Unsupervised Network Anomaly Detection

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Unsupervised Network Anomaly Detection

Unsupervised Network Anomaly Detection written by Johan Mazel and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with categories.

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 written by Dhruba Kumar Bhattacharyya and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-18 with Computers categories.

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.

Unsupervised Anomaly Detection Framework For Multiple Connection Based Network Intrusions

Unsupervised Anomaly Detection Framework For Multiple Connection Based Network Intrusions written by Wei Lu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with categories.

Network Traffic Anomaly Detection And Prevention

Network Traffic Anomaly Detection And Prevention written by Monowar H. Bhuyan and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-09-03 with Computers categories.

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.

23rd Dasc

23rd Dasc written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Air traffic control categories.

Unsupervised Density And Anomaly Based Intrusion Detector In Networks

Unsupervised Density And Anomaly Based Intrusion Detector In Networks written by Eric Lim and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with Computer networks categories.

To protect the integrity and security of networks, experts and researchers are constantly improving ways to capture network-based threats. As the security systems advance towards effcient and effective recognition of such threats, exploiters and malicious users introduce novel attacks unknown to the society to circumvent the defensive systems. Novel threats are difficult to detect and capture due to a lack of information initially, but they must not be disregarded until suficient information is gathered to help identify them. A capable security system should not only be able to detect previously known attacks but also possess abilities to detect unknown novel attacks. We propose a method in which the novel attacks can be detected without prior knowledge or presumption of their intentions and purposes. Our method is based on (1) unsupervised anomaly detection algorithms that do not require labels in data, which makes it a useful exploratory tool for finding potential novel threats in the given data, and (2) local density algorithms, such as LOF and DBSCAN, to robustly analyse data while not being affected by the arbitrary shapes of the data. Our method implements a two-phase structure to mitigate one of the main caveats of an unsupervised anomaly detection task, that is relative underperformance in comparison to its supervised and semi-supervised counterparts. We evaluate each phase of our method as well as the whole, using KDD'99 data, to show that it works as designed and compare the results against other possible methods to report the strengths and weaknesses of our method. In our experiments, our proposed method was able to detect between 95% and 98% of intrusions, on average, from one dataset of KDD'99 and about 88% of intrusions from another, which suggests that our method is accurate. The standard deviations of our results were extremely small to suggest that our method is also precise. Our method was able to outperform clustering-based and PCA-based approaches in terms of both accuracy and precision.

Image Analysis And Processing

Image Analysis And Processing written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Image processing categories.

Data Mining Intrusion Detection Information Assurance And Data Networks Security

Data Mining Intrusion Detection Information Assurance And Data Networks Security written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Data mining categories.

Data Mining

Data Mining written by Hillol Kargupta and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Computers categories.

A state-of-the-art survey of recent advances in data mining or knowledge discovery.

Advances In Neural Networks Isnn

Advances In Neural Networks Isnn written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Neural computers categories.