Network Anomaly Detection: A Machine Learning Perspective, 1st Edition

  • Dhruba Kumar Bhattacharyya
  • Published By: Chapman & Hall
  • ISBN-10: 146658209X
  • ISBN-13: 9781466582095
  • DDC: 005.8
  • Grade Level Range: College Freshman - College Senior
  • 366 Pages | eBook
  • Original Copyright 2013 | Published/Released March 2016
  • This publication's content originally published in print form: 2013

  • Price:  Sign in for price



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.

Table of Contents

Front Cover.
Half Title Page.
Title Page.
Copyright Page.
List of Figures.
List of Tables.
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.