ISBN: 9781466582088 - network anomaly detection: a machine learning perspective (21 risultati)

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Hardback. Condizione: New. 1st. 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 layersThe pros and cons of various machine learning techniques and algorithmsA taxonomy of attacks based on their characteristics and behaviorFeature selection algorithmsHow to assess the accuracy, performance, completeness, timeliness, stability, interoperability, reliability, and other dynamic aspects of a network anomaly detection systemPractical tools for launching attacks, capturing packet or flow traffic, extracting features, detecting attacks, and evaluating detection performanceImportant unresolved issues and research challenges that need to be overcome to provide better protection for networksExamining 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.…

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Hardback. Condizione: New. 1st. 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 layersThe pros and cons of various machine learning techniques and algorithmsA taxonomy of attacks based on their characteristics and behaviorFeature selection algorithmsHow to assess the accuracy, performance, completeness, timeliness, stability, interoperability, reliability, and other dynamic aspects of a network anomaly detection systemPractical tools for launching attacks, capturing packet or flow traffic, extracting features, detecting attacks, and evaluating detection performanceImportant unresolved issues and research challenges that need to be overcome to provide better protection for networksExamining 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.…

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Gebunden. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Dhruba Kumar Bhattacharyya is a professor in computer science and engineering at Tezpur University. Professor Bhattacharyya s research areas include network security, data mining, and bioinformatics. He has published more than 180 resear.…

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Buch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book discusses the detection of anomalies in computer networks from a machine learning perspective. It examines how computer networks work and how they can be attacked by intruders in search of fame, fortune, or challenge. You'll learn how to look for patterns in captured network traffic data to unearth potential intrusion attempts. Coverage includes machine learning techniques and algorithms, a taxonomy of attacks, and practical tools for launching attacks, capturing packet or flow traffic, extracting features, detecting attacks, and evaluating performance. …