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NETWORK ANOMALY DETECTION: MACHINE LEARNING PERSPECTIVE

Information Technology

Computing & Information Technology

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B2B GT

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B2B & B2C

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.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 Anomaly Detection

POST SECONDARY FOUR YEAR

COLFR

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Duration

Duration Unit Of Measure

Transaction Type

Front Cover.
Half Title Page.
Title Page.
Copyright Page.
Dedication.
Contents.
List of Figures.
List of Tables.
Preface.
Acknowledgments.
Abstract.
Authors.
1: Introduction.
2: Networks and Anomalies.
3: An Overview of Machine Learning Methods.
4: Detecting Anomalies in Network Data.
5: Feature Selection.
6: Approaches to Network Anomaly Detection.
7: Evaluation Methods.
8: Tools and Systems.
9: Open Issues, Challenges and Concluding Remarks.
References.

Network Anomaly Detection.css-1p8rvzy{position:absolute;padding-left:8px;}

Network Anomaly Detection