics were enhanced by compaction of the crystal mush. ?? 2007 Elsevier B.V. All rights reserved.

  12. GLRT Based Anomaly Detection for Sensor Network Monitoring

    KAUST Repository

    Harrou, Fouzi

    2015-12-07

    Proper operation of antenna arrays requires continuously monitoring their performances. When a fault occurs in an antenna array, the radiation pattern changes and can significantly deviate from the desired design performance specifications. In this paper, the problem of fault detection in linear antenna arrays is addressed within a statistical framework. Specifically, a statistical fault detection method based on the generalized likelihood ratio (GLR) principle is utilized for detecting potential faults in linear antenna arrays. The proposed method relies on detecting deviations in the radiation pattern of the monitored array with respect to a reference (fault-free) one. To assess the abilities of the GLR based fault detection method, three case studies involving different types of faults have been performed. The simulation results clearly illustrate the effectiveness of the GLR-based fault detection method in monitoring the performance of linear antenna arrays.

  13. GLRT Based Anomaly Detection for Sensor Network Monitoring

    KAUST Repository

    Harrou, Fouzi; Sun, Ying

    2015-01-01

    Proper operation of antenna arrays requires continuously monitoring their performances. When a fault occurs in an antenna array, the radiation pattern changes and can significantly deviate from the desired design performance specifications. In this paper, the problem of fault detection in linear antenna arrays is addressed within a statistical framework. Specifically, a statistical fault detection method based on the generalized likelihood ratio (GLR) principle is utilized for detecting potential faults in linear antenna arrays. The proposed method relies on detecting deviations in the radiation pattern of the monitored array with respect to a reference (fault-free) one. To assess the abilities of the GLR based fault detection method, three case studies involving different types of faults have been performed. The simulation results clearly illustrate the effectiveness of the GLR-based fault detection method in monitoring the performance of linear antenna arrays.

  14. Muon Detection Based on a Hadronic Calorimeter

    CERN Document Server

    Ciodaro, T; Abreu, R; Achenbach, R; Adragna, P; Aharrouche, M; Aielli, G; Al-Shabibi, A; Aleksandrov, I; Alexandrov, E; Aloisio, A; Alviggi, M G; Amorim, A; Amram, N; Andrei, V; Anduaga, X; Angelaszek, D; Anjos, N; Annovi, A; Antonelli, S; Anulli, F; Apolle, R; Aracena, I; Ask, S; Åsman, B; Avolio, G; Baak, M; Backes, M; Backlund, S; Badescu, E; Baines, J; Ballestrero, S; Banerjee, S; Bansil, H S; Barnett, B M; Bartoldus, R; Bartsch, V; Batraneanu, S; Battaglia, A; Bauss, B; Beauchemin, P; Beck, H P; Bee, C; Begel, M; Behera, P K; Bell, P; Bell, W H; Bellagamba, L; Bellomo, M; Ben Ami, S; Bendel, M; Benhammou, Y; Benslama, K; Berge, D; Bernius, C; Berry, T; Bianco, M; Biglietti, M; Blair, R E; Bogaerts, A; Bohm, C; Boisvert, V; Bold, T; Bondioli, M; Borer, C; Boscherini, D; Bosman, M; Bossini, E; Boveia, A; Bracinik, J; Brandt, A G; Brawn, I P; Brelier, B; Brenner, R; Bressler, S; Brock, R; Brooks, W K; Brown, G; Brunet, S; Bruni, A; Bruni, G; Bucci, F; Buda, S; Burckhart-Chromek, D; Buscher, V; Buttinger, W; Calvet, S; Camarri, P; Campanelli, M; Canale, V; Canelli, F; Capasso, L; Caprini, M; Caracinha, D; Caramarcu, C; Cardarelli, R; Carlino, G; Casadei, D; Casado, M P; Cattani, G; Cerri, A; Cerrito, L; Chapleau, B; Childers, J T; Chiodini, G; Christidi, I; Ciapetti, G; Cimino, D; Ciobotaru, M; Coccaro, A; Cogan, J; Collins, N J; Conde Muino, P; Conidi, C; Conventi, F; Corradi, M; Corso-Radu, A; Coura Torres, R; Cranmer, K; Crescioli, F; Crone, G; Crupi, R; Cuenca Almenar, C; Cummings, J T; Curtis, C J; Czyczula, Z; Dam, M; Damazio, D; Dao, V; Darlea, G L; Davis, A O; De Asmundis, R; De Pedis, D; De Santo, A; de Seixas, J M; Degenhardt, J; Della Pietra, M; Della Volpe, D; Demers, S; Demirkoz, B; Di Ciaccio, A; Di Mattia, A; Di Nardo, R; Di Simone, A; Diaz, M A; Dietzsch, T A; Dionisi, C; Dobson, E; Dobson, M; dos Anjos, A; Dotti, A; Dova, M T; Drake, G; Dufour, M-A; Dumitru, I; Eckweiler, S; Ehrenfeld, W; Eifert, T; Eisenhandler, E; Ellis, K V; Ellis, N; Emeliyanov, D; Enoque Ferreira de Lima, D; Ermoline, Y; Ernst, J; Etzion, E; Falciano, S; Farrington, S; Farthouat, P; Faulkner , P J W; Fedorko, W; Fellmann, D; Feng, E; Ferrag, S; Ferrari, R; Ferrer, M L; Fiorini, L; Fischer, G; Flowerdew, M J; Fonseca Martin, T; Francis, D; Fratina, S; French, S T; Front, D; Fukunaga, C; Gadomski, S; Garelli, N; Garitaonandia Elejabarrieta, H; Gaudio, G; Gee, C N P; George, S; Giagu, S; Giannetti, P; Gillman, A R; Giorgi, M; Giunta, M; Giusti, P; Goebel, M; Gonçalo, R; Gonzalez Silva, L; Göringer, C; Gorini, B; Gorini, E; Grabowska-Bold, I; Green, B; Groll, M; Guida, A; Guler, H; Haas, S; Hadavand, H; Hadley, D R; Haller, J; Hamilton, A; Hanke, P; Hansen, J R; Hasegawa, S; Hasegawa, Y; Hauser, R; Hayakawa, T; Hayden, D; Head, S; Heim, S; Hellman, S; Henke, M; Hershenhorn, A; Hidvégi, A; Hillert, S; Hillier, S J; Hirayama, S; Hod, N; Hoffmann, D; Hong, T M; Hryn'ova, T; Huston, J; Iacobucci, G; Igonkina, O; Ikeno, M; Ilchenko, Y; Ishikawa, A; Ishino, M; Iwasaki, H; Izzo, V; Jez, P; Jimenez Otero, S; Johansen, M; Johns, K; Jones, G; Joos, M; Kadlecik, P; Kajomovitz, E; Kanaya, N; Kanega, F; Kanno, T; Kapliy, A; Kaushik, V; Kawagoe, K; Kawamoto, T; Kazarov, A; Kehoe, R; Kessoku, K; Khomich, A; Khoriauli, G; Kieft, G; Kirk, J; Klemetti, M; Klofver, P; Klous, S; Kluge, E-E; Kobayashi, T; Koeneke, K; Koletsou, I; Koll, J D; Kolos, S; Kono, T; Konoplich, R; Konstantinidis, N; Korcyl, K; Kordas, K; Kotov, V; Kowalewski, R V; Krasznahorkay, A; Kraus, J; Kreisel, A; Kubota, T; Kugel, A; Kunkle, J; Kurashige, H; Kuze, M; Kwee, R; Laforge, B; Landon, M; Lane, J; Lankford, A J; Laranjeira Lima, S M; Larner, A; Leahu, L; Lehmann Miotto, G; Lei, X; Lellouch, D; Levinson, L; Li, S; Liberti, B; Lilley, J N; Linnemann, J T; Lipeles, E; Lohse, T; Losada, M; Lowe, A; Luci, C; Luminari, L; Lundberg, J; Lupu, N; Machado Miguéns, J; Mackeprang, R; Maettig, S; Magnoni, L; Maiani, C; Maltrana, D; Mangeard, P-S; Männer, R; Mapelli, L; Marchese, F; Marino, C; Martin, B; Martin, B T; Martin, T; Martyniuk, A; Marzano, F; Masik, J; Mastrandrea, P; Matsushita, T; McCarn, A; Mechnich, J; Medinnis, M; Meier, K; Melachrinos, C; Mendoza Nava, L M; Merola, L; Messina, A; Meyer, C P; Middleton, R P; Mikenberg, G; Mills, C M; Mincer, A; Mineev, M; Misiejuk, A; Moa, T; Moenig, K; Monk, J; Monticelli, F; Mora Herrera, C; Morettini, P; Morris, J D; Müller, F; Munwes, Y; Murillo Garcia, R; Nagano, K; Nagasaka, Y; Navarro, G A; Negri, A; Nelson, S; Nemethy, P; Neubauer, M S; Neusiedl, A; Newman, P; Nisati, A; Nomoto, H; Nozaki, M; Nozicka, M; Nurse, E; Ochando, C; Ochi, A; Oda, S; Oh, A; Ohm, C; Okumura, Y; Olivito, D; Omachi, C; Osculati, B; Oshita, H; Ospanov, R; Owen, M A; Özcan, V E; Ozone, K; Padilla, C; Panes, B; Panikashvili, N; Paramonov, A; Parodi, F; Pasqualucci, E; Pastore, F; Patricelli, S; Pauly, T; Perera, V J O; Perez, E; Petcu, M; Petersen, B A; Petersen, J; Petrolo, E; Phan, A; Piegaia, R; Pilkington, A; Pinder, A; Poddar, S; Polini, A; Pope, B G; Potter, C T; Primavera, M; Prokoshin, F; Ptacek, E; Qian, W; Quinonez, F; Rajagopalan, S; Ramos Dos Santos Neves, R; Reinherz-Aronis, E; Reinsch, A; Renkel, P; Rescigno, M; Rieke, S; Riu, I; Robertson, S H; Robinson, M; Rodriguez, D; Roich, A; Romeo, G; Romero, R; Roos, L; Ruiz Martinez, A; Ryabov, Y; Ryan, P; Saavedra, A; Safai Tehrani, F; Sakamoto, H; Salamanna, G; Salamon, A; Saland, J; Salnikov, A; Salvatore, F; Sankey, D P C; Santamarina, C; Santonico, R; Sarkisyan-Grinbaum, E; Sasaki, O; Savu, D; Scannicchio, D A; Schäfer, U; Scharf, V L; Scheirich, D; Schiavi, C; Schlereth, J; Schmitt, K; Schroder, C; Schroer, N; Schultz-Coulon, H-C; Schwienhorst, R; Sekhniaidze, G; Sfyrla, A; Shamim, M; Sherman, D; Shimojima, M; Shochet, M; Shooltz, D; Sidoti, A; Silbert, O; Silverstein, S; Sinev, N; Siragusa, G; Sivoklokov, S; Sjoen, R; Sjölin, J; Slagle, K; Sloper, J E; Smith, B C; Soffer, A; Soloviev, I; Spagnolo, S; Spiwoks, R; Staley, R J; Stamen, R; Stancu, S; Steinberg, P; Stelzer, J; Stockton, M C; Straessner, A; Strauss, E A; Strom, D; Su, D; Sugaya, Y; Sugimoto, T; Sushkov, S; Sutton, M R; Suzuki, Y; Taffard, A; Taiblum, N; Takahashi, Y; Takeda, H; Takeshita, T; Tamsett, M; Tan, C L A; Tanaka, S; Tapprogge, S; Tarem, S; Tarem, Z; Taylor, C; Teixeira-Dias, P; Thomas, J P; Thompson, P D; Thomson, M A; Tokushuku, K; Tollefson, K; Tomoto, M; Topfel, C; Torrence, E; Touchard, F; Traynor, D; Tremblet, L; Tricoli, A; Tripiana, M; Triplett, N; True, P; Tsiakiris, M; Tsuno, S; Tuggle, J; Ünel, G; Urquijo, P; Urrejola, P; Usai, G; Vachon, B; Vallecorsa, S; Valsan, L; Vandelli, W; Vari, R; Vaz Gil Lopes, L; Veneziano, S; Ventura, A; Venturi, N; Vercesi, V; Vermeulen, J C; Volpi, G; Vorwerk, V; Wagner, P; Wang, M; Warburton, A; Watkins, P M; Watson, A T; Watson, M; Weber, P; Weidberg, A R; Wengler, T; Werner, P; Werth, M; Wessels, M; White, M; Whiteson, D; Wickens, F J; Wiedenmann, W; Wielers, M; Winklmeier, F; Woods, K S; Wu, S-L; Wu, X; Xaplanteris Karampatsos, L; Xella, S; Yakovlev, A; Yamazaki, Y; Yang, U; Yasu, Y; Yuan, L; Zaitsev, A; Zanello, L; Zhang, H; Zhang, J; Zhao, L; Zobernig, H; zur Nedden, M

    2010-01-01

    The ATLAS Tile hadronic calorimeter (TileCal) provides highly-segmented energy measurements of incoming particles. The information from TileCal's last segmentation layer can assist in muon tagging and it is being considered for a near future upgrade of the level-one trigger, mainly for rejecting triggers due to cavern background at the barrel region. A muon receiver for the TileCal muon signals is being designed in order to interface with the ATLAS level-one trigger. This paper addresses the preliminary studies concerning the muon discrimination capability for the muon receiver. Monte Carlo simulations for single muons from the interaction point were used to study the effectiveness of hadronic calorimeter information on muon detection.

  15. Muon Detection Based on a Hadronic Calorimeter

    CERN Document Server

    Ciodaro, Thiago; Abreu, R; Achenbach, R; Adragna, P; Aharrouche, M; Aielli, G; Al-Shabibi, A; Aleksandrov, I; Alexandrov, E; Aloisio, A; Alviggi, M G; Amorim, A; Amram, N; Andrei, V; Anduaga, X; Angelaszek, D; Anjos, N; Annovi, A; Antonelli, S; Anulli, F; Apolle, R; Aracena, I; Ask, S; Åsman, B; Avolio, G; Baak, M; Backes, M; Backlund, S; Badescu, E; Baines, J; Ballestrero, S; Banerjee, S; Bansil, H S; Barnett, B M; Bartoldus, R; Bartsch, V; Batraneanu, S; Battaglia, A; Bauss, B; Beauchemin, P; Beck, H P; Bee, C; Begel, M; Behera, P K; Bell, P; Bell, W H; Bellagamba, L; Bellomo, M; Ben Ami, S; Bendel, M; Benhammou, Y; Benslama, K; Berge, D; Bernius, C; Berry, T; Bianco, M; Biglietti, M; Blair, R E; Bogaerts, A; Bohm, C; Boisvert, V; Bold, T; Bondioli, M; Borer, C; Boscherini, D; Bosman, M; Bossini, E; Boveia, A; Bracinik, J; Brandt, A G; Brawn, I P; Brelier, B; Brenner, R; Bressler, S; Brock, R; Brooks, W K; Brown, G; Brunet, S; Bruni, A; Bruni, G; Bucci, F; Buda, S; Burckhart-Chromek, D; Buscher, V; Buttinger, W; Calvet, S; Camarri, P; Campanelli, M; Canale, V; Canelli, F; Capasso, L; Caprini, M; Caracinha, D; Caramarcu, C; Cardarelli, R; Carlino, G; Casadei, D; Casado, M P; Cattani, G; Cerri, A; Cerrito, L; Chapleau, B; Childers, J T; Chiodini, G; Christidi, I; Ciapetti, G; Cimino, D; Ciobotaru, M; Coccaro, A; Cogan, J; Collins, N J; Conde Muino, P; Conidi, C; Conventi, F; Corradi, M; Corso-Radu, A; Coura Torres, R; Cranmer, K; Crescioli, F; Crone, G; Crupi, R; Cuenca Almenar, C; Cummings, J T; Curtis, C J; Czyczula, Z; Dam, M; Damazio, D; Dao, V; Darlea, G L; Davis, A O; De Asmundis, R; De Pedis, D; De Santo, A; de Seixas, J M; Degenhardt, J; Della Pietra, M; Della Volpe, D; Demers, S; Demirkoz, B; Di Ciaccio, A; Di Mattia, A; Di Nardo, R; Di Simone, A; Diaz, M A; Dietzsch, T A; Dionisi, C; Dobson, E; Dobson, M; dos Anjos, A; Dotti, A; Dova, M T; Drake, G; Dufour, M-A; Dumitru, I; Eckweiler, S; Ehrenfeld, W; Eifert, T; Eisenhandler, E; Ellis, K V; Ellis, N; Emeliyanov, D; Enoque Ferreira de Lima, D; Ermoline, Y; Ernst, J; Etzion, E; Falciano, S; Farrington, S; Farthouat, P; Faulkner, P J W; Fedorko, W; Fellmann, D; Feng, E; Ferrag, S; Ferrari, R; Ferrer, M L; Fiorini, L; Fischer, G; Flowerdew, M J; Fonseca Martin, T; Francis, D; Fratina, S; French, S T; Front, D; Fukunaga, C; Gadomski, S; Garelli, N; Garitaonandia Elejabarrieta, H; Gaudio, G; Gee, C N P; George, S; Giagu, S; Giannetti, P; Gillman, A R; Giorgi, M; Giunta, M; Giusti, P; Goebel, M; Gonçalo, R; Gonzalez Silva, L; Göringer, C; Gorini, B; Gorini, E; Grabowska-Bold, I; Green, B; Groll, M; Guida, A; Guler, H; Haas, S; Hadavand, H; Hadley, D R; Haller, J; Hamilton, A; Hanke, P; Hansen, J R; Hasegawa, S; Hasegawa, Y; Hauser, R; Hayakawa, T; Hayden, D; Head, S; Heim, S; Hellman, S; Henke, M; Hershenhorn, A; Hidvégi, A; Hillert, S; Hillier, S J; Hirayama, S; Hod, N; Hoffmann, D; Hong, T M; Hryn'ova, T; Huston, J; Iacobucci, G; Igonkina, O; Ikeno, M; Ilchenko, Y; Ishikawa, A; Ishino, M; Iwasaki, H; Izzo, V; Jez, P; Jimenez Otero, S; Johansen, M; Johns, K; Jones, G; Joos, M; Kadlecik, P; Kajomovitz, E; Kanaya, N; Kanega, F; Kanno, T; Kapliy, A; Kaushik, V; Kawagoe, K; Kawamoto, T; Kazarov, A; Kehoe, R; Kessoku, K; Khomich, A; Khoriauli, G; Kieft, G; Kirk, J; Klemetti, M; Klofver, P; Klous, S; Kluge, E-E; Kobayashi, T; Koeneke, K; Koletsou, I; Koll, J D; Kolos, S; Kono, T; Konoplich, R; Konstantinidis, N; Korcyl, K; Kordas, K; Kotov, V; Kowalewski, R V; Krasznahorkay, A; Kraus, J; Kreisel, A; Kubota, T; Kugel, A; Kunkle, J; Kurashige, H; Kuze, M; Kwee, R; Laforge, B; Landon, M; Lane, J; Lankford, A J; Laranjeira Lima, S M; Larner, A; Leahu, L; Lehmann Miotto, G; Lei, X; Lellouch, D; Levinson, L; Li, S; Liberti, B; Lilley, J N; Linnemann, J T; Lipeles, E; Lohse, T; Losada, M; Lowe, A; Luci, C; Luminari, L; Lundberg, J; Lupu, N; Machado Miguéns, J; Mackeprang, R; Maettig, S; Magnoni, L; Maiani, C; Maltrana, D; Mangeard, P-S; Männer, R; Mapelli, L; Marchese, F; Marino, C; Martin, B; Martin, B T; Martin, T; Martyniuk, A; Marzano, F; Masik, J; Mastrandrea, P; Matsushita, T; McCarn, A; Mechnich, J; Medinnis, M; Meier, K; Melachrinos, C; Mendoza Nava, L M; Merola, L; Messina, A; Meyer, C P; Middleton, R P; Mikenberg, G; Mills, C M; Mincer, A; Mineev, M; Misiejuk, A; Moa, T; Moenig, K; Monk, J; Monticelli, F; Mora Herrera, C; Morettini, P; Morris, J D; Müller, F; Munwes, Y; Murillo Garcia, R; Nagano, K; Nagasaka, Y; Navarro, G A; Negri, A; Nelson, S; Nemethy, P; Neubauer, M S; Neusiedl, A; Newman, P; Nisati, A; Nomoto, H; Nozaki, M; Nozicka, M; Nurse, E; Ochando, C; Ochi, A; Oda, S; Oh, A; Ohm, C; Okumura, Y; Olivito, D; Omachi, C; Osculati, B; Oshita, H; Ospanov, R; Owen, M A; Özcan, V E; Ozone, K; Padilla, C; Panes, B; Panikashvili, N; Paramonov, A; Parodi, F; Pasqualucci, E; Pastore, F; Patricelli, S; Pauly, T; Perera, V J O; Perez, E; Petcu, M; Petersen, B A; Petersen, J; Petrolo, E; Phan, A; Piegaia, R; Pilkington, A; Pinder, A; Poddar, S; Polini, A; Pope, B G; Potter, C T; Primavera, M; Prokoshin, F; Ptacek, E; Qian, W; Quinonez, F; Rajagopalan, S; Ramos Dos Santos Neves, R; Reinherz-Aronis, E; Reinsch, A; Renkel, P; Rescigno, M; Rieke, S; Riu, I; Robertson, S H; Robinson, M; Rodriguez, D; Roich, A; Romeo, G; Romero, R; Roos, L; Ruiz Martinez, A; Ryabov, Y; Ryan, P; Saavedra, A; Safai Tehrani, F; Sakamoto, H; Salamanna, G; Salamon, A; Saland, J; Salnikov, A; Salvatore, F; Sankey, D P C; Santamarina, C; Santonico, R; Sarkisyan-Grinbaum, E; Sasaki, O; Savu, D; Scannicchio, D A; Schäfer, U; Scharf, V L; Scheirich, D; Schiavi, C; Schlereth, J; Schmitt, K; Schroder, C; Schroer, N; Schultz-Coulon, H-C; Schwienhorst, R; Sekhniaidze, G; Sfyrla, A; Shamim, M; Sherman, D; Shimojima, M; Shochet, M; Shooltz, D; Sidoti, A; Silbert, O; Silverstein, S; Sinev, N; Siragusa, G; Sivoklokov, S; Sjoen, R; Sjölin, J; Slagle, K; Sloper, J E; Smith, B C; Soffer, A; Soloviev, I; Spagnolo, S; Spiwoks, R; Staley, R J; Stamen, R; Stancu, S; Steinberg, P; Stelzer, J; Stockton, M C; Straessner, A; Strauss, E A; Strom, D; Su, D; Sugaya, Y; Sugimoto, T; Sushkov, S; Sutton, M R; Suzuki, Y; Taffard, A; Taiblum, N; Takahashi, Y; Takeda, H; Takeshita, T; Tamsett, M; Tan, C L A; Tanaka, S; Tapprogge, S; Tarem, S; Tarem, Z; Taylor, C; Teixeira-Dias, P; Thomas, J P; Thompson, P D; Thomson, M A; Tokushuku, K; Tollefson, K; Tomoto, M; Topfel, C; Torrence, E; Touchard, F; Traynor, D; Tremblet, L; Tricoli, A; Tripiana, M; Triplett, N; True, P; Tsiakiris, M; Tsuno, S; Tuggle, J; Ünel, G; Urquijo, P; Urrejola, P; Usai, G; Vachon, B; Vallecorsa, S; Valsan, L; Vandelli, W; Vari, R; Vaz Gil Lopes, L; Veneziano, S; Ventura, A; Venturi, N; Vercesi, V; Vermeulen, J C; Volpi, G; Vorwerk, V; Wagner, P; Wang, M; Warburton, A; Watkins, P M; Watson, A T; Watson, M; Weber, P; Weidberg, A R; Wengler, T; Werner, P; Werth, M; Wessels, M; White, M; Whiteson, D; Wickens, F J; Wiedenmann, W; Wielers, M; Winklmeier, F; Woods, K S; Wu, S-L; Wu, X; Xaplanteris Karampatsos, L; Xella, S; Yakovlev, A; Yamazaki, Y; Yang, U; Yasu, Y; Yuan, L; Zaitsev, A; Zanello, L; Zhang, H; Zhang, J; Zhao, L; Zobernig, H; zur Nedden, M

    2010-01-01

    The TileCal hadronic calorimeter provides a muon signal which can be used to assist in muon tagging at the ATLAS level-one trigger. Originally, the muon signal was conceived to be combined with the RPC trigger in order to reduce unforeseen high trigger rates due to cavern background. Nevertheless, the combined trigger cannot significantly deteriorate the muon detection performance at the barrel region. This paper presents preliminary studies concerning the impact in muon identification at the ATLAS level-one trigger, through the use of Monte Carlo simulations with single muons with 40 GeV/c momentum. Further, different trigger scenarios were proposed, together with an approach for matching both TileCal and RPC geometries.

  16. 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.

  17. Contextual anomaly detection for cyber-physical security in Smart Grids based on an artificial neural network model

    DEFF Research Database (Denmark)

    Kosek, Anna Magdalena

    2016-01-01

    control. An intrusion detection system observes distributed energy resource’s behaviour, control actions and the power system impact, and is tested together with an ongoing voltage control attack in a co-simulation set-up. The simulation results obtained with a real photovoltaic rooftop power plant data...

  18. 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.

  19. Laser Spot Detection Based on Reaction Diffusion.

    Science.gov (United States)

    Vázquez-Otero, Alejandro; Khikhlukha, Danila; Solano-Altamirano, J M; Dormido, Raquel; Duro, Natividad

    2016-03-01

    Center-location of a laser spot is a problem of interest when the laser is used for processing and performing measurements. Measurement quality depends on correctly determining the location of the laser spot. Hence, improving and proposing algorithms for the correct location of the spots are fundamental issues in laser-based measurements. In this paper we introduce a Reaction Diffusion (RD) system as the main computational framework for robustly finding laser spot centers. The method presented is compared with a conventional approach for locating laser spots, and the experimental results indicate that RD-based computation generates reliable and precise solutions. These results confirm the flexibility of the new computational paradigm based on RD systems for addressing problems that can be reduced to a set of geometric operations.

  20. Laser Spot Detection Based on Reaction Diffusion

    Directory of Open Access Journals (Sweden)

    Alejandro Vázquez-Otero

    2016-03-01

    Full Text Available Center-location of a laser spot is a problem of interest when the laser is used for processing and performing measurements. Measurement quality depends on correctly determining the location of the laser spot. Hence, improving and proposing algorithms for the correct location of the spots are fundamental issues in laser-based measurements. In this paper we introduce a Reaction Diffusion (RD system as the main computational framework for robustly finding laser spot centers. The method presented is compared with a conventional approach for locating laser spots, and the experimental results indicate that RD-based computation generates reliable and precise solutions. These results confirm the flexibility of the new computational paradigm based on RD systems for addressing problems that can be reduced to a set of geometric operations.

  1. Count out your intrusions: Effects of verbal encoding on intrusive memories

    NARCIS (Netherlands)

    Krans, J.; Näring, G.W.B.; Becker, E.S.

    2009-01-01

    Peri-traumatic information processing is thought to affect the development of intrusive trauma memories. This study aimed to replicate and improve the study by Holmes, Brewin, and Hennessy (2004, Exp. 3) on the role of peri-traumatic verbal processing in analogue traumatic intrusion development.

  2. Respon Konsumen pada Mobile Advergames: Intrusiveness dan Irritation

    Directory of Open Access Journals (Sweden)

    Sony Kusumasondjaja

    2016-12-01

    Full Text Available Abstract. Increasing adoption of mobile advergames to deliver marketing messages has not been followed by empirical findings to support its effectiveness. This research attempts to examine the effect of mobile advergames intrusiveness on consumer irritation, attitudes, and purchase intention. This investigation on mobile advergame effectiveness was based on the increasing use of mobile media to deliver marketing messages to consumers from different demographic background. Conceptual framework was developed based on Advertising Avoidance Theory. For data collection, self-administered survey was conducted by adopting purposive sampling involving 213 respondents residing in Surabaya who have had experience in playing mobile game as respondents. Results indicate that intrusiveness positively affects consumer irritation. Consumer irritation negatively affects attitude towards the mobile advergames and attitude towards the advertised product. The better the consumer attitude towards the mobile advergames, the more positive the attitude towards the advertised product. Moreover, the more positive the attitude towards the advertised product, the greater the consumer intention to purchase. Interestingly, consumer attitude toward the mobile advergames has insignificant influence on purchase intention. Findings of the study offer significant contribution to marketing practices using mobile advergames as media placement in their advertising strategy. Keywords: intrusiveness, irritation, mobile advergames, attitude, advertising

  3. Aptamer-Based Technologies in Foodborne Pathogen Detection.

    Science.gov (United States)

    Teng, Jun; Yuan, Fang; Ye, Yingwang; Zheng, Lei; Yao, Li; Xue, Feng; Chen, Wei; Li, Baoguang

    2016-01-01

    Aptamers are single stranded DNA or RNA ligands, which can be selected by a method called systematic evolution of ligands by exponential enrichment (SELEX); and they can specifically recognize and bind to their targets. These unique characteristics of aptamers offer great potentials in applications such as pathogen detection and biomolecular screening. Pathogen detection is the critical means in detecting and identifying the problems related to public health and food safety; and only the rapid, sensitive and efficient detection technologies can enable the users to make the accurate assessments on the risks of infections (humans and animals) or contaminations (foods and other commodities) caused by various pathogens. This article reviews the development in the field of the aptamer-based approaches for pathogen detection, including whole-cell SELEX and Genomic SELEX. Nowadays, a variety of aptamer-based biosensors have been developed for pathogen detection. Thus, in this review, we also cover the development in aptamer-based biosensors including optical biosensors for multiple pathogen detection by multiple-labeling or label-free models such as fluorescence detection and surface plasmon resonance, electrochemical biosensors and lateral chromatography test strips, and their applications in pathogen detection and biomolecular screening. While notable progress has been made in the field in the last decade, challenges or drawbacks in their applications such as pathogen detection and biomolecular screening remain to be overcome.

  4. Aptamer-Based Technologies in Foodborne Pathogen Detection

    Directory of Open Access Journals (Sweden)

    Jun Teng

    2016-09-01

    Full Text Available Aptamers are single stranded DNA or RNA ligands, which can be selected by a method called systematic evolution of ligands by exponential enrichment (SELEX; and they can specifically recognize and bind to their targets. These unique characteristics of aptamers offer great potentials in applications such as pathogen detection and biomolecular screening. Pathogen detection is the first and critical means in detecting and identifying the problems related to public health and food safety; and only the rapid, sensitive and efficient detection technologies can enable the users to make to accurate assessments on the risk of infections (humans and animals or contaminations (foods and other commodities caused by various pathogens. This article reviews the developments in the field of the aptamer-based approaches for pathogen detection, including whole-cell SELEX and Genomic SELEX. Nowadays, a variety of aptamer-based biosensors have been developed for pathogen detection. Thus, in this review, we also cover the development of aptamer-based biosensors including optical biosensors for multiple pathogen detection in multiple-labeling or label-free models such as fluorescence detection and surface plasmon resonance, electrochemical biosensors, and lateral chromatography test strips, and their applications in the pathogen detection and biomolecular screening. While notable progress has been made in the field in the last decade, challenges or drawbacks in their applications such as pathogen detection and biomolecular screening, remain to be overcome.

  5. Research on moving object detection based on frog's eyes

    Science.gov (United States)

    Fu, Hongwei; Li, Dongguang; Zhang, Xinyuan

    2008-12-01

    On the basis of object's information processing mechanism with frog's eyes, this paper discussed a bionic detection technology which suitable for object's information processing based on frog's vision. First, the bionics detection theory by imitating frog vision is established, it is an parallel processing mechanism which including pick-up and pretreatment of object's information, parallel separating of digital image, parallel processing, and information synthesis. The computer vision detection system is described to detect moving objects which has special color, special shape, the experiment indicates that it can scheme out the detecting result in the certain interfered background can be detected. A moving objects detection electro-model by imitating biologic vision based on frog's eyes is established, the video simulative signal is digital firstly in this system, then the digital signal is parallel separated by FPGA. IN the parallel processing, the video information can be caught, processed and displayed in the same time, the information fusion is taken by DSP HPI ports, in order to transmit the data which processed by DSP. This system can watch the bigger visual field and get higher image resolution than ordinary monitor systems. In summary, simulative experiments for edge detection of moving object with canny algorithm based on this system indicate that this system can detect the edge of moving objects in real time, the feasibility of bionic model was fully demonstrated in the engineering system, and it laid a solid foundation for the future study of detection technology by imitating biologic vision.

  6. Repeated magmatic intrusions at El Hierro Island following the 2011-2012 submarine eruption

    Science.gov (United States)

    Benito-Saz, Maria A.; Parks, Michelle M.; Sigmundsson, Freysteinn; Hooper, Andrew; García-Cañada, Laura

    2017-09-01

    After more than 200 years of quiescence, in July 2011 an intense seismic swarm was detected beneath the center of El Hierro Island (Canary Islands), culminating on 10 October 2011 in a submarine eruption, 2 km off the southern coast. Although the eruption officially ended on 5 March 2012, magmatic activity continued in the area. From June 2012 to March 2014, six earthquake swarms, indicative of magmatic intrusions, were detected underneath the island. We have studied these post-eruption intrusive events using GPS and InSAR techniques to characterize the ground surface deformation produced by each of these intrusions, and to determine the optimal source parameters (geometry, location, depth, volume change). Source inversions provide insight into the depth of the intrusions ( 11-16 km) and the volume change associated with each of them (between 0.02 and 0.13 km3). During this period, > 20 cm of uplift was detected in the central-western part of the island, corresponding to approximately 0.32-0.38 km3 of magma intruded beneath the volcano. We suggest that these intrusions result from deep magma migrating from the mantle, trapped at the mantle/lower crust discontinuity in the form of sill-like bodies. This study, using joint inversion of GPS and InSAR data in a post-eruption period, provides important insight into the characteristics of the magmatic plumbing system of El Hierro, an oceanic intraplate volcanic island.

  7. Novel gas-based detection techniques

    International Nuclear Information System (INIS)

    Graaf, Harry van der

    2009-01-01

    This year we celebrate the 100th birthday of gaseous detectors: Hans Geiger operated the first gas-filled counter in Manchester in 1908. The thin wires, essential for obtaining gas amplification, have been replaced by Micro Pattern Gas Detectors (MPGDs): Micromegas (1995) and GEM (1996). In the GridPix detector, each of the grid holes of a MPGD is equipped with its own electronic readout channel in the form of an active pixel in suitable pixel CMOS chips. By means of MEMS technology, the grid has been integrated with the chip, forming a monolithic readout device for gas volumes. By applying a protection layer made of hydrogenated amorphous silicon, the chips can be made spark proof. New protection layers have been made of silicon nitride. The use of gas as detection material for trackers is compared to Si, and the issue of chamber aging is addressed. New developments are set out: the development of Micro Channel Plates, integrated on pixel chips, the development of electron emission foil, and the realization of TimePix-2: a general-purpose pixel chip with time and amplitude measurement, per pixel, of charge signals.

  8. Laser Spot Detection Based on Reaction Diffusion

    OpenAIRE

    Alejandro Vázquez-Otero; Danila Khikhlukha; J. M. Solano-Altamirano; Raquel Dormido; Natividad Duro

    2016-01-01

    Center-location of a laser spot is a problem of interest when the laser is used for processing and performing measurements. Measurement quality depends on correctly determining the location of the laser spot. Hence, improving and proposing algorithms for the correct location of the spots are fundamental issues in laser-based measurements. In this paper we introduce a Reaction Diffusion (RD) system as the main computational framework for robustly finding laser spot centers. The method presente...

  9. Similarity-based Polymorphic Shellcode Detection

    Directory of Open Access Journals (Sweden)

    Denis Yurievich Gamayunov

    2013-02-01

    Full Text Available In the work the method for polymorphic shellcode dedection based on the set of known shellcodes is proposed. The method’s main idea is in sequential applying of deobfuscating transformations to a data analyzed and then recognizing similarity with malware samples. The method has been tested on the sets of shellcodes generated using Metasploit Framework v.4.1.0 and PELock Obfuscator and shows 87 % precision with zero false positives rate.

  10. Sill intrusion in volcanic calderas: implications for vent opening probability

    Science.gov (United States)

    Giudicepietro, Flora; Macedonio, Giovanni; Martini, Marcello; D'Auria, Luca

    2017-04-01

    Calderas show peculiar behaviors with remarkable dynamic processes, which do not often culminate in eruptions. Observations and studies conducted in recent decades have shown that the most common cause of unrest in the calderas is due to magma intrusion; in particular, the intrusion of sills at shallow depths. Monogenic cones, with large areal dispersion, are quite common in the calderas, suggesting that the susceptibility analysis based on geological features, is not strictly suitable for estimating the vent opening probability in calderas. In general, the opening of a new eruptive vent can be regarded as a rock failure process. The stress field in the rocks that surrounds and tops the magmatic reservoirs plays an important role in causing the rock failure and creating the path that magma can follow towards the surface. In this conceptual framework, we approach the problem of getting clues about the probability of vent opening in volcanic calderas through the study of the stress field produced by the intrusion of magma, in particular, by the intrusion of a sill. We simulate the intrusion of a sill free to expand radially, with shape and dimensions which vary with time. The intrusion process is controlled by the elastic response of the rock plate above the sill, which bends because of the intrusion, and by gravity, that drives the magma towards the zones where the thickness of the sill is smaller. We calculated the stress field in the plate rock above the sill. We found that at the bottom of the rock plate above the sill the maximum intensity of tensile stress is concentrated at the front of the sill and spreads radially with it, over time. For this reason, we think that the front of the spreading sill is prone to open for eruptive vents. Even in the central area of the sill the intensity of stress is relatively high, but at the base of the rock plate stress is compressive. Under isothermal conditions, the stress soon reaches its maximum value (time interval

  11. IGNEOUS INTRUSION IMPACTS ON WASTE PACKAGES AND WASTE FORMS

    International Nuclear Information System (INIS)

    Bernot, P.

    2004-01-01

    The purpose of this model report is to assess the potential impacts of igneous intrusion on waste packages and waste forms in the emplacement drifts at the Yucca Mountain Repository. The models are based on conceptual models and includes an assessment of deleterious dynamic, thermal, hydrologic, and chemical impacts. The models described in this report constitute the waste package and waste form impacts submodel of the Total System Performance Assessment for the License Application (TSPA-LA) model assessing the impacts of a hypothetical igneous intrusion event on the repository total system performance. This submodel is carried out in accordance with Technical Work Plan for Waste Form Degradation Modeling, Testing, and Analyses in Support of LA (BSC 2004 [DIRS:167796]) and Total System Performance Assessment-License Application Methods and Approaches (BSC 2003 [DIRS: 166296]). The technical work plan was prepared in accordance with AP-2.27Q, Planning for Science Activities. Any deviations from the technical work plan are documented in the following sections as they occur. The TSPA-LA approach to implementing the models for waste package and waste form response during igneous intrusion is based on identification of damage zones. Zone 1 includes all emplacement drifts intruded by the basalt dike, and Zone 2 includes all other emplacement drifts in the repository that are not in Zone 1. This model report will document the following model assessments: (1) Mechanical and thermal impacts of basalt magma intrusion on the invert, waste packages and waste forms of the intersected emplacement drifts of Zone 1. (2) Temperature and pressure trends of basaltic magma intrusion intersecting Zone 1 and their potential effects on waste packages and waste forms in Zone 2 emplacement drifts. (3) Deleterious volatile gases, exsolving from the intruded basalt magma and their potential effects on waste packages of Zone 2 emplacement drifts. (4) Post-intrusive physical

  12. Developing nucleic acid-based electrical detection systems

    Directory of Open Access Journals (Sweden)

    Gabig-Ciminska Magdalena

    2006-03-01

    Full Text Available Abstract Development of nucleic acid-based detection systems is the main focus of many research groups and high technology companies. The enormous work done in this field is particularly due to the broad versatility and variety of these sensing devices. From optical to electrical systems, from label-dependent to label-free approaches, from single to multi-analyte and array formats, this wide range of possibilities makes the research field very diversified and competitive. New challenges and requirements for an ideal detector suitable for nucleic acid analysis include high sensitivity and high specificity protocol that can be completed in a relatively short time offering at the same time low detection limit. Moreover, systems that can be miniaturized and automated present a significant advantage over conventional technology, especially if detection is needed in the field. Electrical system technology for nucleic acid-based detection is an enabling mode for making miniaturized to micro- and nanometer scale bio-monitoring devices via the fusion of modern micro- and nanofabrication technology and molecular biotechnology. The electrical biosensors that rely on the conversion of the Watson-Crick base-pair recognition event into a useful electrical signal are advancing rapidly, and recently are receiving much attention as a valuable tool for microbial pathogen detection. Pathogens may pose a serious threat to humans, animal and plants, thus their detection and analysis is a significant element of public health. Although different conventional methods for detection of pathogenic microorganisms and their toxins exist and are currently being applied, improvements of molecular-based detection methodologies have changed these traditional detection techniques and introduced a new era of rapid, miniaturized and automated electrical chip detection technologies into pathogen identification sector. In this review some developments and current directions in

  13. Multifeature Fusion Vehicle Detection Algorithm Based on Choquet Integral

    Directory of Open Access Journals (Sweden)

    Wenhui Li

    2014-01-01

    Full Text Available Vision-based multivehicle detection plays an important role in Forward Collision Warning Systems (FCWS and Blind Spot Detection Systems (BSDS. The performance of these systems depends on the real-time capability, accuracy, and robustness of vehicle detection methods. To improve the accuracy of vehicle detection algorithm, we propose a multifeature fusion vehicle detection algorithm based on Choquet integral. This algorithm divides the vehicle detection problem into two phases: feature similarity measure and multifeature fusion. In the feature similarity measure phase, we first propose a taillight-based vehicle detection method, and then vehicle taillight feature similarity measure is defined. Second, combining with the definition of Choquet integral, the vehicle symmetry similarity measure and the HOG + AdaBoost feature similarity measure are defined. Finally, these three features are fused together by Choquet integral. Being evaluated on public test collections and our own test images, the experimental results show that our method has achieved effective and robust multivehicle detection in complicated environments. Our method can not only improve the detection rate but also reduce the false alarm rate, which meets the engineering requirements of Advanced Driving Assistance Systems (ADAS.

  14. 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

  15. Model Based Fault Detection in a Centrifugal Pump Application

    DEFF Research Database (Denmark)

    Kallesøe, Carsten; Cocquempot, Vincent; Izadi-Zamanabadi, Roozbeh

    2006-01-01

    A model based approach for fault detection in a centrifugal pump, driven by an induction motor, is proposed in this paper. The fault detection algorithm is derived using a combination of structural analysis, observer design and Analytical Redundancy Relation (ARR) design. Structural considerations...

  16. Active Fault Detection Based on a Statistical Test

    DEFF Research Database (Denmark)

    Sekunda, André Krabdrup; Niemann, Hans Henrik; Poulsen, Niels Kjølstad

    2016-01-01

    In this paper active fault detection of closed loop systems using dual Youla-Jabr-Bongiorno-Kucera(YJBK) parameters is presented. Until now all detector design for active fault detection using the dual YJBK parameters has been based on CUSUM detectors. Here a method for design of a matched filter...

  17. Nonlinear Model-Based Fault Detection for a Hydraulic Actuator

    NARCIS (Netherlands)

    Van Eykeren, L.; Chu, Q.P.

    2011-01-01

    This paper presents a model-based fault detection algorithm for a specific fault scenario of the ADDSAFE project. The fault considered is the disconnection of a control surface from its hydraulic actuator. Detecting this type of fault as fast as possible helps to operate an aircraft more cost

  18. Treatment of Intrusive Suicidal Imagery Using Eye Movements

    Directory of Open Access Journals (Sweden)

    Jaël S. van Bentum

    2017-06-01

    Full Text Available Suicide and suicidal behavior are major public health concerns, and affect 3–9% of the population worldwide. Despite increased efforts for national suicide prevention strategies, there are still few effective interventions available for reducing suicide risk. In this article, we describe various theoretical approaches for suicide ideation and behavior, and propose to examine the possible effectiveness of a new and innovative preventive strategy. A model of suicidal intrusion (mental imagery related to suicide, also referred to as suicidal flash-forwards is presented describing one of the assumed mechanisms in the etiology of suicide and the mechanism of therapeutic change. We provide a brief rationale for an Eye Movement Dual Task (EMDT treatment for suicidal intrusions, describing techniques that can be used to target these suicidal mental images and thoughts to reduce overall behavior. Based on the available empirical evidence for the mechanisms of suicidal intrusions, this approach appears to be a promising new treatment to prevent suicidal behavior as it potentially targets one of the linking pins between suicidal ideation and suicidal actions.

  19. 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.

  20. Numerical Continuation Methods for Intrusive Uncertainty Quantification Studies

    Energy Technology Data Exchange (ETDEWEB)

    Safta, Cosmin [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Najm, Habib N. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Phipps, Eric Todd [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2014-09-01

    Rigorous modeling of engineering systems relies on efficient propagation of uncertainty from input parameters to model outputs. In recent years, there has been substantial development of probabilistic polynomial chaos (PC) Uncertainty Quantification (UQ) methods, enabling studies in expensive computational models. One approach, termed ”intrusive”, involving reformulation of the governing equations, has been found to have superior computational performance compared to non-intrusive sampling-based methods in relevant large-scale problems, particularly in the context of emerging architectures. However, the utility of intrusive methods has been severely limited due to detrimental numerical instabilities associated with strong nonlinear physics. Previous methods for stabilizing these constructions tend to add unacceptably high computational costs, particularly in problems with many uncertain parameters. In order to address these challenges, we propose to adapt and improve numerical continuation methods for the robust time integration of intrusive PC system dynamics. We propose adaptive methods, starting with a small uncertainty for which the model has stable behavior and gradually moving to larger uncertainty where the instabilities are rampant, in a manner that provides a suitable solution.