Intrusion Detection Networks: A Key to Collaborative Security

Carol Fung, Raouf Boutaba

October 12, 2017 by Auerbach Publications
Reference - 261 Pages - 92 B/W Illustrations
ISBN 9781138198890 - CAT# K31421

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    • Provides a complete introduction to intrusion detection networks and intrusion detection systems
    • Identifies the challenges underlying the design of IDNs and IDSs
    • Evaluates various IDN designs
    • Outlines comprehensive validation methodologies and evaluation metrics


    The rapidly increasing sophistication of cyber intrusions makes them nearly impossible to detect without the use of a collaborative intrusion detection network (IDN). Using overlay networks that allow an intrusion detection system (IDS) to exchange information, IDNs can dramatically improve your overall intrusion detection accuracy.

    Intrusion Detection Networks: A Key to Collaborative Security
    focuses on the design of IDNs and explains how to leverage effective and efficient collaboration between participant IDSs. Providing a complete introduction to IDSs and IDNs, it explains the benefits of building IDNs, identifies the challenges underlying their design, and outlines possible solutions to these problems. It also reviews the full-range of proposed IDN solutions—analyzing their scope, topology, strengths, weaknesses, and limitations.

    • Includes a case study that examines the applicability of collaborative intrusion detection to real-world malware detection scenarios
    • Illustrates distributed IDN architecture design
    • Considers trust management, intrusion detection decision making, resource management, and collaborator management

    The book provides a complete overview of network intrusions, including their potential damage and corresponding detection methods. Covering the range of existing IDN designs, it elaborates on privacy, malicious insiders, scalability, free-riders, collaboration incentives, and intrusion detection efficiency. It also provides a collection of problem solutions to key IDN design challenges and shows how you can use various theoretical tools in this context.

    The text outlines comprehensive validation methodologies and metrics to help you improve efficiency of detection, robustness against malicious insiders, incentive-compatibility for all participants, and scalability in network size. It concludes by highlighting open issues and future challenges.


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