Network Anomaly Detection

Network Anomaly Detection
A Machine Learning Perspective

by Dhruba Kumar Bhattacharyya,Jugal Kumar Kalita

  • Publisher : CRC Press
  • Release : 2013-06-18
  • Pages : 366
  • ISBN : 146658209X
  • Language : En, Es, Fr & De
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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 machine learning techniques in depth to help you more effectively detect and counter network intrusion. In this book, you’ll learn about: Network anomalies and vulnerabilities at various layers The pros and cons of various machine learning techniques and algorithms A taxonomy of attacks based on their characteristics and behavior Feature selection algorithms How to assess the accuracy, performance, completeness, timeliness, stability, interoperability, reliability, and other dynamic aspects of a network anomaly detection system Practical tools for launching attacks, capturing packet or flow traffic, extracting features, detecting attacks, and evaluating detection performance Important unresolved issues and research challenges that need to be overcome to provide better protection for networks Examining numerous attacks in detail, the authors look at the tools that intruders use and show how to use this knowledge to protect networks. The book also provides material for hands-on development, so that you can code on a testbed to implement detection methods toward the development of your own intrusion detection system. It offers a thorough introduction to the state of the art in network anomaly detection using machine learning approaches and systems.

Network Traffic Anomaly Detection and Prevention

Network Traffic Anomaly Detection and Prevention
Concepts, Techniques, and Tools

by Monowar H. Bhuyan,Dhruba K. Bhattacharyya,Jugal K. Kalita

  • Publisher : Springer
  • Release : 2017-09-19
  • Pages : 263
  • ISBN : 9783319651866
  • Language : En, Es, Fr & De
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This indispensable text/reference presents a comprehensive overview on the detection and prevention of anomalies in computer network traffic, from coverage of the fundamental theoretical concepts to in-depth analysis of systems and methods. Readers will benefit from invaluable practical guidance on how to design an intrusion detection technique and incorporate it into a system, as well as on how to analyze and correlate alerts without prior information. Topics and features: introduces the essentials of traffic management in high speed networks, detailing types of anomalies, network vulnerabilities, and a taxonomy of network attacks; describes a systematic approach to generating large network intrusion datasets, and reviews existing synthetic, benchmark, and real-life datasets; provides a detailed study of network anomaly detection techniques and systems under six different categories: statistical, classification, knowledge-base, cluster and outlier detection, soft computing, and combination learners; examines alert management and anomaly prevention techniques, including alert preprocessing, alert correlation, and alert post-processing; presents a hands-on approach to developing network traffic monitoring and analysis tools, together with a survey of existing tools; discusses various evaluation criteria and metrics, covering issues of accuracy, performance, completeness, timeliness, reliability, and quality; reviews open issues and challenges in network traffic anomaly detection and prevention. This informative work is ideal for graduate and advanced undergraduate students interested in network security and privacy, intrusion detection systems, and data mining in security. Researchers and practitioners specializing in network security will also find the book to be a useful reference.

Unsupervised Network Anomaly Detection

Unsupervised Network Anomaly Detection
A Book

by Johan Mazel

  • Publisher : Unknown Publisher
  • Release : 2011
  • Pages : 40
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Anomaly detection has become a vital component of any network in today's Internet. Ranging from non-malicious unexpected events such as flash-crowds and failures, to network attacks such as denials-of-service and network scans, network traffic anomalies can have serious detrimental effects on the performance and integrity of the network. The continuous arising of new anomalies and attacks create a continuous challenge to cope with events that put the network integrity at risk. Moreover, the inner polymorphic nature of traffic caused, among other things, by a highly changing protocol landscape, complicates anomaly detection system's task. In fact, most network anomaly detection systems proposed so far employ knowledge-dependent techniques, using either misuse detection signature-based detection methods or anomaly detection relying on supervised-learning techniques. However, both approaches present major limitations: the former fails to detect and characterize unknown anomalies (letting the network unprotected for long periods) and the latter requires training over labeled normal traffic, which is a difficult and expensive stage that need to be updated on a regular basis to follow network traffic evolution. Such limitations impose a serious bottleneck to the previously presented problem.We introduce an unsupervised approach to detect and characterize network anomalies, without relying on signatures, statistical training, or labeled traffic, which represents a significant step towards the autonomy of networks. Unsupervised detection is accomplished by means of robust data-clustering techniques, combining Sub-Space clustering with Evidence Accumulation or Inter-Clustering Results Association, to blindly identify anomalies in traffic flows. Correlating the results of several unsupervised detections is also performed to improve detection robustness. The correlation results are further used along other anomaly characteristics to build an anomaly hierarchy in terms of dangerousness. Characterization is then achieved by building efficient filtering rules to describe a detected anomaly. The detection and characterization performances and sensitivities to parameters are evaluated over a substantial subset of the MAWI repository which contains real network traffic traces.Our work shows that unsupervised learning techniques allow anomaly detection systems to isolate anomalous traffic without any previous knowledge. We think that this contribution constitutes a great step towards autonomous network anomaly detection.This PhD thesis has been funded through the ECODE project by the European Commission under the Framework Programme 7. The goal of this project is to develop, implement, and validate experimentally a cognitive routing system that meet the challenges experienced by the Internet in terms of manageability and security, availability and accountability, as well as routing system scalability and quality. The concerned use case inside the ECODE project is network anomaly.

Network Anomaly Detection

Network Anomaly Detection
A Book

by Jugal Kalita

  • Publisher : Unknown Publisher
  • Release : 2013
  • Pages : 366
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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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 machine learning techniques in depth to help you more effectively detect and counter network intrusion. In this book, you'll learn about: Network anomalies and vulnerabilities at various layers The pros and cons of various machine learning techniques and algorithms A taxonomy of attacks based on their characteristics and behavior Feature selection algorithms How to assess the accuracy, performance, completeness, timeliness, stability, interoperability, reliability, and other dynamic aspects of a network anomaly detection system Practical tools for launching attacks, capturing packet or flow traffic, extracting features, detecting attacks, and evaluating detection performance Important unresolved issues and research challenges that need to be overcome to provide better protection for networks Examining numerous attacks in detail, the authors look at the tools that intruders use and show how to use this knowledge to protect networks. The book also provides material for hands-on development, so that you can code on a testbed to implement detection methods toward the development of your own intrusion detection system. It offers a thorough introduction to the state of the art in network anomaly detection using machine learning approaches and systems.

New Trends in Network Anomaly Detection

New Trends in Network Anomaly Detection
A Book

by Yasser Yasami,Saadat Pourmozaffari

  • Publisher : Unknown Publisher
  • Release : 2010
  • Pages : 129
  • ISBN : 9789533070728
  • Language : En, Es, Fr & De
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Network Anomaly Detection

Network Anomaly Detection
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 2013
  • Pages : 11
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Outlier Detection: Techniques and Applications

Outlier Detection: Techniques and Applications
A Data Mining Perspective

by N. N. R. Ranga Suri,Narasimha Murty M,G. Athithan

  • Publisher : Springer
  • Release : 2019-01-10
  • Pages : 214
  • ISBN : 3030051277
  • Language : En, Es, Fr & De
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This book, drawing on recent literature, highlights several methodologies for the detection of outliers and explains how to apply them to solve several interesting real-life problems. The detection of objects that deviate from the norm in a data set is an essential task in data mining due to its significance in many contemporary applications. More specifically, the detection of fraud in e-commerce transactions and discovering anomalies in network data have become prominent tasks, given recent developments in the field of information and communication technologies and security. Accordingly, the book sheds light on specific state-of-the-art algorithmic approaches such as the community-based analysis of networks and characterization of temporal outliers present in dynamic networks. It offers a valuable resource for young researchers working in data mining, helping them understand the technical depth of the outlier detection problem and devise innovative solutions to address related challenges.

Network Traffic Characterization and Network Anomaly Detection

Network Traffic Characterization and Network Anomaly Detection
A Book

by Lan Li

  • Publisher : Unknown Publisher
  • Release : 2006
  • Pages : 220
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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NADIR (Network Anomaly Detection and Intrusion Reporter)

NADIR (Network Anomaly Detection and Intrusion Reporter)
A Prototype Network Intrusion Detection System

by Anonim

  • Publisher : Unknown Publisher
  • Release : 1990
  • Pages : 15
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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The Network Anomaly Detection and Intrusion Reporter (NADIR) is an expert system which is intended to provide real-time security auditing for intrusion and misuse detection at Los Alamos National Laboratory's Integrated Computing Network (ICN). It is based on three basic assumptions: that statistical analysis of computer system and user activities may be used to characterize normal system and user behavior, and that given the resulting statistical profiles, behavior which deviates beyond certain bounds can be detected, that expert system techniques can be applied to security auditing and intrusion detection, and that successful intrusion detection may take place while monitoring a limited set of network activities such as user authentication and access control, file movement and storage, and job scheduling. NADIR has been developed to employ these basic concepts while monitoring the audited activities of more than 8000 ICN users.

Clustering-based Network Anomaly Detection

Clustering-based Network Anomaly Detection
A Book

by SunHee Baek

  • Publisher : Unknown Publisher
  • Release : 2017
  • Pages : 110
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Network Traffic Anomaly Detection Using Modified Hidden Markov Model

Network Traffic Anomaly Detection Using Modified Hidden Markov Model
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 2019
  • Pages : 164
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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With the growing number of attacks and malicious threats on the Internet services and network infrastructures, the need for techniques to identity and detect attacks is increasing. Therefore, using machine learning techniques along traditional security mechanisms such as firewall and cryptography, can improve the performance of intrusion detection systems (IDSs). Network anomaly detection has become a very important area for both industrial application and academic research in the recent years. It is involved widely in a broad spectrum of domains and many research areas. Detection anomalies (attacks are detected as anomalies) in data is a crucial problem to diverse real-world applications. The goal of anomaly detection is to identify anomalous behavior, events based on deviations from expected normal usage. Hidden Markov Models (HMM) have been applied to anomaly detection since 1996. The previous researches applying HMM were limited to small data sets. In our work, we have used the term anomaly detection to describe the process of differentiating abnormal behavior from normal behavior on datasets available in this study. In this dissertation, we describe our research contributions for detecting anomalous patterns in network traffic data using HMM. We built HMM correlates the observation sequences and state transitions to predict the most probable intrusion state sequences that are capable of reducing false positive rate.

Network Traffic Anomaly Detection and Prevention

Network Traffic Anomaly Detection and Prevention
Concepts, Techniques, and Tools

by Monowar H. Bhuyan,Dhruba K. Bhattacharyya,Jugal K. Kalita

  • Publisher : Springer
  • Release : 2017-09-03
  • Pages : 263
  • ISBN : 3319651889
  • Language : En, Es, Fr & De
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This indispensable text/reference presents a comprehensive overview on the detection and prevention of anomalies in computer network traffic, from coverage of the fundamental theoretical concepts to in-depth analysis of systems and methods. Readers will benefit from invaluable practical guidance on how to design an intrusion detection technique and incorporate it into a system, as well as on how to analyze and correlate alerts without prior information. Topics and features: introduces the essentials of traffic management in high speed networks, detailing types of anomalies, network vulnerabilities, and a taxonomy of network attacks; describes a systematic approach to generating large network intrusion datasets, and reviews existing synthetic, benchmark, and real-life datasets; provides a detailed study of network anomaly detection techniques and systems under six different categories: statistical, classification, knowledge-base, cluster and outlier detection, soft computing, and combination learners; examines alert management and anomaly prevention techniques, including alert preprocessing, alert correlation, and alert post-processing; presents a hands-on approach to developing network traffic monitoring and analysis tools, together with a survey of existing tools; discusses various evaluation criteria and metrics, covering issues of accuracy, performance, completeness, timeliness, reliability, and quality; reviews open issues and challenges in network traffic anomaly detection and prevention. This informative work is ideal for graduate and advanced undergraduate students interested in network security and privacy, intrusion detection systems, and data mining in security. Researchers and practitioners specializing in network security will also find the book to be a useful reference.

Anomaly detection using the correlational paraconsistent machine with digital signatures of network segment

Anomaly detection using the correlational paraconsistent machine with digital signatures of network segment

by Eduardo H.M. Pena ,Luiz F. Carvalho ,Sylvio Barbon ,Joel J.P.C. Rodrigues ,Mario Lemes Proença

  • Publisher : Infinite Study
  • Release : 2021
  • Pages : 16
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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This study presents the correlational paraconsistent machine (CPM), a tool for anomaly detection that incorporates unsupervised models for traffic characterization and principles of paraconsistency, to inspect irregularities at the network traffic flow level.

ספר מהרי״ל

ספר מהרי״ל

by Yaacov Molin

  • Publisher : Unknown Publisher
  • Release : 1968
  • Pages : 176
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Signal Processing Methodology for Network Anomaly Detection

Signal Processing Methodology for Network Anomaly Detection
A Book

by Rafał Renk

  • Publisher : Unknown Publisher
  • Release : 2011
  • Pages : 129
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Signal Processing Methodology for Network Anomaly Detection.

Entropy-based Network Anomaly Detection

Entropy-based Network Anomaly Detection
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 2015
  • Pages : 129
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Visualizing Network Traffic as Images for Network Anomaly Detection

Visualizing Network Traffic as Images for Network Anomaly Detection
A Book

by Samabia Tehsin

  • Publisher : Unknown Publisher
  • Release : 2007
  • Pages : 129
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Statistical Topics Relating to Computer Network Anomaly Detection

Statistical Topics Relating to Computer Network Anomaly Detection
A Book

by Qi Ding

  • Publisher : Unknown Publisher
  • Release : 2012
  • Pages : 188
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Abstract: This dissertation makes fundamental contributions to statistical methods relating to the detection of anomalies in the context of computer network traffic monitoring. In particular, it contributes basic statistical tools for socially-based network anomaly characterization and detection, it extends a popular detection methodology to high-dimensional contexts, and it demonstrates that standard flow sampling can interact with inherent network topology in ways unexpected. In the first contribution of my research, I define anomalous intrusion in terms of locations in social space, rather than in physical space. I develop statistical detectors based on simple graph-based summaries of the network, with a focus on detecting anti-social behaviors. This research suggests that certain values of local graphical measurements, like clustering coefficients and betweenness centrality, are associated with the malicious antisocial behaviors in the types of network representations of IP flow measurements used in this work. This motivates me to propose a simple, efficient and robust anomaly detection technique. I evaluate this methodology on different network representations and using different social summaries. In the second contribution of my research, I extend the use of the PCA subspace method to high-dimensional spaces. Specifically, I show that, under appropriate conditions, with high probability the magnitude of the residuals of a standard PCA subspace analysis of randomly projected data behaves comparably to that of the residuals of a similar PCA analysis of the original data. My results indicate the feasibility of applying subspace-based anomaly detection algorithms to Gaussian random projection data. This concept is illustrated in the context of computer network traffic anomaly detection for the purpose of detecting volume anomalies. The impact of sampling on so-called Peer-to-Peer (P2P) network analysis is the focus of the third contribution of my research. In this research I use a combination of probability calculations and simulation techniques to characterize the extent to which standard packet sampling in the Internet can adversely affect the topology of stylized versions of Bittorrent download networks reconstructed from measurements of network Rows. The results indicate that a certain stratification observed in these networks impacts the reconstructed topology in ways decidedly different from typical networks which have no stratification.

Distributed Online Averaged One Dependence Estimator Algorithm for Network Anomaly Detection Systems

Distributed Online Averaged One Dependence Estimator Algorithm for Network Anomaly Detection Systems
A Book

by Mukrimah Nawir

  • Publisher : Unknown Publisher
  • Release : 2019
  • Pages : 101
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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The research concretely attention to the anomaly detection for network attacks in the dataset. the dataset used in this work is a labelled dataset and only work on supervised learning for classification. In the meantime, the focus of this observation is on ML approaches. A small component in a basis IDS is a detection model by using ML approaches. Moreover, the network data representing as a set of data named UNSW-NB15 dataset. The experiments evaluate a binary and multi-class classification for NADS by using ML techniques. An emphasize of this thesis is on the topic of an online classification algorithm. The online classifier is continuously updated the classifier change over time due to network traffic. It is used for large-scale NADS by using distributed learning can improve the performances of ML algorithms based on accuracy and time taken to train the classifier. In this thesis, the distributed algorithm does not deal with dynamic and large networks. The network assumes in fix, static, and middle size (with less than 100 nodes) network. For a distributed architecture, this work assumed that all the nodes present in the network connected to each other.

Network Traffic Anomaly Detection and Evaluation

Network Traffic Anomaly Detection and Evaluation
A Book

by Daniela Brauckhoff

  • Publisher : Unknown Publisher
  • Release : 2010
  • Pages : 180
  • ISBN : 9783832289775
  • Language : En, Es, Fr & De
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