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

On Emulation-Based Network Intrusion Detection Systems

NARCIS (Netherlands)

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

2014-01-01

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

On emulation-based network intrusion detection systems

NARCIS (Netherlands)

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

2014-01-01

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

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.

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.

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.

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.

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

Approaches in anomaly-based network intrusion detection systems

NARCIS (Netherlands)

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

2008-01-01

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

Approaches in Anomaly-based Network Intrusion Detection Systems

NARCIS (Netherlands)

Bolzoni, D.; Etalle, Sandro

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

Combining Host-based and network-based intrusion detection system

African Journals Online (AJOL)

These attacks were simulated using hping. The proposed system is implemented in Java. The results show that the proposed system is able to detect attacks both from within (host-based) and outside sources (network-based). Key Words: Intrusion Detection System (IDS), Host-based, Network-based, Signature, Security log.

A 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.

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.

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...

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.

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.

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

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...

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.

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.

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

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.

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.

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.

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...

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.

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.

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...

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. ...

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.

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

NARCIS (Netherlands)

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

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

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

NARCIS (Netherlands)

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

2009-01-01

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

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

NARCIS (Netherlands)

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

2009-01-01

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

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

NARCIS (Netherlands)

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

2009-01-01

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

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...

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

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.

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

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

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…

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

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.

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 ...

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.

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.

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.

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....

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.

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.

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

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

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

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.

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.

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.

Data mining approach to web application intrusions detection

Science.gov (United States)

Kalicki, Arkadiusz

2011-10-01

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

A 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%.

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.

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.

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

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.

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.

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.

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

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.

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...

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.

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

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

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.

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.

The evolution of interior intrusion detection technology at Sandia National Laboratories

International Nuclear Information System (INIS)

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

1987-07-01

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

The evolution of interior intrusion detection technology at Sandia National Laboratories

International Nuclear Information System (INIS)

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

1987-01-01

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

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.

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.

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.

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...

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.

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)

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.

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

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.

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.

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.

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.

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.

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

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.

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...

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...

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.

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.

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.

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...

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.

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

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 ...

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.

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...

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...

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.

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

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.

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.

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...

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)

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%.

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...

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.

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 ...

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

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

Science.gov (United States)

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

2014-11-01

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

A 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.

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

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

Directory of Open Access Journals (Sweden)

Min-Joo Kang

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

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

Science.gov (United States)

Kang, Min-Joo; Kang, Je-Won

2016-01-01

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

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...

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.

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

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...

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

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). ...

Multi-User Low Intrusive Occupancy Detection.

Science.gov (United States)

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

2018-03-06

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

Multi-User Low Intrusive Occupancy Detection

Science.gov (United States)

Widyawan, Widyawan; Lazovik, Alexander

2018-01-01

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

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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

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...

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.

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

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.

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.

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

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.

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

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

International Nuclear Information System (INIS)

EL-Kafas, A.A.

2011-01-01

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

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...

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

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.

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.

Abstracting massive data for lightweight intrusion detection in computer networks

KAUST Repository

Wang, Wei

2016-10-15

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

Access Control from an Intrusion Detection Perspective

NARCIS (Netherlands)

Nunes Leal Franqueira, V.

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

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.

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

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.

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....

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

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.

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.

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

Directory of Open Access Journals (Sweden)

Malik N Ahmed

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

A two-stage flow-based intrusion detection model for next-generation networks.

Science.gov (United States)

Umer, Muhammad Fahad; Sher, Muhammad; Bi, Yaxin

2018-01-01

The next-generation network provides state-of-the-art access-independent services over converged mobile and fixed networks. Security in the converged network environment is a major challenge. Traditional packet and protocol-based intrusion detection techniques cannot be used in next-generation networks due to slow throughput, low accuracy and their inability to inspect encrypted payload. An alternative solution for protection of next-generation networks is to use network flow records for detection of malicious activity in the network traffic. The network flow records are independent of access networks and user applications. In this paper, we propose a two-stage flow-based intrusion detection system for next-generation networks. The first stage uses an enhanced unsupervised one-class support vector machine which separates malicious flows from normal network traffic. The second stage uses a self-organizing map which automatically groups malicious flows into different alert clusters. We validated the proposed approach on two flow-based datasets and obtained promising results.

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

KAUST Repository

Hassanzadeh, Amin

2011-07-18

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

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....

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.

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.

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

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

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.

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.

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.

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

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.

Intrusion problematic during water supply systems' operation

Energy Technology Data Exchange (ETDEWEB)

Mora-Rodriguez, Jesus; Lopez-Jimenez, P. Amparo [Departamento de Ingenieria Hidraulica y Medio Ambiente, Universidad Politecnica de Valencia, Camino de Vera, s/n, 46022, Valencia (Spain); Ramos, Helena M. [Civil Engineering Department and CEHIDRO, Instituto Superior Tecnico, Technical University of Lisbon, Av. Rovisco Pais, 1049-001, Lisbon (Portugal)

2011-07-01

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 focuses in obtaining up the physical representation on a specific case intrusion in a pipe water system. The combination of two factors is required to generate this kind of intrusion in a water supply system: on one hand the existence of at least a leak in the system; on the other hand, a pressure variation could occur during the operation of the system due to consumption variation, pump start-up or shutdown. The potential of intrusion during a dynamic or transient event is here analyzed. To obtain this objective an experimental case study of pressure transient scenario is analyzed with a small leak located nearby the transient source.

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.

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...

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

KAUST Repository

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

2014-01-01

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

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

KAUST Repository

Wang, Wei

2014-06-22

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

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.

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...

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...

An intrusion prevention system as a proactive security mechanism in network infrastructure

Directory of Open Access Journals (Sweden)

Dulanović Nenad

2008-01-01

Full Text Available A properly configured firewall is a good starting point in securing a computer network. However, complex network environments that involve higher number of participants and endpoints require better security infrastructure. Intrusion Detection Systems (IDS, proposed as a solution to perimeter defense, have many open problems and it is clear that better solutions must be found. Due to many unsolved problems associated with IDS, Intrusion Prevention Systems (IPS are introduced. The main idea in IPS is to be proactive. This paper gives an insight of Cobrador Bouncer IPS implementation. System architecture is given and three different Bouncer IPS deployment modes are presented. The Bouncer IPS as a proactive honeypot is also discussed.

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.

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.

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

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

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

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.

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

DEFF Research Database (Denmark)

Meng, Weizhi; Li, Wenjuan; Su, Chunhua

2017-01-01

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

A Survey on 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 ...

Adaptive intrusion data system (AIDS) software routines

International Nuclear Information System (INIS)

Corlis, N.E.

1980-07-01

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

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)

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.

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.

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.

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.

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.

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.

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)

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.

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...

Environmental data processor of the adaptive intrusion data system

International Nuclear Information System (INIS)

Rogers, M.S.

1977-06-01

A data acquisition system oriented specifically toward collection and processing of various meteorological and environmental parameters has been designed around a National Semiconductor IMP-16 microprocessor, This system, called the Environmental Data Processor (EDP), was developed specifically for use with the Adaptive Intrusion Data System (AIDS) in a perimeter intrusion alarm evaluation, although its design is sufficiently general to permit use elsewhere. This report describes in general detail the design of the EDP and its interaction with other AIDS components

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.

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

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

Directory of Open Access Journals (Sweden)

Ralf C. Staudemeyer

2015-07-01

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

A 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.

Detection and recognition of mechanical, digging and vehicle signals in the optical fiber pre-warning system

Science.gov (United States)

Tian, Qing; Yang, Dan; Zhang, Yuan; Qu, Hongquan

2018-04-01

This paper presents detection and recognition method to locate and identify harmful intrusions in the optical fiber pre-warning system (OFPS). Inspired by visual attention architecture (VAA), the process flow is divided into two parts, i.e., data-driven process and task-driven process. At first, data-driven process takes all the measurements collected by the system as input signals, which is handled by detection method to locate the harmful intrusion in both spatial domain and time domain. Then, these detected intrusion signals are taken over by task-driven process. Specifically, we get pitch period (PP) and duty cycle (DC) of the intrusion signals to identify the mechanical and manual digging (MD) intrusions respectively. For the passing vehicle (PV) intrusions, their strong low frequency component can be used as good feature. In generally, since the harmful intrusion signals only account for a small part of whole measurements, the data-driven process reduces the amount of input data for subsequent task-driven process considerably. Furthermore, the task-driven process determines the harmful intrusions orderly according to their severity, which makes a priority mechanism for the system as well as targeted processing for different harmful intrusion. At last, real experiments are performed to validate the effectiveness of this method.

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)

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.

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

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.

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

The design about the intrusion defense system for IHEP

International Nuclear Information System (INIS)

Liu Baoxu; Xu Rongsheng; Yu Chuansong; Wu Chunzhen

2003-01-01

With the development of network technologies, limitations on traditional methods of network security protection are becoming more and more obvious. An individual network security product or the simple combination of several products can hardly complete the goal of keeping from hackers' intrusion. Therefore, on the basis of the analyses about the security problems of IHEPNET which is an open and scientific research network, the author designs an intrusion defense system especially for IHEPNET

A survey of intrusion detection techniques in Cloud

OpenAIRE

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

2013-01-01

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

A Novel Architecture for Intrusion Detection in Mobile Ad hoc Network

OpenAIRE

Atul Patel; Ruchi Kansara; Dr. Paresh Virparia

2011-01-01

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

Perancangan dan Implementasi Instrusion Detection System di Jaringan Universitas Diponegoro

Directory of Open Access Journals (Sweden)

Dyakso Anindito Nugroho

2015-04-01

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

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.

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

Conjunctive Management of Multi-Aquifer System for Saltwater Intrusion Mitigation

Science.gov (United States)

Tsai, F. T. C.; Pham, H. V.

2015-12-01

Due to excessive groundwater withdrawals, many water wells in Baton Rouge, Louisiana experience undesirable chloride concentration because of saltwater intrusion. The study goal is to develop a conjunctive management framework that takes advantage of the Baton Rouge multi-aquifer system to mitigate saltwater intrusion. The conjunctive management framework utilizes several hydraulic control techniques to mitigate saltwater encroachment. These hydraulic control approaches include pumping well relocation, freshwater injection, saltwater scavenging, and their combinations. Specific objectives of the study are: (1) constructing scientific geologic architectures of the "800-foot" sand, the "1,000-foot" sand, the "1,200-foot" sand, the "1,500-foot" sand, the "1,700-foot" sand, and the "2,000-foot" sand, (2) developing scientific saltwater intrusion models for these sands. (3) using connector wells to draw native groundwater from one sand and inject to another sand to create hydraulic barriers to halt saltwater intrusion, (4) using scavenger wells or well couples to impede saltwater intrusion progress and reduce chloride concentration in pumping wells, and (5) reducing cones of depression by relocating and dispersing pumping wells to different sands. The study utilizes optimization techniques and newest LSU high performance computing (HPC) facilities to derive solutions. The conjunctive management framework serves as a scientific tool to assist policy makers to solve the urgent saltwater encroachment issue in the Baton Rouge area. The research results will help water companies as well as industries in East Baton Rouge Parish and neighboring parishes by reducing their saltwater intrusion threats, which in turn would sustain Capital Area economic development.

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

International Nuclear Information System (INIS)

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

1978-03-01

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

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

Science.gov (United States)

McEvoy, Thomas Richard; Wolthusen, Stephen D.

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

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

Science.gov (United States)

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

2002-05-01

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

A two-tier system for web attack detection using linear discriminant method

NARCIS (Netherlands)

Tan, Zhiyuan; Jamdagni, Aruna; Nanda, Priyadarsi; He, Xiangjian; Liu, Ren Ping; Jia, Wenjing; Yeh, Wei-chang

2010-01-01

Computational cost is one of the major concerns of the commercial Intrusion Detection Systems (IDSs). Although these systems are proven to be promising in detecting network attacks, they need to check all the signatures to identify a suspicious attack in the worst case. This is time consuming. This

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.

Multi-Use Non-Intrusive Flow Characterization System (FCS), Phase I

Data.gov (United States)

National Aeronautics and Space Administration — The innovation is a Multi-Use Non-Intrusive Flow Characterization System (FCS) for densified, normal boiling point, and two-phase cryogenic flows, capable of...

Diagnostic Indicators for Shipboard Mechanical Systems Using Non-Intrusive Load Monitoring

National Research Council Canada - National Science Library

McKay, Thomas D

2006-01-01

This thesis examines the use of Non-intrusive Load Monitoring (NILM) in auxiliary shipboard systems, such as a low pressure air system, to determine the state of equipment in larger connected systems, such as the main propulsion engines...

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.

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.

Novel Non-Intrusive Vibration Monitoring System for Turbopumps, Phase I

Data.gov (United States)

National Aeronautics and Space Administration — AI Signal Research, Inc. proposes to develop a Non-Intrusive Vibration Measurement System (NI-VMS) for turbopumps which will provide effective on-board/off-board...

Novel Non-Intrusive Vibration Monitoring System for Turbopumps, Phase II

Data.gov (United States)

National Aeronautics and Space Administration — ASRI proposes to develop an advanced and commercially viable Non-Intrusive Vibration Monitoring System (NI-VMS) which can provide effective on-line/off-line engine...

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

Design and implementation of network attack analysis and detect system

International Nuclear Information System (INIS)

Lu Zhigang; Wu Huan; Liu Baoxu

2007-01-01

This paper first analyzes the present research state of IDS (intrusion detection system), classifies and compares existing methods. According to the problems existing in IDS, such as false-positives, false-negatives and low information visualization, this paper suggests a system named NAADS which supports multi data sources. Through a series of methods such as clustering analysis, association analysis and visualization, rate of detection and usability of NAADS are increased. (authors)

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.

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.

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– ...

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....

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.

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.

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 ...

Notes on saltwater intrusion and trace element distribution in Metro Manila groundwaters

International Nuclear Information System (INIS)

Santos, G. Jr.; Ramos, A.F.; Fernandez, L.G.; Almoneda, R.V.; Garcia, T.Y.; Cruz, C.C.; Petrache, C.A.; Andal, T.T.; Alcantara, E.

1989-01-01

Preliminary analyses of waters for uranium and other trace elements from deepwells operated by the Metropolitan Waterworks and Sewerage System (MWSS) in Metro Manila were performed. Uranium, which ranged from 0.2 ppb to 6 ppb, was correlated with saltwater intrusion. Values >=0.8 ppb for uranium were considered indicative of saline water intrusion in the aquifers. Saline water intrusions in Malabon, Navotas, Paranaque, Las Pinas, Bacoor, Imus, Kawit, Pasig, Antipolo, San Mateo, Taguig, Cainta, Taytay, Alabang and Muntinlupa were noted. Most of these areas were also identified by MWSS as being affected by saltwater intrusion. Tritium values ranged from 0 (below detection limits) to 44 tritium units. Except for one well in Muntinlupa, all the values obtained were below the lower limit of detection of 30.83 T.U. Mercury contents in six well locations had values above the maximum limit set by the National Standards for Drinking Water. Four wells exceeded the permissible level for manganese while two wells had iron concentrations greater than the National Standards. Other trace element concentrations such as Cr, Pb, Zn, Co and Ni either did not exceed their permissible levels or were not included in the National Standards. (Auth.). 6 refs.; 1 tab.; 3 figs

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

Torque Control During Intrusion on Upper Central Incisor in Labial and Lingual bracket System - A 3D Finite Element Study.

Science.gov (United States)

Pol, Tejas R; Vandekar, Meghna; Patil, Anuradha; Desai, Sanjana; Shetty, Vikram; Hazarika, Saptarshi

2018-01-01

The aim of present study was to investigate the difference of torque control during intrusive force on upper central incisors with normal, under and high torque in lingual and labial orthodontic systems through 3D finite element analysis. Six 3D models of an upper right central incisor with different torque were designed in Solid Works 2006. Software ANSYS Version 16.0 was used to evaluate intrusive force on upper central incisor model . An intrusive force of 0.15 N was applied to the bracket slot in different torque models and the displacements along a path of nodes in the upper central incisor was assessed. On application of Intrusive force on under torqued upper central incisor in Labial system produce labial crown movement but in Lingual system caused lingual movement in the apical and incisal parts. The same intrusive force in normal-torqued central incisor led to a palatal movement in apical and labial displacement of incisal edge in Lingual system and a palatal displacement in apical area and a labial movement in the incisal edge in Labial systemin. In overtorqued upper central incisor, the labial crown displacement in Labial system is more than Lingual system. In labial and lingual system on application of the same forces in upper central incisor with different inclinations showed different responses. The magnitudes of torque Loss during intrusive loads in incisors with normal, under and over-torque were higher in Labial system than Lingual orthodontic appliances. Key words: FEM, lingual orthodontics, intrusion, torque control, labial bracket systems.

Definition and means of maintaining the process vacuum liquid detection interlock systems portion of the PFP safety envelope

International Nuclear Information System (INIS)

THOMAS, R.J.

1999-01-01

The Process Vacuum Liquid Detection interlock systems prevent intrusion of process liquids into the HEPA filters downstream of demisters No.6 and No.7 during Process Vacuum System operation. This prevents liquid intrusion into the filters, which could cause a criticality. The Safety Envelope (SE) includes the equipment, which detects the presence of liquids in the vacuum headers; isolates the filters; shuts down the vacuum pumps; and alarms the condition. This report identifies the equipment in the SE operating, maintenance, and surveillance procedures needed to maintain the SE equipment; and rationale for exclusion of some equipment and testing from the SE

Morphologic and hemodynamic analysis of dental pulp in dogs after molar intrusion with the skeletal anchorage system.

Science.gov (United States)

Konno, Yuichi; Daimaruya, Takayoshi; Iikubo, Masahiro; Kanzaki, Reiko; Takahashi, Ichiro; Sugawara, Junji; Sasano, Takashi

2007-08-01

We have successfully treated skeletal open bite by intruding posterior teeth with the skeletal anchorage system. Our aim in this study was to morphologically and hemodynamically evaluate the changes in pulp tissues when molars are radically intruded. The mandibular fourth premolars of 9 adult beagle dogs were divided into 3 groups: a sham operated group (n = 6, 3 dogs), 4-month intrusion group (n = 6, 3 dogs), and a further 4-month retention group (n = 6, 3 dogs). We evaluated the morphological changes of the pulp and dentin-the amount of vacuolar degeneration in the odontoblast layer, the predentin width and nervous continuity in the pulp tissue, and the pulpal blood-flow response evoked by electrical stimulation in the dental pulp. Extreme molar intrusion with the skeletal anchorage system caused slight degenerative changes in the pulp tissue, followed by recovery after the orthodontic force was released. Circulatory system and nervous functions were basically maintained during the intrusion, although a certain level of downregulation was observed. These morphologic and functional regressive changes in the pulp tissue after molar intrusion improved during the retention period. Histologic changes and changes in pulpal blood flow and function are reversible, even during radical intrusion of molars.

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.

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.

Incipient Transient Detection in Reactor Systems: Experimental and Theoretical Investigation

Energy Technology Data Exchange (ETDEWEB)

Lefteri H. Tsoukalas; S.T. Revankar; X Wang; R. Sattuluri

2005-09-27

The main goal of this research was to develop a method for detecting reactor system transients at the earliest possible time through a comprehensive experimental, testing and benchmarking program. This approach holds strong promise for developing new diagnostic technologies that are non-intrusive, generic and highly portable across different systems. It will help in the design of new generation nuclear power reactors, which utilize passive safety systems with a reliable and non-intrusive multiphase flow diagnostic system to monitor the function of the passive safety systems. The main objective of this research was to develop an improved fuzzy logic based detection method based on a comprehensive experimental testing program to detect reactor transients at the earliest possible time, practically at their birth moment. A fuzzy logic and neural network based transient identification methodology and implemented in a computer code called PROTREN was considered in this research and was compared with SPRT (Sequentially Probability Ratio Testing) decision and Bayesian inference. The project involved experiment, theoretical modeling and a thermal-hydraulic code assessment. It involved graduate and undergraduate students participation providing them with exposure and training in advanced reactor concepts and safety systems. In this final report, main tasks performed during the project period are summarized and the selected results are presented. Detailed descriptions for the tasks and the results are presented in previous yearly reports (Revankar et al 2003 and Revankar et al 2004).

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.

An automated computer misuse detection system for UNICOS

Energy Technology Data Exchange (ETDEWEB)

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

1994-09-27

An effective method for detecting computer misuse is the automatic monitoring and analysis of on-line user activity. This activity is reflected in the system audit record, in the system vulnerability posture, and in other evidence found through active testing of the system. During the last several years we have implemented an automatic misuse detection system at Los Alamos. This is the Network Anomaly Detection and Intrusion Reporter (NADIR). We are currently expanding NADIR to include processing of the Cray UNICOS operating system. This new component is called the UNICOS Realtime NADIR, or UNICORN. UNICORN 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. The first phase of UNICORN development is nearing completion, and will be operational in late 1994.

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...

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...

New device to measure dynamic intrusion/extrusion cycles of lyophobic heterogeneous systems.

Science.gov (United States)

Guillemot, Ludivine; Galarneau, Anne; Vigier, Gérard; Abensur, Thierry; Charlaix, Élisabeth

2012-10-01

Lyophobic heterogeneous systems (LHS) are made of mesoporous materials immersed in a non-wetting liquid. One application of LHS is the nonlinear damping of high frequency vibrations. The behaviour of LHS is characterized by P - ΔV cycles, where P is the pressure applied to the system, and ΔV its volume change due to the intrusion of the liquid into the pores of the material, or its extrusion out of the pores. Very few dynamic studies of LHS have been performed until now. We describe here a new apparatus that allows us to carry out dynamic intrusion/extrusion cycles with various liquid/porous material systems, controlling the temperature from ambient to 120 °C and the frequency from 0.01 to 20 Hz. We show that for two LHS: water/MTS and Galinstan/CPG, the energy dissipated during one cycle depends very weakly on the cycle frequency, in strong contrast to conventional dampers.

New Non-Intrusive Inspection Technologies for Nuclear Security and Nonproliferation

Science.gov (United States)

Ledoux, Robert J.

2015-10-01

Comprehensive monitoring of the supply chain for nuclear materials has historically been hampered by non-intrusive inspection systems that have such large false alarm rates that they are impractical in the flow of commerce. Passport Systems, Inc. (Passport) has developed an active interrogation system which detects fissionable material, high Z material, and other contraband in land, sea and air cargo. Passport's design utilizes several detection modalities including high resolution imaging, passive radiation detection, effective-Z (EZ-3D™) anomaly detection, Prompt Neutrons from Photofission (PNPF), and Nuclear Resonance Fluorescence (NRF) isotopic identification. These technologies combine to: detect fissionable, high-Z, radioactive and contraband materials, differentiate fissionable materials from high-Z shielding materials, and isotopically identify actinides, Special Nuclear Materials (SNM), and other contraband (e.g. explosives, drugs, nerve agents). Passport's system generates a 3-D image of the scanned object which contains information such as effective-Z and density, as well as a 2-D image and isotopic and fissionable information for regions of interest.

Cyber-intrusion Auto-response and Policy Management System (CAPMS)

Energy Technology Data Exchange (ETDEWEB)

Lusk, Steve [ViaSat Inc., Boston, MA (United States); Lawrence, David [Duke Energy, Charlotte, NC (United States); Suvana, Prakash [Southern California Edison, Rosemead, CA (United States)

2015-11-11

The Cyber-intrusion Auto-response and Policy Management System (CAPMS) project was funded by a grant from the US Department of Energy (DOE) Cybersecurity for Energy Delivery Systems (CEDS) program with contributions from two partner electric utilities: Southern California Edison (SCE) and Duke Energy. The goal of the project was to demonstrate protecting smart grid assets from a cyber attack in a way that “does not impede critical energy delivery functions.” This report summarizes project goals and activities for the CAPMS project and explores what did and did not work as expected. It concludes with an assessment of possible benefits and value of the system for the future.

REAL-TIME INTELLIGENT MULTILAYER ATTACK CLASSIFICATION SYSTEM

Directory of Open Access Journals (Sweden)

T. Subbhulakshmi

2014-01-01

Full Text Available Intrusion Detection Systems (IDS takes the lion’s share of the current security infrastructure. Detection of intrusions is vital for initiating the defensive procedures. Intrusion detection was done by statistical and distance based methods. A threshold value is used in these methods to indicate the level of normalcy. When the network traffic crosses the level of normalcy then above which it is flagged as anomalous. When there are occurrences of new intrusion events which are increasingly a key part of system security, the statistical techniques cannot detect them. To overcome this issue, learning techniques are used which helps in identifying new intrusion activities in a computer system. The objective of the proposed system designed in this paper is to classify the intrusions using an Intelligent Multi Layered Attack Classification System (IMLACS which helps in detecting and classifying the intrusions with improved classification accuracy. The intelligent multi layered approach contains three intelligent layers. The first layer involves Binary Support Vector Machine classification for detecting the normal and attack. The second layer involves neural network classification to classify the attacks into classes of attacks. The third layer involves fuzzy inference system to classify the attacks into various subclasses. The proposed IMLACS can be able to detect an intrusion behavior of the networks since the system contains a three intelligent layer classification and better set of rules. Feature selection is also used to improve the time of detection. The experimental results show that the IMLACS achieves the Classification Rate of 97.31%.

Effects of igneous intrusions on the petroleum system: a review

NARCIS (Netherlands)

Senger, Kim; Millett, John; Planke, Sverre; Ogata, Kei; Eide, Christian Haug; Festøy, Marte; Galland, Olivier; Jerram, Dougal A.

2017-01-01

Igneous intrusions feature in many sedimentary basins where hydrocarbon exploration and production is continuing. Owing to distinct geophysical property contrasts with siliciclastic host rocks (e.g., higher Vp, density and resistivity than host rocks), intrusions can be easily delineated within data

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.

Statistical Feature Extraction for Fault Locations in Nonintrusive Fault Detection of Low Voltage Distribution Systems

Directory of Open Access Journals (Sweden)

Hsueh-Hsien Chang

2017-04-01

Full Text Available This paper proposes statistical feature extraction methods combined with artificial intelligence (AI approaches for fault locations in non-intrusive single-line-to-ground fault (SLGF detection of low voltage distribution systems. The input features of the AI algorithms are extracted using statistical moment transformation for reducing the dimensions of the power signature inputs measured by using non-intrusive fault monitoring (NIFM techniques. The data required to develop the network are generated by simulating SLGF using the Electromagnetic Transient Program (EMTP in a test system. To enhance the identification accuracy, these features after normalization are given to AI algorithms for presenting and evaluating in this paper. Different AI techniques are then utilized to compare which identification algorithms are suitable to diagnose the SLGF for various power signatures in a NIFM system. The simulation results show that the proposed method is effective and can identify the fault locations by using non-intrusive monitoring techniques for low voltage distribution systems.

An Immune-inspired Adaptive Automated Intrusion Response System Model

Directory of Open Access Journals (Sweden)

Ling-xi Peng

2012-09-01

Full Text Available An immune-inspired adaptive automated intrusion response system model, named as , is proposed. The descriptions of self, non-self, immunocyte, memory detector, mature detector and immature detector of the network transactions, and the realtime network danger evaluation equations are given. Then, the automated response polices are adaptively performed or adjusted according to the realtime network danger. Thus, not only accurately evaluates the network attacks, but also greatly reduces the response times and response costs.

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.

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.

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.

Mass memory formatter subsystem of the adaptive intrusion data system

International Nuclear Information System (INIS)

Corlis, N.E.

1980-09-01

The Mass Memory Formatter was developed as part of the Adaptive Intrusion Data System (AIDS) to control a 2.4-megabit mass memory. The data from a Memory Controlled Processor is formatted before it is stored in the memory and reformatted during the readout mode. The data is then transmitted to a NOVA 2 minicomputer-controlled magnetic tape recorder for storage. Techniques and circuits are described

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

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.

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

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

Intrusion Prevention and Detection in Grid Computing - The ALICE Case

Science.gov (United States)

Gomez, Andres; Lara, Camilo; Kebschull, Udo

2015-12-01

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

Intrusion Prevention and Detection in Grid Computing - The ALICE Case

International Nuclear Information System (INIS)

Gomez, Andres; Lara, Camilo; Kebschull, Udo

2015-01-01

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

A New Anomaly Detection System for School Electricity Consumption Data

Directory of Open Access Journals (Sweden)

Wenqiang Cui

2017-11-01

Full Text Available Anomaly detection has been widely used in a variety of research and application domains, such as network intrusion detection, insurance/credit card fraud detection, health-care informatics, industrial damage detection, image processing and novel topic detection in text mining. In this paper, we focus on remote facilities management that identifies anomalous events in buildings by detecting anomalies in building electricity consumption data. We investigated five models within electricity consumption data from different schools to detect anomalies in the data. Furthermore, we proposed a hybrid model that combines polynomial regression and Gaussian distribution, which detects anomalies in the data with 0 false negative and an average precision higher than 91%. Based on the proposed model, we developed a data detection and visualization system for a facilities management company to detect and visualize anomalies in school electricity consumption data. The system is tested and evaluated by facilities managers. According to the evaluation, our system has improved the efficiency of facilities managers to identify anomalies in the data.

Feature Selection of Network Intrusion Data using Genetic Algorithm and Particle Swarm Optimization

Directory of Open Access Journals (Sweden)

Iwan Syarif

2016-12-01

Full Text Available This paper describes the advantages of using Evolutionary Algorithms (EA for feature selection on network intrusion dataset. Most current Network Intrusion Detection Systems (NIDS are unable to detect intrusions in real time because of high dimensional data produced during daily operation. Extracting knowledge from huge data such as intrusion data requires new approach. The more complex the datasets, the higher computation time and the harder they are to be interpreted and analyzed. This paper investigates the performance of feature selection algoritms in network intrusiona data. We used Genetic Algorithms (GA and Particle Swarm Optimizations (PSO as feature selection algorithms. When applied to network intrusion datasets, both GA and PSO have significantly reduces the number of features. Our experiments show that GA successfully reduces the number of attributes from 41 to 15 while PSO reduces the number of attributes from 41 to 9. Using k Nearest Neighbour (k-NN as a classifier,the GA-reduced dataset which consists of 37% of original attributes, has accuracy improvement from 99.28% to 99.70% and its execution time is also 4.8 faster than the execution time of original dataset. Using the same classifier, PSO-reduced dataset which consists of 22% of original attributes, has the fastest execution time (7.2 times faster than the execution time of original datasets. However, its accuracy is slightly reduced 0.02% from 99.28% to 99.26%. Overall, both GA and PSO are good solution as feature selection techniques because theyhave shown very good performance in reducing the number of features significantly while still maintaining and sometimes improving the classification accuracy as well as reducing the computation time.

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.

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

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...

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.

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.

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)

Number of Waste Package Hit by Igneous Intrusion

International Nuclear Information System (INIS)

M. Wallace

2004-01-01

The purpose of this scientific analysis report is to document calculations of the number of waste packages that could be damaged in a potential future igneous event through a repository at Yucca Mountain. The analyses include disruption from an intrusive igneous event and from an extrusive volcanic event. This analysis supports the evaluation of the potential consequences of future igneous activity as part of the total system performance assessment for the license application (TSPA-LA) for the Yucca Mountain Project (YMP). Igneous activity is a disruptive event that is included in the TSPA-LA analyses. Two igneous activity scenarios are considered: (1) The igneous intrusion groundwater release scenario (also called the igneous intrusion scenario) considers the in situ damage to waste packages or failure of waste packages that occurs if they are engulfed or otherwise affected by magma as a result of an igneous intrusion. (2) The volcanic eruption scenario depicts the direct release of radioactive waste due to an intrusion that intersects the repository followed by a volcanic eruption at the surface. An igneous intrusion is defined as the ascent of a basaltic dike or dike system (i.e., a set or swarm of multiple dikes comprising a single intrusive event) to repository level, where it intersects drifts. Magma that does reach the surface from igneous activity is an eruption (or extrusive activity) (Jackson 1997 [DIRS 109119], pp. 224, 333). The objective of this analysis is to develop a probabilistic measure of the number of waste packages that could be affected by each of the two scenarios

Protecting against cyber threats in networked information systems

Science.gov (United States)

Ertoz, Levent; Lazarevic, Aleksandar; Eilertson, Eric; Tan, Pang-Ning; Dokas, Paul; Kumar, Vipin; Srivastava, Jaideep

2003-07-01

This paper provides an overview of our efforts in detecting cyber attacks in networked information systems. Traditional signature based techniques for detecting cyber attacks can only detect previously known intrusions and are useless against novel attacks and emerging threats. Our current research at the University of Minnesota is focused on developing data mining techniques to automatically detect attacks against computer networks and systems. This research is being conducted as a part of MINDS (Minnesota Intrusion Detection System) project at the University of Minnesota. Experimental results on live network traffic at the University of Minnesota show that the new techniques show great promise in detecting novel intrusions. In particular, during the past few months our techniques have been successful in automatically identifying several novel intrusions that could not be detected using state-of-the-art tools such as SNORT.

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

Science.gov (United States)

2004-01-01

login identity to the one under which the system call is executed, the parameters of the system call execution - file names including full path...Anomaly detection COAST-EIMDT Distributed on target hosts EMERALD Distributed on target hosts and security servers Signature recognition Anomaly...uses a centralized architecture, and employs an anomaly detection technique for intrusion detection. The EMERALD project [80] proposes a

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

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

Science.gov (United States)

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

2013-09-01

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

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.

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

Science.gov (United States)

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

2012-09-01

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

Linking precious metal enrichment and halogen cycling in mafic magmatic systems: insights from the Rum layered intrusion, NW Scotland

Science.gov (United States)

Kelly, A. P.; O'Driscoll, B.; Clay, P. L.; Burgess, R.

2017-12-01

Layered intrusions host the world's largest known concentrations of the platinum-group elements (PGE). Emphasis has been attached to the role of halogen-bearing fluids in concentrating the precious metals, but whether this occurs at the magmatic stage, or via subsequent metasomatism, is actively debated. One obstacle to progress has been the analytical difficulty of measuring low abundances of the halogens in the cumulate products of layered intrusions. To elucidate the importance of the halogens in facilitating PGE-mineralisation, as well as fingerprint halogen provenance and assess the importance of halogen cycling in mafic magma systems more generally, a suite of samples encompassing different stages of activity of the Palaeogene Rum layered intrusion was investigated. Halogen abundances were measured by neutron irradiation noble gas mass spectrometric analysis, permitting the detection of relatively low (ppm-ppb) abundances of Cl, Br and I in mg-sized samples. The samples include PGE-enriched chromite seams, various cumulates (e.g., peridotites), picrites (approximating the Rum parental magma), and pegmatites representing volatile-rich melts that circulated the intrusion at a late-stage in its solidification history. The new data reveal that PGE-bearing chromite seams contain relatively low Cl concentrations (2-3 ppm), with high molar ratios of Br/Cl and I/Cl (0.005 and 0.009, respectively). The picrites and cumulates have Br/Cl and I/Cl ratios close to sub-continental lithospheric mantle values of approximately 0.0013 and 0.00002, respectively, and thus likely reflect the Rum magma source region. A positive correlation between Cl and Br signifies comparable partitioning behaviour in all samples. However, I is more variable, displaying a positive correlation with Cl for more primitive samples (e.g. picrite and peridotite), and seemingly decoupling from Br and Cl in chromite seams and pegmatites. The relative enrichment of I over Cl in the chromite seams points

Monitoring and Assessment of Saltwater Intrusion using Geographic Information Systems (GIS), Remote Sensing and Geophysical measurements of Guimaras Island, Philippines

Science.gov (United States)

Hernandez, B. C. B.

2015-12-01

Degrading groundwater quality due to saltwater intrusion is one of the key challenges affecting many island aquifers. These islands hold limited capacity for groundwater storage and highly dependent on recharge due to precipitation. But its ease of use, natural storage and accessibility make it more vulnerable to exploitation and more susceptible to encroachment from its surrounding oceanic waters. Estimating the extent of saltwater intrusion and the state of groundwater resources are important in predicting and managing water supply options for the community. In Guimaras island, central Philippines, increasing settlements, agriculture and tourism are causing stresses on its groundwater resource. Indications of saltwater intrusion have already been found at various coastal areas in the island. A Geographic Information Systems (GIS)-based approach using the GALDIT index was carried out. This includes six parameters assessing the seawater intrusion vulnerability of each hydrogeologic setting: Groundwater occurrence, Aquifer hydraulic conductivity, Groundwater Level above sea, Distance to shore, Impact of existing intrusion and Thickness of Aquifer. To further determine the extent of intrusion, Landsat images of various thematic layers were stacked and processed for unsupervised classification and electrical resistivity tomography using a 28-electrode system with array lengths of 150 and 300 meters was conducted. The GIS index showed where the vulnerable areas are located, while the geophysical measurements and images revealed extent of seawater encroachment along the monitoring wells. These results are further confirmed by the measurements collected from the monitoring wells. This study presents baseline information on the state of groundwater resources and increase understanding of saltwater intrusion dynamics in island ecosystems by providing a guideline for better water resource management in the Philippines.

Identifying seawater intrusion in coastal areas by means of 1D and quasi-2D joint inversion of TDEM and VES data

Science.gov (United States)

Martínez-Moreno, F. J.; Monteiro-Santos, F. A.; Bernardo, I.; Farzamian, M.; Nascimento, C.; Fernandes, J.; Casal, B.; Ribeiro, J. A.

2017-09-01

Seawater intrusion is an increasingly widespread problem in coastal aquifers caused by climate changes -sea-level rise, extreme phenomena like flooding and droughts- and groundwater depletion near to the coastline. To evaluate and mitigate the environmental risks of this phenomenon it is necessary to characterize the coastal aquifer and the salt intrusion. Geophysical methods are the most appropriate tool to address these researches. Among all geophysical techniques, electrical methods are able to detect seawater intrusions due to the high resistivity contrast between saltwater, freshwater and geological layers. The combination of two or more geophysical methods is recommended and they are more efficient when both data are inverted jointly because the final model encompasses the physical properties measured for each methods. In this investigation, joint inversion of vertical electric and time domain soundings has been performed to examine seawater intrusion in an area within the Ferragudo-Albufeira aquifer system (Algarve, South of Portugal). For this purpose two profiles combining electrical resistivity tomography (ERT) and time domain electromagnetic (TDEM) methods were measured and the results were compared with the information obtained from exploration drilling. Three different inversions have been carried out: single inversion of the ERT and TDEM data, 1D joint inversion and quasi-2D joint inversion. Single inversion results identify seawater intrusion, although the sedimentary layers detected in exploration drilling were not well differentiated. The models obtained with 1D joint inversion improve the previous inversion due to better detection of sedimentary layer and the seawater intrusion appear to be better defined. Finally, the quasi-2D joint inversion reveals a more realistic shape of the seawater intrusion and it is able to distinguish more sedimentary layers recognised in the exploration drilling. This study demonstrates that the quasi-2D joint

A New Generic Taxonomy on Hybrid Malware Detection Technique

OpenAIRE

Robiah, Y.; Rahayu, S. Siti; Zaki, M. Mohd; Shahrin, S.; Faizal, M. A.; Marliza, R.

2009-01-01

Malware is a type of malicious program that replicate from host machine and propagate through network. It has been considered as one type of computer attack and intrusion that can do a variety of malicious activity on a computer. This paper addresses the current trend of malware detection techniques and identifies the significant criteria in each technique to improve malware detection in Intrusion Detection System (IDS). Several existing techniques are analyzing from 48 various researches and...

Bald Mountain gold mining district, Nevada: A Jurassic reduced intrusion-related gold system

Science.gov (United States)

Nutt, C.J.; Hofstra, A.H.

2007-01-01

The Bald Mountain mining district has produced about 2 million ounces (Moz) of An. Geologic mapping, field relationships, geochemical data, petrographic observations, fluid inclusion characteristics, and Pb, S, O, and H isotope data indicate that An mineralization was associated with a reduced Jurassic intrusion. Gold deposits are localized within and surrounding a Jurassic (159 Ma) quartz monzonite porphyry pluton and dike complex that intrudes Cambrian to Mississippian carbonate and clastic rocks. The pluton, associated dikes, and An mineralization were controlled by a crustal-scale northwest-trending structure named the Bida trend. Gold deposits are localized by fracture networks in the pluton and the contact metamorphic aureole, dike margins, high-angle faults, and certain strata or shale-limestone contacts in sedimentary rocks. Gold mineralization was accompanied by silicification and phyllic alteration, ??argillic alteration at shallow levels. Although An is typically present throughout, the system exhibits a classic concentric geochemical zonation pattern with Mo, W, Bi, and Cu near the center, Ag, Pb, and Zn at intermediate distances, and As and Sb peripheral to the intrusion. Near the center of the system, micron-sized native An occurs with base metal sulfides and sulfosalts. In peripheral deposits and in later stages of mineralization, Au is typically submicron in size and resides in pyrite or arsenopyrite. Electron microprobe and laser ablation ICP-MS analyses show that arsenopyrite, pyrite, and Bi sulfide minerals contain 10s to 1,000s of ppm Au. Ore-forming fluids were aqueous and carbonic at deep levels and episodically hypersaline at shallow levels due to boiling. The isotopic compositions of H and O in quartz and sericite and S and Pb in sulfides are indicative of magmatic ore fluids with sedimentary sulfur. Together, the evidence suggests that Au was introduced by reduced S-bearing magmatic fluids derived from a reduced intrusion. The reduced

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...

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.

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...

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.

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

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

International Nuclear Information System (INIS)

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

2009-01-01

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

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.

Sequence-aware intrusion detection in industrial control systems

NARCIS (Netherlands)

Caselli, M.; Zambon, Emmanuele; Kargl, Frank; Zhou, Jianying; Jones, D.

Nowadays, several threats endanger cyber-physical systems. Among these systems, industrial control systems (ICS) operating on critical infrastructures have been proven to be an attractive target for attackers. The case of Stuxnet has not only showed that ICSs are vulnerable to cyber-attacks, but

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

Directory of Open Access Journals (Sweden)

Kyoo Nam Choi

2018-01-01

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

Oxygen isotope mapping and evaluation of paleo-hydrothermal systems associated with synvolcanic intrusion and VMS deposits

International Nuclear Information System (INIS)

Taylor, B.E

2001-01-01

Whole-rock oxygen isotope mapping provides a useful method for the delineation and quantitative evaluation of paleo-hydrothermal systems associated with syn-volcanic intrusions and volcanic-associated massive sulfide (VMS) deposits. During the course of a four-year study of regional alteration systems associated with VMS Deposits, four syn-volcanic intrusive complexes in Canada were mapped using stable isotope techniques. The complexes included Noranda, Quebec; Clifford-Ben Nevis, Ontario; Snow Lake, Manitoba, and Sturgeon Lake, Ontario. This study was regional in extent, involving large areas and large numbers of whole-rock samples: Noranda (625 km 2 ;≥600 samples, plus others (total = 1198); Sturgeon Lake (525 km 2 ; 452 samples); Clifford-Ben Nevis (160 km 2 ; 251 samples); and Snow Lake (84 km 2 ; 575 samples). Isotopic data on whole-rock carbonates and hydrous minerals were also collected. The regional isotopic studies were carried out in concert with other studies on mineral assemblages and mineral composition, and on associated intrusive and extrusive rocks. The Clifford-Ben Nevis area was selected as a control area, in as much as it contains no known VMS deposits; all other areas are well-known, productive VMS districts. Oxygen isotope maps are, in a sense, thermal maps, illustrating the paleo-distribution of heat and fluids, and offering a potential aid to exploration. The isotopic data may be contoured to reveal zones of 18 O depletion and enrichment, relative to unaltered rocks. Zones of δ 18 O≤60% comprise rocks that have reacted with seawater at high (e.g., 300+ o C) temperatures. The volume of foot-wall rocks isotopically-depleted by water/rock interaction during the life of one or more episodes of submarine hydrothermal activity is proportional to the amount of heat available from the syn-volcanic intrusive center. These altered rocks comprise the reaction zone often inferred to have supplied metals and other constituents for the VMS deposits

Prospective relations between intrusive parenting and child behavior problems: Differential moderation by parasympathetic nervous system regulation and child sex.

Science.gov (United States)

Rudd, Kristen L; Alkon, Abbey; Yates, Tuppett M

2017-10-15

This study examined children's parasympathetic nervous system (PNS) regulation, which was indexed by respiratory sinus arrhythmia (RSA) during rest, reactivity, and recovery episodes, and sex as moderators of predicted relations between observed intrusive parenting and later observer-rated child behavior problems. Child-caregiver dyads (N=250; 50% girls; 46% Latino/a) completed a series of laboratory assessments yielding independent measures of intrusive parenting at age 4, PNS regulation at age 6, and child behavior problems at age 8. Results indicated that intrusive parenting was related to more internalizing problems among boys who showed low RSA reactivity (i.e., PNS withdrawal from pre-startle to startle challenge), but RSA reactivity did not moderate this relation among girls. Interestingly, RSA recovery (i.e., PNS activation from startle challenge to post-startle) moderated these relations differently for boys and girls. For girls with relatively low RSA post-startle (i.e., less recovery), intrusive parenting was positively related to both internalizing and externalizing problems. However, the reverse was true for boys, such that there was a significant positive relation between intrusive parenting and later externalizing problems among boys who evidenced relatively high RSA post-startle (i.e., more recovery). Findings provide evidence for the moderation of intrusive caregiving effects by children's PNS regulation while highlighting the differential patterning of these relations across distinct phases of the regulatory response and as a function of child sex. Copyright © 2017 Elsevier Inc. All rights reserved.

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.

Fuzzy Kernel k-Medoids algorithm for anomaly detection problems

Science.gov (United States)

Rustam, Z.; Talita, A. S.

2017-07-01

Intrusion Detection System (IDS) is an essential part of security systems to strengthen the security of information systems. IDS can be used to detect the abuse by intruders who try to get into the network system in order to access and utilize the available data sources in the system. There are two approaches of IDS, Misuse Detection and Anomaly Detection (behavior-based intrusion detection). Fuzzy clustering-based methods have been widely used to solve Anomaly Detection problems. Other than using fuzzy membership concept to determine the object to a cluster, other approaches as in combining fuzzy and possibilistic membership or feature-weighted based methods are also used. We propose Fuzzy Kernel k-Medoids that combining fuzzy and possibilistic membership as a powerful method to solve anomaly detection problem since on numerical experiment it is able to classify IDS benchmark data into five different classes simultaneously. We classify IDS benchmark data KDDCup'99 data set into five different classes simultaneously with the best performance was achieved by using 30 % of training data with clustering accuracy reached 90.28 percent.

Evaluation of of μ-controller PIR Intrusion Detector | Eludire | West ...

African Journals Online (AJOL)

When there is intrusion, a piezo speaker beeps and also a visual indicator with light emitting diode blinks to indicate intrusion. For security, cost effectiveness and access control to certain areas of homes, offices and industries this system is a better replacement to human surveillance needed around our valuable goods and ...

A 5-year follow-up case of multiple intrusive luxative injuries

Directory of Open Access Journals (Sweden)

Seema Thakur

2012-01-01

Full Text Available Introduction: Traumatic intrusive luxation is one of the most severe forms of dental injuries, usually affecting the maxillary incisors. The consequence of such an occurrence is a high risk of healing complications such as pulp necrosis, external inflammatory resorption, and external replacement resorption (ankylosis. Case Report: This report presents a case of severe intrusive luxation of multiple anterior teeth in an 11-year-old girl. The teeth were repositioned successfully by endodontic and orthodontic management. The case was monitored for 5 years. Discussion: Depending on the severity of the injury, different clinical approaches for treatment of intrusive luxation may be used. Despite the variety of treatment modalities, rehabilitation of intruded teeth is always a challenge and a multidisciplinary approach is important to achieve a successful result. In this case, intruded teeth were endodontically treated with multiple calcium hydroxide dressings and repositioned orthodontically. The follow-up of such cases is very important as the repair process after intrusion is complex. After 5 years, no clinical or radiographic pathology was detected.

Improved wavelet packet classification algorithm for vibrational intrusions in distributed fiber-optic monitoring systems

Science.gov (United States)

Wang, Bingjie; Pi, Shaohua; Sun, Qi; Jia, Bo

2015-05-01

An improved classification algorithm that considers multiscale wavelet packet Shannon entropy is proposed. Decomposition coefficients at all levels are obtained to build the initial Shannon entropy feature vector. After subtracting the Shannon entropy map of the background signal, components of the strongest discriminating power in the initial feature vector are picked out to rebuild the Shannon entropy feature vector, which is transferred to radial basis function (RBF) neural network for classification. Four types of man-made vibrational intrusion signals are recorded based on a modified Sagnac interferometer. The performance of the improved classification algorithm has been evaluated by the classification experiments via RBF neural network under different diffusion coefficients. An 85% classification accuracy rate is achieved, which is higher than the other common algorithms. The classification results show that this improved classification algorithm can be used to classify vibrational intrusion signals in an automatic real-time monitoring system.

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

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

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....

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.

A DoS/DDoS Attack Detection System Using Chi-Square Statistic Approach

Directory of Open Access Journals (Sweden)

Fang-Yie Leu

2010-04-01

Full Text Available Nowadays, users can easily access and download network attack tools, which often provide friendly interfaces and easily operated features, from the Internet. Therefore, even a naive hacker can also launch a large scale DoS or DDoS attack to prevent a system, i.e., the victim, from providing Internet services. In this paper, we propose an agent based intrusion detection architecture, which is a distributed detection system, to detect DoS/DDoS attacks by invoking a statistic approach that compares source IP addresses' normal and current packet statistics to discriminate whether there is a DoS/DDoS attack. It first collects all resource IPs' packet statistics so as to create their normal packet distribution. Once some IPs' current packet distribution suddenly changes, very often it is an attack. Experimental results show that this approach can effectively detect DoS/DDoS attacks.

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.

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

Analysis of the performance capability of an infrared interior intrusion detector

International Nuclear Information System (INIS)

Dunn, D.R.

1977-01-01

Component performances are required by the LLL assessment procedure for material control and accounting (MC and A) systems. Monitors are an example of an MC and A component whose functions are to process measurements or observations for purposes of detecting abnormalities. This report develops a methodology for characterizing the performance of a class of infrared (IR) interior intrusion monitors or detectors. The methodology is developed around a specific commercial IR detector, the InfrAlarm, manufactured by Barnes Engineering Company (Models 19-124 and 19-115A). Statistical detection models for computing probabilities of detection and false alarms were derived, and the performance capability of the InfrAlarm IR detector was shown using these measures. The results obtained in the performance analysis show that the detection capability of the InfrAlarm is excellent (approx. 1), with very low false alarm rates, for a wide range in target characteristics. These results should be representative and particularly for non-hostile environments

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.

Non-intrusive uncertainty quantification in structural-acoustic systems using polynomial chaos expansion method

Directory of Open Access Journals (Sweden)

Wang Mingjie

2017-01-01

Full Text Available A framework of non-intrusive polynomial chaos expansion method (PC was proposed to investigate the statistic characteristics of the response of structural-acoustic system containing random uncertainty. The PC method does not need to reformulate model equations, and the statistics of the response can be evaluated directly. The results show that compared to the direct Monte Carlo method (MCM based on the original numerical model, the PC method is effective and more efficient.

[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.

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

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.

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.

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.

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

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.

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.

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

OpenAIRE

Zhang, James; Vukotic, Ilija; Gardner, Robert

2018-01-01

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

An Ontology for Identifying Cyber Intrusion Induced Faults in Process Control Systems

Science.gov (United States)

Hieb, Jeffrey; Graham, James; Guan, Jian

This paper presents an ontological framework that permits formal representations of process control systems, including elements of the process being controlled and the control system itself. A fault diagnosis algorithm based on the ontological model is also presented. The algorithm can identify traditional process elements as well as control system elements (e.g., IP network and SCADA protocol) as fault sources. When these elements are identified as a likely fault source, the possibility exists that the process fault is induced by a cyber intrusion. A laboratory-scale distillation column is used to illustrate the model and the algorithm. Coupled with a well-defined statistical process model, this fault diagnosis approach provides cyber security enhanced fault diagnosis information to plant operators and can help identify that a cyber attack is underway before a major process failure is experienced.

The use of data-mining techniques for developing effective decisionsupport systems: A case study of simulating the effects ofclimate change on coastal salinity intrusion

Science.gov (United States)

Conrads, Paul; Edwin Roehl, Jr.

2017-01-01

Natural-resource managers and stakeholders face difficult challenges when managing interactions between natural and societal systems. Potential changes in climate could alter interactions between environmental and societal systems and adversely affect the availability of water resources in many coastal communities. The availability of freshwater in coastal streams can be threatened by saltwater intrusion. Even though the collective interests and computer skills of the community of managers, scientists and other stakeholders are quite varied, there is an overarching need for equal access by all to the scientific knowledge needed to make the best possible decisions. This paper describes a decision support system, PRISM-2, developed to evaluate salinity intrusion due to potential climate change along the South Carolina coast in southeastern USA. The decision support system is disseminated as a spreadsheet application and integrates the output of global circulation models, watershed models and salinity intrusion models with real-time databases for simulation, graphical user interfaces, and streaming displays of results. The results from PRISM-2 showed that a 31-cm and 62-cm increase in sea level reduced the daily availability of freshwater supply to a coastal municipal intake by 4% and 12% of the time, respectively. Future climate change projections by a global circulation model showed a seasonal change in salinity intrusion events from the summer to the fall for the majority of events.

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

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...

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.

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....

Management of multiple intrusive luxative injuries: A case report with 7-year follow-up

Directory of Open Access Journals (Sweden)

Seema Thakur

2014-01-01

Full Text Available This report presents a case of severe intrusive luxation of multiple anterior teeth in an 11-year-old girl. The teeth were repositioned successfully by endodontic and orthodontic management. The case was monitored for 7 years. Depending on the severity of the injury, different clinical approaches for treatment of intrusive luxation may be used. Despite the variety of treatment modalities, rehabilitation of intruded teeth is always a challenge and a multidisciplinary approach is important to achieve a successful result. In this case, intruded teeth were endodontically treated with multiple calcium hydroxide dressings and repositioned orthodontically. The follow-up of such cases is very important as the repair process after intrusion is complex. After 7 years, no clinical or radiographic pathology was detected.

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.

Uniqueness for cross-diffusion systems issuing from seawater intrusion problems

Directory of Open Access Journals (Sweden)

Catherine Choquet

2017-10-01

Full Text Available We consider a model mixing sharp and diffuse interface approaches for seawater intrusion phenomenons in confined and unconfined aquifers. More precisely, a phase field model is introduced in the boundary conditions on the virtual sharp interfaces. We thus include in the model the existence of diffuse transition zones but we preserve the simplified structure allowing front tracking. The three-dimensional problem then reduces to a two-dimensional model involving a strongly coupled system of partial differential equations of parabolic and elliptic type describing the evolution of the depth of the interface between salt- and freshwater and the evolution of the freshwater hydraulic head. Assuming a low hydraulic conductivity inside the aquifer, we prove the uniqueness of a weak solution for the model completed with initial and boundary conditions. Thanks to a generalization of a Meyer's regularity result, we establish that the gradient of the solution belongs to the space $L^r$, r>2. This additional regularity combined with the Gagliardo-Nirenberg inequality for r=4 allows to handle the nonlinearity of the system in the proof of uniqueness.

Relationship between vapor intrusion and human exposure to trichloroethylene.

Science.gov (United States)

Archer, Natalie P; Bradford, Carrie M; Villanacci, John F; Crain, Neil E; Corsi, Richard L; Chambers, David M; Burk, Tonia; Blount, Benjamin C

2015-01-01

Trichloroethylene (TCE) in groundwater has the potential to volatilize through soil into indoor air where it can be inhaled. The purpose of this study was to determine whether individuals living above TCE-contaminated groundwater are exposed to TCE through vapor intrusion. We examined associations between TCE concentrations in various environmental media and TCE concentrations in residents. For this assessment, indoor air, outdoor air, soil gas, and tap water samples were collected in and around 36 randomly selected homes; blood samples were collected from 63 residents of these homes. Additionally, a completed exposure survey was collected from each participant. Environmental and blood samples were analyzed for TCE. Mixed model multiple linear regression analyses were performed to determine associations between TCE in residents' blood and TCE in indoor air, outdoor air, and soil gas. Blood TCE concentrations were above the limit of quantitation (LOQ; ≥ 0.012 µg L(-1)) in 17.5% of the blood samples. Of the 36 homes, 54.3%, 47.2%, and >84% had detectable concentrations of TCE in indoor air, outdoor air, and soil gas, respectively. Both indoor air and soil gas concentrations were statistically significantly positively associated with participants' blood concentrations (P = 0.0002 and P = 0.04, respectively). Geometric mean blood concentrations of residents from homes with indoor air concentrations of >1.6 µg m(-3) were approximately 50 times higher than geometric mean blood TCE concentrations in participants from homes with no detectable TCE in indoor air (P < .0001; 95% CI 10.4-236.4). This study confirms the occurrence of vapor intrusion and demonstrates the magnitude of exposure from vapor intrusion of TCE in a residential setting.

Data Fusion to Develop a Driver Drowsiness Detection System with Robustness to Signal Loss

Directory of Open Access Journals (Sweden)

Sajjad Samiee

2014-09-01

Full Text Available This study proposes a drowsiness detection approach based on the combination of several different detection methods, with robustness to the input signal loss. Hence, if one of the methods fails for any reason, the whole system continues to work properly. To choose correct combination of the available methods and to utilize the benefits of methods of different categories, an image processing-based technique as well as a method based on driver-vehicle interaction is used. In order to avoid driving distraction, any use of an intrusive method is prevented. A driving simulator is used to gather real data and then artificial neural networks are used in the structure of the designed system. Several tests were conducted on twelve volunteers while their sleeping situations during one day prior to the tests, were fully under control. Although the impact of the proposed system on the improvement of the detection accuracy is not remarkable, the results indicate the main advantages of the system are the reliability of the detections and robustness to the loss of the input signals. The high reliability of the drowsiness detection systems plays an important role to reduce drowsiness related road accidents and their associated costs.

Effect of winds and waves on salt intrusion in the Pearl River estuary

Science.gov (United States)

Gong, Wenping; Lin, Zhongyuan; Chen, Yunzhen; Chen, Zhaoyun; Zhang, Heng

2018-02-01

Salt intrusion in the Pearl River estuary (PRE) is a dynamic process that is influenced by a range of factors and to date, few studies have examined the effects of winds and waves on salt intrusion in the PRE. We investigate these effects using the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) modeling system applied to the PRE. After careful validation, the model is used for a series of diagnostic simulations. It is revealed that the local wind considerably strengthens the salt intrusion by lowering the water level in the eastern part of the estuary and increasing the bottom landward flow. The remote wind increases the water mixing on the continental shelf, elevates the water level on the shelf and in the PRE and pumps saltier shelf water into the estuary by Ekman transport. Enhancement of the salt intrusion is comparable between the remote and local winds. Waves decrease the salt intrusion by increasing the water mixing. Sensitivity analysis shows that the axial down-estuary wind, is most efficient in driving increases in salt intrusion via wind straining effect.

How stratospheric are deep stratospheric intrusions? LUAMI 2008

Directory of Open Access Journals (Sweden)

T. Trickl

2016-07-01

Full Text Available A large-scale comparison of water-vapour vertical-sounding instruments took place over central Europe on 17 October 2008, during a rather homogeneous deep stratospheric intrusion event (LUAMI, Lindenberg Upper-Air Methods Intercomparison. The measurements were carried out at four observational sites: Payerne (Switzerland, Bilthoven (the Netherlands, Lindenberg (north-eastern Germany, and the Zugspitze mountain (Garmisch-Partenkichen, German Alps, and by an airborne water-vapour lidar system creating a transect of humidity profiles between all four stations. A high data quality was verified that strongly underlines the scientific findings. The intrusion layer was very dry with a minimum mixing ratios of 0 to 35 ppm on its lower west side, but did not drop below 120 ppm on the higher-lying east side (Lindenberg. The dryness hardens the findings of a preceding study (“Part 1”, Trickl et al., 2014 that, e.g., 73 % of deep intrusions reaching the German Alps and travelling 6 days or less exhibit minimum mixing ratios of 50 ppm and less. These low values reflect values found in the lowermost stratosphere and indicate very slow mixing with tropospheric air during the downward transport to the lower troposphere. The peak ozone values were around 70 ppb, confirming the idea that intrusion layers depart from the lowermost edge of the stratosphere. The data suggest an increase of ozone from the lower to the higher edge of the intrusion layer. This behaviour is also confirmed by stratospheric aerosol caught in the layer. Both observations are in agreement with the idea that sections of the vertical distributions of these constituents in the source region were transferred to central Europe without major change. LAGRANTO trajectory calculations demonstrated a rather shallow outflow from the stratosphere just above the dynamical tropopause, for the first time confirming the conclusions in “Part 1” from the Zugspitze CO observations. The

EU-project AEROJET. Non-intrusive measurements of aircraft engine exhaust emissions

Energy Technology Data Exchange (ETDEWEB)

Schaefer, K.; Heland, J. [Fraunhofer-Inst. fuer Atmosphaerische Umweltforschung (IFU), Garmisch-Partenkirchen (Germany); Burrows, R. [Rolls-Royce Ltd. (United Kingdom). Engine Support Lab.; Bernard, M. [AUXITROL, S.A. (France). Aerospace Equipment Div.; Bishop, G. [British Aerospace (United Kingdom). Sowerby Research Centre; Lindermeir, E. [Deutsche Forschungsanstalt fuer Luft- und Raumfahrt e. V. (DLR), Bonn (Germany). Inst. fuer Optoelektronik; Lister, D.H. [Defence and Research Agency, Hants (United Kingdom). Propulsion and Development Dept.; Wiesen, P. [Bergische Univ. Wuppertal (Gesamthochshule) (Germany); Hilton, M. [University of Reading (United Kingdom). Dept. of Physics

1997-12-31

The main goal of the AEROJET programme is to demonstrate the equivalence of remote measurement techniques to conventional extractive methods for both gaseous and particulate measurements. The different remote measurement techniques are compared and calibrated. A demonstrator measurement system for exhaust gases, temperature and particulates including data-analysis software is regarded as result of this project. Non-intrusive measurements are the method of choice within the AEROJET project promising to avoid the disadvantages of the gas sampling techniques which are currently used. Different ground based non-intrusive measurement methods are demonstrated during a final evaluation phase. Several non-intrusive techniques are compared with conventional gas sampling and analysis techniques. (R.P.) 3 refs.

EU-project AEROJET. Non-intrusive measurements of aircraft engine exhaust emissions

Energy Technology Data Exchange (ETDEWEB)

Schaefer, K; Heland, J [Fraunhofer-Inst. fuer Atmosphaerische Umweltforschung (IFU), Garmisch-Partenkirchen (Germany); Burrows, R [Rolls-Royce Ltd. (United Kingdom). Engine Support Lab.; Bernard, M [AUXITROL, S.A. (France). Aerospace Equipment Div.; Bishop, G [British Aerospace (United Kingdom). Sowerby Research Centre; Lindermeir, E [Deutsche Forschungsanstalt fuer Luft- und Raumfahrt e. V. (DLR), Bonn (Germany). Inst. fuer Optoelektronik; Lister, D H [Defence and Research Agency, Hants (United Kingdom). Propulsion and Development Dept.; Wiesen, P [Bergische Univ. Wuppertal (Gesamthochshule) (Germany); Hilton, M [University of Reading (United Kingdom). Dept. of Physics

1998-12-31

The main goal of the AEROJET programme is to demonstrate the equivalence of remote measurement techniques to conventional extractive methods for both gaseous and particulate measurements. The different remote measurement techniques are compared and calibrated. A demonstrator measurement system for exhaust gases, temperature and particulates including data-analysis software is regarded as result of this project. Non-intrusive measurements are the method of choice within the AEROJET project promising to avoid the disadvantages of the gas sampling techniques which are currently used. Different ground based non-intrusive measurement methods are demonstrated during a final evaluation phase. Several non-intrusive techniques are compared with conventional gas sampling and analysis techniques. (R.P.) 3 refs.

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...

Duo: Software Defined Intrusion Tolerant System Using Dual Cluster

Directory of Open Access Journals (Sweden)

Yongjae Lee

2018-01-01

Full Text Available An intrusion tolerant system (ITS is a network security system that is composed of redundant virtual servers that are online only in a short time window, called exposure time. The servers are periodically recovered to their clean state, and any infected servers are refreshed again, so attackers have insufficient time to succeed in breaking into the servers. However, there is a conflicting interest in determining exposure time, short for security and long for performance. In other words, the short exposure time can increase security but requires more servers to run in order to process requests in a timely manner. In this paper, we propose Duo, an ITS incorporated in SDN, which can reduce exposure time without consuming computing resources. In Duo, there are two types of servers: some servers with long exposure time (White server and others with short exposure time (Gray server. Then, Duo classifies traffic into benign and suspicious with the help of SDN/NFV technology that also allows dynamically forwarding the classified traffic to White and Gray servers, respectively, based on the classification result. By reducing exposure time of a set of servers, Duo can decrease exposure time on average. We have implemented the prototype of Duo and evaluated its performance in a realistic environment.

Application of the Concept of Intrusion Tolerant System for Evaluating Cyber Security Enhancements

Energy Technology Data Exchange (ETDEWEB)

Lee, Chanyoung; Seong, Poong Hyun [KAIST, Daejeon (Korea, Republic of)

2016-10-15

One of the major problems is that nuclear industry is in very early stage in dealing with cyber security issues. It is because that cyber security has received less attention compared to other safety problems. In addition, late adoption of digital I and C systems has resulted in lower level of cyber security advancements in nuclear industry than ones in other industries. For the cyber security of NPP I and C systems, many regulatory documents, guides and standards were already published. These documents include cyber security plans, methods for cyber security assessments and comprehensive set of security controls. However, methods which can help assess how much security is improved if a specific security control is applied are not included in these documents. Hence, NPP I and C system designers may encounter difficulties when trying to apply security controls with limited structure and cost. In order to provide useful information about cyber security issues including cyber security enhancements, this paper suggests a framework to evaluate how much cyber security is improved when a specific cyber security enhancement is applied in NPPs. In order to provide useful information about cyber security issues including cyber security enhancements, this paper suggests a framework to evaluate how much cyber security is improved when a specific cyber security enhancement is applied in NPPs. The extent of cyber security improvement caused by security enhancement was defined as reduction ratio of the failure probability to secure the system from cyber-attack as Eq.1. The concept of 'intrusion tolerant system' was applied to not only prevent cyber-attacks but also limit the extent of damage in this study. For applying the concept of intrusion tolerant system to NPP, the event tree was constructed with some assumptions. Cyber security improvement caused by cyber security enhancement can be estimated as Eq.3. By comparing current system to the enhanced system, it is

Application of the Concept of Intrusion Tolerant System for Evaluating Cyber Security Enhancements

International Nuclear Information System (INIS)

Lee, Chanyoung; Seong, Poong Hyun

2016-01-01

One of the major problems is that nuclear industry is in very early stage in dealing with cyber security issues. It is because that cyber security has received less attention compared to other safety problems. In addition, late adoption of digital I and C systems has resulted in lower level of cyber security advancements in nuclear industry than ones in other industries. For the cyber security of NPP I and C systems, many regulatory documents, guides and standards were already published. These documents include cyber security plans, methods for cyber security assessments and comprehensive set of security controls. However, methods which can help assess how much security is improved if a specific security control is applied are not included in these documents. Hence, NPP I and C system designers may encounter difficulties when trying to apply security controls with limited structure and cost. In order to provide useful information about cyber security issues including cyber security enhancements, this paper suggests a framework to evaluate how much cyber security is improved when a specific cyber security enhancement is applied in NPPs. In order to provide useful information about cyber security issues including cyber security enhancements, this paper suggests a framework to evaluate how much cyber security is improved when a specific cyber security enhancement is applied in NPPs. The extent of cyber security improvement caused by security enhancement was defined as reduction ratio of the failure probability to secure the system from cyber-attack as Eq.1. The concept of 'intrusion tolerant system' was applied to not only prevent cyber-attacks but also limit the extent of damage in this study. For applying the concept of intrusion tolerant system to NPP, the event tree was constructed with some assumptions. Cyber security improvement caused by cyber security enhancement can be estimated as Eq.3. By comparing current system to the enhanced system, it is possible to

Intrusive trauma memory: A review and functional analysis

NARCIS (Netherlands)

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

2009-01-01

Our contribution to this special issue focuses on the phenomenon of intrusive trauma memory. While intrusive trauma memories can undoubtedly cause impairment, we argue that they may exist for a potentially adaptive reason. Theory and experimental research on intrusion development are reviewed and

Real-Time Recognition Non-Intrusive Electrical Appliance Monitoring Algorithm for a Residential Building Energy Management System

Directory of Open Access Journals (Sweden)

Kofi Afrifa Agyeman

2015-08-01

Full Text Available The concern of energy price hikes and the impact of climate change because of energy generation and usage forms the basis for residential building energy conservation. Existing energy meters do not provide much information about the energy usage of the individual appliance apart from its power rating. The detection of the appliance energy usage will not only help in energy conservation, but also facilitate the demand response (DR market participation as well as being one way of building energy conservation. However, energy usage by individual appliance is quite difficult to estimate. This paper proposes a novel approach: an unsupervised disaggregation method, which is a variant of the hidden Markov model (HMM, to detect an appliance and its operation state based on practicable measurable parameters from the household energy meter. Performing experiments in a practical environment validates our proposed method. Our results show that our model can provide appliance detection and power usage information in a non-intrusive manner, which is ideal for enabling power conservation efforts and participation in the demand response market.

Effect of winds and waves on salt intrusion in the Pearl River estuary

Directory of Open Access Journals (Sweden)

W. Gong

2018-02-01

Full Text Available Salt intrusion in the Pearl River estuary (PRE is a dynamic process that is influenced by a range of factors and to date, few studies have examined the effects of winds and waves on salt intrusion in the PRE. We investigate these effects using the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST modeling system applied to the PRE. After careful validation, the model is used for a series of diagnostic simulations. It is revealed that the local wind considerably strengthens the salt intrusion by lowering the water level in the eastern part of the estuary and increasing the bottom landward flow. The remote wind increases the water mixing on the continental shelf, elevates the water level on the shelf and in the PRE and pumps saltier shelf water into the estuary by Ekman transport. Enhancement of the salt intrusion is comparable between the remote and local winds. Waves decrease the salt intrusion by increasing the water mixing. Sensitivity analysis shows that the axial down-estuary wind, is most efficient in driving increases in salt intrusion via wind straining effect.

Effective use of surface-water management to control saltwater intrusion

Science.gov (United States)

Hughes, J. D.; White, J.

2012-12-01

The Biscayne aquifer in southeast Florida is susceptible to saltwater intrusion and inundation from rising sea-level as a result of high groundwater withdrawal rates and low topographic relief. Groundwater levels in the Biscayne aquifer are managed by an extensive canal system that is designed to control flooding, supply recharge to municipal well fields, and control saltwater intrusion. We present results from an integrated surface-water/groundwater model of a portion of the Biscayne aquifer to evaluate the ability of the existing managed surface-water control network to control saltwater intrusion. Surface-water stage and flow are simulated using a hydrodynamic model that solves the diffusive-wave approximation of the depth-integrated shallow surface-water equations. Variable-density groundwater flow and fluid density are solved using the Oberbeck--Boussinesq approximation of the three-dimensional variable-density groundwater flow equation and a sharp interface approximation, respectively. The surface-water and variable-density groundwater domains are implicitly coupled during each Picard iteration. The Biscayne aquifer is discretized into a multi-layer model having a 500-m square horizontal grid spacing. All primary and secondary surface-water features in the active model domain are discretized into segments using the 500-m square horizontal grid. A 15-year period of time is simulated and the model includes 66 operable surface-water control structures, 127 municipal production wells, and spatially-distributed daily internal and external hydrologic stresses. Numerical results indicate that the existing surface-water system can be effectively used in many locations to control saltwater intrusion in the Biscayne aquifer resulting from increases in groundwater withdrawals or sea-level rise expected to occur over the next 25 years. In other locations, numerical results indicate surface-water control structures and/or operations may need to be modified to control

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

Rapid laccolith intrusion driven by explosive volcanic eruption.

Science.gov (United States)

Castro, Jonathan M; Cordonnier, Benoit; Schipper, C Ian; Tuffen, Hugh; Baumann, Tobias S; Feisel, Yves

2016-11-23

Magmatic intrusions and volcanic eruptions are intimately related phenomena. Shallow magma intrusion builds subsurface reservoirs that are drained by volcanic eruptions. Thus, the long-held view is that intrusions must precede and feed eruptions. Here we show that explosive eruptions can also cause magma intrusion. We provide an account of a rapidly emplaced laccolith during the 2011 rhyolite eruption of Cordón Caulle, Chile. Remote sensing indicates that an intrusion began after eruption onset and caused severe (>200 m) uplift over 1 month. Digital terrain models resolve a laccolith-shaped body ∼0.8 km 3 . Deformation and conduit flow models indicate laccolith depths of only ∼20-200 m and overpressures (∼1-10 MPa) that likely stemmed from conduit blockage. Our results show that explosive eruptions may rapidly force significant quantities of magma in the crust to build laccoliths. These iconic intrusions can thus be interpreted as eruptive features that pose unique and previously unrecognized volcanic hazards.

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.

An Artificially Intelligent Physical Model-Checking Approach to Detect Switching-Related Attacks on Power Systems

Energy Technology Data Exchange (ETDEWEB)

El Hariri, Mohamad [Florida Intl Univ., Miami, FL (United States); Faddel, Samy [Florida Intl Univ., Miami, FL (United States); Mohammed, Osama [Florida Intl Univ., Miami, FL (United States)

2017-11-01

Decentralized and hierarchical microgrid control strategies have lain the groundwork for shaping the future smart grid. Such control approaches require the cooperation between microgrid operators in control centers, intelligent microcontrollers, and remote terminal units via secure and reliable communication networks. In order to enhance the security and complement the work of network intrusion detection systems, this paper presents an artificially intelligent physical model-checking that detects tampered-with circuit breaker switching control commands whether, due to a cyber-attack or human error. In this technique, distributed agents, which are monitoring sectionalized areas of a given microgrid, will be trained and continuously adapted to verify that incoming control commands do not violate the physical system operational standards and do not put the microgrid in an insecure state. The potential of this approach has been tested by deploying agents that monitor circuit breakers status commands on a 14-bus IEEE benchmark system. The results showed the accuracy of the proposed framework in characterizing the power system and successfully detecting malicious and/or erroneous control commands.

Intrusion of soil covered uranium mill tailings by whitetail prairie dogs and Richardson's ground squirrels

International Nuclear Information System (INIS)

Shuman, R.

1984-01-01

The primary objective of the reclamation of uranium mill tailings is the long-term isolation of the matrial from the biosphere. Fossorial and semi-fossorial species represent a potentially disruptive influence as a result of their burrowing habits. The potential for intrusion was investigated with respect to two sciurids, the whitetail prairie dog (Cynomys leucurus) and Richardson's ground squirrel (Spermophilus richardsonii). Populations of prairie dogs were established on a control area, lacking a tailings layer, and two experimental areas, underlain by a waste layer, in southeastern Wyoming. Weekly measurements of prairie dog mound surface activities were conducted to demonstrate penetration, or lack thereof, of the tailings layer. Additionally, the impact of burrowing upon radon flux was determined. Limited penetration of the waste layer was noted after which frequency of inhabitance of the intruding burrow system declined. No significant changes in radon flux were detected. In another experiment, it was found that Richardson's ground squirrels burrowed to less extreme depths when confronted by mill tailings. Additional work at an inactive tailings pile in western Colorado revealed repeated intrusion through a shallow cover, and subsequent transport of radioactive material to the ground surface by prairie dogs. Radon flux from burrow entrances was significantly greater than that from undisturbed ground. Data suggested that textural and pH properties of tailings material may act to discourage repeated intrusion at some sites. 58 references

Analogue modelling on the interaction between shallow magma intrusion and a strike-slip fault: Application on the Middle Triassic Monzoni Intrusive Complex (Dolomites, Italy)

Science.gov (United States)

Michail, Maria; Coltorti, Massimo; Gianolla, Piero; Riva, Alberto; Rosenau, Matthias; Bonadiman, Costanza; Galland, Olivier; Guldstrand, Frank; Thordén Haug, Øystein; Rudolf, Michael; Schmiedel, Tobias

2017-04-01

The southwestern part of the Dolomites in Northern Italy has undergone a short-lived Ladinian (Middle Triassic) tectono-magmatic event, forming a series of significant magmatic features. These intrusive bodies deformed and metamorphosed the Permo-Triassic carbonate sedimentary framework. In this study we focus on the tectono-magmatic evolution of the shallow shoshonitic Monzoni Intrusive Complex of this Ladinian event (ca 237 Ma), covering an area of 20 km^2. This NW-SE elongated intrusive structure (5 km length) shows an orogenic magmatic affinity which is in contrast to the tectonic regime at the time of intrusion. Strain analysis shows anorogenic transtensional displacement in accordance with the ENE-WSW extensional pattern in the central Dolomites during the Ladinian. Field interpretations led to a detailed description of the regional stratigraphic sequence and the structural features of the study area. However, the geodynamic context of this magmatism and the influence of the inherited strike-slip fault on the intrusion, are still in question. To better understand the specific natural prototype and the general mechanisms of magma emplacement in tectonically active areas, we performed analogue experiments defined by, but not limited to, first order field observations. We have conducted a systematic series of experiments in different tectonic regimes (static conditions, strike-slip, transtension). We varied the ratio of viscous to brittle stresses between magma and country rock, by injecting Newtonian fluids both of high and low viscosity (i.e. silicone oil/vegetable oil) into granular materials of varying cohesion (sand, silica flour, glass beads). The evolving surface and side view of the experiments were monitored by photogrammetric techniques for strain analyses and topographic evolution. In our case, the combination of the results from field and analogue experiments brings new insights regarding the tectonic regime, the geometry of the intrusive body, and

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.

Young women's experiences of intrusive behavior in 12 countries.

Science.gov (United States)

Sheridan, Lorraine; Scott, Adrian J; Roberts, Karl

2016-01-01

The present study provides international comparisons of young women's (N = 1,734) self-reported experiences of intrusive activities enacted by men. Undergraduate psychology students from 12 countries (Armenia, Australia, England, Egypt, Finland, India, Indonesia, Italy, Japan, Portugal, Scotland, and Trinidad) indicated which of 47 intrusive activities they had personally experienced. Intrusive behavior was not uncommon overall, although large differences were apparent between countries when women's personal experiences of specific intrusive activities were compared. Correlations were carried out between self-reported intrusive experiences, the Gender Empowerment Measure (GEM), and Hofstede's dimensions of national cultures. The primary associations were between women's experiences of intrusive behavior and the level of power they are afforded within the 12 countries. Women from countries with higher GEM scores reported experiencing more intrusive activities relating to courtship and requests for sex, while the experiences of women from countries with lower GEM scores related more to monitoring and ownership. Intrusive activities, many of them constituent of harassment and stalking, would appear to be widespread and universal, and their incidence and particular form reflect national level gender inequalities. © 2015 Wiley Periodicals, Inc.

Pukala intrusion, its age and connection to hydrothermal alteration in Orivesi, southwestern Finland

Directory of Open Access Journals (Sweden)

Matti Talikka

2005-01-01

Full Text Available The Pukala intrusion is situated in the Paleoproterozoic Svecofennian domain of the Fennoscandian Shield in the contact region between the Central Finland Granitoid Complex and the Tampere Belt. The acid subvolcanic intrusion, which is in contact or close to severalaltered domains, mainly consists of porphyritic granodiorite and trondhjemite. The Pukala intrusion was emplaced into volcanic sequence in an island-arc or fore-arc setting before or during the early stages of the main regional deformation phase of the Svecofennian orogeny. On the basis of the geochemical data, the Pukala intrusion is a peraluminous volcanic-arc granitoid. After crystallisation at 1896±3 Ma, multiphase deformation and metamorphismcaused alteration, recrystallisation, and orientation of the minerals, and tilted the intrusion steeply towards south. The 1851±5 Ma U-Pb age for titanite is connected to the late stages of the Svecofennian tectonometamorphic evolution of the region. Several hydrothermally altered domains are located in the felsic and intermediate metavolcanic rocks of the Tampere Belt within less than one kilometre south of the Pukala intrusion. Alteration is divided into three basic types: partial silica alteration, chlorite-sericite±silica alteration, and sericite alteration in shear zones. The first two types probably formed during the emplacement and crystallisation of the Pukala intrusion, and the third is linked to late shearing. Intense sericitisation and comb quartz bands in the contact of theintrusion and the altered domain at Kutemajärvi suggest that the hydrothermal system was driven by the Pukala intrusion.

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)

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)

Embedded security system for multi-modal surveillance in a railway carriage

Science.gov (United States)

Zouaoui, Rhalem; Audigier, Romaric; Ambellouis, Sébastien; Capman, François; Benhadda, Hamid; Joudrier, Stéphanie; Sodoyer, David; Lamarque, Thierry

2015-10-01

Public transport security is one of the main priorities of the public authorities when fighting against crime and terrorism. In this context, there is a great demand for autonomous systems able to detect abnormal events such as violent acts aboard passenger cars and intrusions when the train is parked at the depot. To this end, we present an innovative approach which aims at providing efficient automatic event detection by fusing video and audio analytics and reducing the false alarm rate compared to classical stand-alone video detection. The multi-modal system is composed of two microphones and one camera and integrates onboard video and audio analytics and fusion capabilities. On the one hand, for detecting intrusion, the system relies on the fusion of "unusual" audio events detection with intrusion detections from video processing. The audio analysis consists in modeling the normal ambience and detecting deviation from the trained models during testing. This unsupervised approach is based on clustering of automatically extracted segments of acoustic features and statistical Gaussian Mixture Model (GMM) modeling of each cluster. The intrusion detection is based on the three-dimensional (3D) detection and tracking of individuals in the videos. On the other hand, for violent events detection, the system fuses unsupervised and supervised audio algorithms with video event detection. The supervised audio technique detects specific events such as shouts. A GMM is used to catch the formant structure of a shout signal. Video analytics use an original approach for detecting aggressive motion by focusing on erratic motion patterns specific to violent events. As data with violent events is not easily available, a normality model with structured motions from non-violent videos is learned for one-class classification. A fusion algorithm based on Dempster-Shafer's theory analyses the asynchronous detection outputs and computes the degree of belief of each probable event.

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.

Intrusive Memories of Distressing Information: An fMRI Study.

Directory of Open Access Journals (Sweden)

Eva Battaglini

Full Text Available Although intrusive memories are characteristic of many psychological disorders, the neurobiological underpinning of these involuntary recollections are largely unknown. In this study we used functional magentic resonance imaging (fMRI to identify the neural networks associated with encoding of negative stimuli that are subsequently experienced as intrusive memories. Healthy partipants (N = 42 viewed negative and neutral images during a visual/verbal processing task in an fMRI context. Two days later they were assessed on the Impact of Event Scale for occurrence of intrusive memories of the encoded images. A sub-group of participants who reported significant intrusions (n = 13 demonstrated stronger activation in the amygdala, bilateral ACC and parahippocampal gyrus during verbal encoding relative to a group who reported no intrusions (n = 13. Within-group analyses also revealed that the high intrusion group showed greater activity in the dorsomedial (dmPFC and dorsolateral prefrontal cortex (dlPFC, inferior frontal gyrus and occipital regions during negative verbal processing compared to neutral verbal processing. These results do not accord with models of intrusions that emphasise visual processing of information at encoding but are consistent with models that highlight the role of inhibitory and suppression processes in the formation of subsequent intrusive memories.

Biological intrusion of low-level-waste trench covers

International Nuclear Information System (INIS)

Hakonson, T.E.; Gladney, E.S.

1981-01-01

The long-term integrity of low-level waste shallow land burial sites is dependent on the interaction of physical, chemical, and biological factors that modify the waste containment system. Past research on low-level waste shallow land burial methods has emphasized physical (i.e., water infiltration, soil erosion) and chemical (radionuclide leaching) processes that can cause waste site failure and subsequent radionuclide transport. The purpose of this paper is to demonstrate the need to consider biological processes as being potentially important in reducing the integrity of waste burial site cover treatments. Plants and animals not only can transport radionuclides to the ground surface via root systems and soil excavated from the cover profile by animal burrowing activities, but they modify physical and chemical processes within the cover profile by changing the water infiltration rates, soil erosion rates and chemical composition of the soil. One approach to limiting biological intrusion through the waste cover is to apply a barrier within the profile to limit root and animal penetration with depth. Experiments in the Los Alamos Experimental Engineered Test Facility were initiated to develop and evaluate biological barriers that are effective in minimizing intrusion into waste trenches. The experiments that are described employ four different candidate barrier materials of geologic origin. Experimental variables that will be evaluated, in addition to barrier type, are barrier depth and soil overburden depth. The rate of biological intrusion through the various barrier materials is being evaluated through the use of activatable stable tracers

Intrusion Detection in Networked Control Systems: From System Knowledge to Network Security

NARCIS (Netherlands)

Caselli, M.

2016-01-01

“Networked control system‿ (NCS) is an umbrella term encompassing a broad variety of infrastructures such as industrial control systems (ICSs) and building automation systems (BASs). Nowadays, all these infrastructures play an important role in several aspects of our daily life, from managing

Status of physical protection systems of nuclear facilities; survey report

International Nuclear Information System (INIS)

Hwang, In Koo; Kwack, Eun Ho; Ahn, Jin Soo; Lee, Hyun Chul; Kim, Jung Soo

2002-02-01

This report presents a survey on the physical protection equipment for a nuclear power plant. This survey was conducted by Korea Atomic Energy Research Institute as a part of the project, 'Development of Technologies for National Control of and Accountancy for Nuclear Material,' funded by the Ministry of Science and Technology of Korea. A physical protection system of a nuclear plant includes outer and inner fences, intrusion detection sensors, alarm generation system, illumination equipment, central monitoring and control station, entry control and management system, etc. The outermost fence indicates the boundary of the plant area and prevents a simple or unintentional intrusion. The inner fence area of each plant unit associated with intrusion detection sensors, illuminators, monitoring cameras, serves the key role for physical protection function for the nuclear plant

Quantitative evaluation of physical protection system in nuclear power plant

International Nuclear Information System (INIS)

Sun Yahua; Li Bin; Li Shiju

2009-01-01

Based on the prompt detection analysis, this paper introduced one analysis model of intrusion path in nuclear power plant by means of morphology analysis and developed the evaluation software for path model analysis of physical protection system. Quantitative analysis on three elements (detection, delay, and response) of physical protection system was presented with an imaginary intrusion event example in Mac Arthur nuclear center. The results indicated that the path prompt detection analysis worked effectively to find the weak point of the physical protection system in NPP, and meantime we can also get the high cost-effectiveness improved measures. It is an effective approach to evaluate the overall performance of the system. (authors)

Contraband detection with fast neutrons

Energy Technology Data Exchange (ETDEWEB)

Buffler, Andy E-mail: abuffler@science.uct.ac.za

2004-11-01

Recent terror events and the increase in the trade of illicit drugs have fuelled the exploration of the use of fast neutrons as probes for the detection of hidden contraband, especially explosives, in packages ranging in size from small mail items to cargo containers. The various approaches using fast neutrons for contraband detection, presently under development, are reviewed. The role that a neutron system might play in the non-intrusive interrogation of airline luggage is discussed.

Salt water intrusion on Uznam Island - 'Wydrzany' water intake

International Nuclear Information System (INIS)

Kochaniec, M.

1999-01-01

Aquifers of Uznam Island have high risk of saline water intrusion due to geographical and geological location. Hydrogeological and geophysical researchers were taken up in order to evaluate changes in intrusion of saline water into aquifer of Uznam Island. Water intake named 'Wydrzany' was built in south part of island in 1973. Since 1975 geophysical research has shown intrusion of salt water from reservoirs and bedrock due to withdrawn of water. In 1997 geoelectrical researches evaluated changes which have taken place since 1975 in saline water intrusion into aquifers of Uznam Island. The last research result showed that intrusion front moved 1100 m to the centre of island in comparison with situation in 1975. (author)

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

Individual differences in spatial configuration learning predict the occurrence of intrusive memories.

Science.gov (United States)

Meyer, Thomas; Smeets, Tom; Giesbrecht, Timo; Quaedflieg, Conny W E M; Girardelli, Marta M; Mackay, Georgina R N; Merckelbach, Harald

2013-03-01

The dual-representation model of posttraumatic stress disorder (PTSD; Brewin, Gregory, Lipton, & Burgess, Psychological Review, 117, 210-232 2010) argues that intrusions occur when people fail to construct context-based representations during adverse experiences. The present study tested a specific prediction flowing from this model. In particular, we investigated whether the efficiency of temporal-lobe-based spatial configuration learning would account for individual differences in intrusive experiences and physiological reactivity in the laboratory. Participants (N = 82) completed the contextual cuing paradigm, which assesses spatial configuration learning that is believed to depend on associative encoding in the parahippocampus. They were then shown a trauma film. Afterward, startle responses were quantified during presentation of trauma reminder pictures versus unrelated neutral and emotional pictures. PTSD symptoms were recorded in the week following participation. Better configuration learning performance was associated with fewer perceptual intrusions, r = -.33, p .46) and had no direct effect on intrusion-related distress and overall PTSD symptoms, rs > -.12, ps > .29. However, configuration learning performance tended to be associated with reduced physiological responses to unrelated negative images, r = -.20, p = .07. Thus, while spatial configuration learning appears to be unrelated to affective responding to trauma reminders, our overall findings support the idea that the context-based memory system helps to reduce intrusions.

Magmatic intrusions in the lunar crust

Science.gov (United States)

Michaut, C.; Thorey, C.

2015-10-01

The lunar highlands are very old, with ages covering a timespan between 4.5 to 4.2 Gyr, and probably formed by flotation of light plagioclase minerals on top of the lunar magma ocean. The lunar crust provides thus an invaluable evidence of the geological and magmatic processes occurring in the first times of the terrestrial planets history. According to the last estimates from the GRAIL mission, the lunar primary crust is particularly light and relatively thick [1] This low-density crust acted as a barrier for the dense primary mantle melts. This is particularly evident in the fact that subsequent mare basalts erupted primarily within large impact basin: at least part of the crust must have been removed for the magma to reach the surface. However, the trajectory of the magma from the mantle to the surface is unknown. Using a model of magma emplacement below an elastic overlying layer with a flexural wavelength Λ, we characterize the surface deformations induced by the presence of shallow magmatic intrusions. We demonstrate that, depending on its size, the intrusion can show two different shapes: a bell shape when its radius is smaller than 4 times Λ or a flat top with small bended edges if its radius is larger than 4 times Λ[2]. These characteristic shapes for the intrusion result in characteristic deformations at the surface that also depend on the topography of the layer overlying the intrusion [3].Using this model we provide evidence of the presence of intrusions within the crust of the Moon as surface deformations in the form of low-slope lunar domes and floor-fractured craters. All these geological features have morphologies consistent with models of magma spreading at depth and deforming an overlying elastic layer. Further more,at floor-fractured craters, the deformation is contained within the crater interior, suggesting that the overpressure at the origin of magma ascent and intrusion was less than the pressure due to the weight of the crust removed by

Preliminary evaluation of solution-mining intrusion into a salt-dome repository

International Nuclear Information System (INIS)

1981-06-01

This report is the product of the work of an ONWI task force to evaluate inadvertant human intrusion into a salt dome repository by solution mining. It summarizes the work in the following areas: a general review of the levels of defense that could reduce both the likelihood and potential consequences of human intrusion into a salt dome repository; evaluation of a hypothetical intrusion scenario and its consequences; recommendation for further studies. The conclusions of this task force report can be summarized as follows: (1) it is not possible at present to establish with certainty that solution mining is credible as a human-intrusion event. The likelihood of such an intrusion will depend on the effectiveness of the preventive measures; (2) an example analysis based on the realistic approach is presented in this report; it concluded that the radiological consequences are strongly dependent upon the mode of radionuclide release from the waste form, time after emplacement, package design, impurities in the host salt, the amount of a repository intercepted, the solution mining cavity form, the length of time over which solution mining occurs, the proportion of contaminated salt source for human consumption compared to other sources, and the method of salt purification for culinary purposes; (3) worst case scenarios done by other studies suggest considerable potential for exposures to man while preliminary evaluations of more realistic cases suggest significantly reduced potential consequences. Mathematical model applications to process systems, guided by more advanced assumptions about human intrusion into geomedia, will shed more light on the potential for concerns and the degree to which mitigative measures will be required

Corticostriatal circuitry in regulating diseases characterized by intrusive thinking.

Science.gov (United States)

Kalivas, Benjamin C; Kalivas, Peter W

2016-03-01

Intrusive thinking triggers clinical symptoms in many neuropsychiatric disorders. Using drug addiction as an exemplar disorder sustained in part by intrusive thinking, we explore studies demonstrating that impairments in corticostriatal circuitry strongly contribute to intrusive thinking. Neuroimaging studies have long implicated this projection in cue-induced craving to use drugs, and preclinical models show that marked changes are produced at corticostriatal synapses in the nucleus accumbens during a relapse episode. We delineate an accumbens microcircuit that mediates cue-induced drug seeking becoming an intrusive event. This microcircuit harbors many potential therapeutic targets. We focus on preclinical and clinical studies, showing that administering N-acetylcysteine restores uptake of synaptic glutamate by astroglial glutamate transporters and thereby inhibits intrusive thinking. We posit that because intrusive thinking is a shared endophenotype in many disorders, N-acetylcysteine has positive effects in clinical trials for a variety of neuropsychiatric disorders, including drug addiction, gambling, trichotillomania, and depression.

Biological intrusion of low-level-waste trench covers

Science.gov (United States)

Hakonson, T. E.; Gladney, E. S.

The long-term integrity of low-level waste shallow land burialsites is dependent on the interaction of physical, chemical, and biological factors that modify the waste containment system. The need to consider biological processes as being potentially important in reducing the integrity of waste burial site cover treatment is demonstrated. One approach to limiting biological intrusion through the waste cover is to apply a barrier within the profile to limit root and animal penetration with depth. Experiments in the Los Alamos Experimental Engineered Test Facility were initiated to develop and evaluate biological barriers that are effective in minimizing intrusion into waste trenches. The experiments that are described employ four different candidate barrier materials of geologic origin. Experimental variables that will be evaluated, in addition to barrier type, are barrier depth and sil overburden depth.

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

Science.gov (United States)

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

2017-02-11

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

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.

Intrusion Detection in Networked Control Systems: From System Knowledge to Network Security

OpenAIRE

Caselli, M.

2016-01-01

“Networked control system‿ (NCS) is an umbrella term encompassing a broad variety of infrastructures such as industrial control systems (ICSs) and building automation systems (BASs). Nowadays, all these infrastructures play an important role in several aspects of our daily life, from managing essential services such as en- ergy and water (e.g., critical infrastructures) to monitoring the increasingly smart environments that surround us (e.g., the Internet of Things). Over the years, NCS techn...

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

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.

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.

AMDIS Case Conference: Intrusive Medication Safety Alerts.

Science.gov (United States)

Graham, J; Levick, D; Schreiber, R

2010-01-01

Clinical decision support that provides enhanced patient safety at the point of care frequently encounters significant pushback from clinicians who find the process intrusive or time-consuming. We present a hypothetical medical center's dilemma about its allergy alerting system and discuss similar problems faced by real hospitals. We then share some lessons learned and best practices for institutions who wish to implement these tools themselves.

AMDIS Case Conference: Intrusive Medication Safety Alerts

OpenAIRE

Graham, J.; Levick, D.; Schreiber, R.

2010-01-01

Clinical decision support that provides enhanced patient safety at the point of care frequently encounters significant pushback from clinicians who find the process intrusive or time-consuming. We present a hypothetical medical center’s dilemma about its allergy alerting system and discuss similar problems faced by real hospitals. We then share some lessons learned and best practices for institutions who wish to implement these tools themselves.

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

Aspects of cold intrusions over Greece during autumn

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Mita, Constantina; Marinaki, Aggeliki; Zeini, Konstantina; Konstantara, Metaxia

2010-05-01

This study is focused on the description of atmospheric disturbances that caused intense cold intrusions over Greece during autumn for a period of 25 years (1982-2006). The study was based on data analysis from the meteorological station network of the Hellenic National Meteorological Service (HNMS) and the European Centre for Medium Range Weather Forecasts (ECMWF). Initially, the days with temperature at the isobaric surface of 850 hPa less or equal to the mean temperature for the 10-day period the day under investigation belongs to are isolated, composing a new confined data set which was further used. An event of intense cold intrusion is identified based on a subjective set of criteria, considering the temperature decrease at the level of 850 hPa and its duration. In particular, the criteria that were used to identify a cold intrusion were: temperature variation between two successive days at the isobaric level of 850 hPa being equal or greater than 50 C at least once during the event and duration of the event of at least two successive days with continuous temperature decrease. Additionally, the synoptic analysis of the atmospheric disturbances involved using weather charts from ECMWF, revealed that all cases were related to low pressure systems at the level of 500 hPa, accompanied by cold air masses. Moreover, a methodology proposed to classify the cold intrusions based on general circulation characteristics of the atmosphere, resulted in seven major categories. More than half of the events belong in two categories, originated northwest of the greater Greek area (Greece and parts of neighbouring countries), between 400 and 600 N. Further analysis indicated that the frequency of events increases from September to November and the majority of the events lasted two to three days. Additionally, the non-parametric Mann-Kendall test was used for the investigation of the statistical significance of the trends appearing in the results. The tests revealed that over

Intrusions of a drowsy mind: neural markers of phenomenological unpredictability.

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Noreika, Valdas; Canales-Johnson, Andrés; Koh, Justin; Taylor, Mae; Massey, Irving; Bekinschtein, Tristan A

2015-01-01

The transition from a relaxed to a drowsy state of mind is often accompanied by hypnagogic experiences: most commonly, perceptual imagery, but also linguistic intrusions, i.e., the sudden emergence of unpredictable anomalies in the stream of inner speech. This study has sought to describe the contents of such intrusions, to verify their association with the progression of sleep onset, and to investigate the electroencephalographic processes associated with linguistic intrusions as opposed to more common hypnagogic perceptual imagery. A single participant attended 10 experimental sessions in the EEG laboratory, where he was allowed to drift into a drowsy state of mind, while maintaining metacognition of his own experiences. Once a linguistic intrusion or a noticeable perceptual image occurred, the participant pressed a button and reported it verbally. An increase in the EEG-defined depth of drowsiness as assessed by the Hori system of sleep onset was observed in the last 20 s before a button press. Likewise, EEG Dimension of Activation values decreased before the button press, indicating that the occurrence of cognitively incongruous experiences coincides with the rapid change of EEG predictability patterns. EEG hemispheric asymmetry analysis showed that linguistic intrusions had a higher alpha and gamma power in the left hemisphere electrodes, whereas perceptual imagery reports were associated with a higher beta power over the right hemisphere. These findings indicate that the modality as well as the incongruence of drowsiness-related hypnagogic experiences is strongly associated with distinct EEG signatures in this participant. Sleep onset may provide a unique possibility to study the neural mechanisms accompanying the fragmentation of the stream of consciousness in healthy individuals.

Intrusions of a drowsy mind: Neural markers of phenomenological unpredictability

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