Intrusion Detection

A Data Mining Approach

  • Nandita Sengupta
  • Jaya Sil

Part of the Cognitive Intelligence and Robotics book series (CIR)

Table of contents

  1. Front Matter
    Pages i-xx
  2. Nandita Sengupta, Jaya Sil
    Pages 1-25
  3. Nandita Sengupta, Jaya Sil
    Pages 27-46
  4. Nandita Sengupta, Jaya Sil
    Pages 47-82
  5. Nandita Sengupta, Jaya Sil
    Pages 83-111
  6. Nandita Sengupta, Jaya Sil
    Pages 113-118
  7. Back Matter
    Pages 119-136

About this book


This book presents state-of-the-art research on intrusion detection using reinforcement learning, fuzzy and rough set theories, and genetic algorithm. Reinforcement learning is employed to incrementally learn the computer network behavior, while rough and fuzzy sets are utilized to handle the uncertainty involved in the detection of traffic anomaly to secure data resources from possible attack. Genetic algorithms make it possible to optimally select the network traffic parameters to reduce the risk of network intrusion.

The book is unique in terms of its content, organization, and writing style. Primarily intended for graduate electrical and computer engineering students, it is also useful for doctoral students pursuing research in intrusion detection and practitioners interested in network security and administration. The book covers a wide range of applications, from general computer security to server, network, and cloud security.


Data Discretization Dimension Reduction Intrusion Detection Reinforcement Learning Rough Set Theory

Authors and affiliations

  • Nandita Sengupta
    • 1
  • Jaya Sil
    • 2
  1. 1.Department of Information TechnologyUniversity College of BahrainManamaBahrain
  2. 2.Department of Computer Science and TechnologyIndian Institute of Engineering Science and Technology (IIEST), ShibpurHowrahIndia

Bibliographic information

  • DOI
  • Copyright Information Springer Nature Singapore Pte Ltd. 2020
  • Publisher Name Springer, Singapore
  • eBook Packages Computer Science
  • Print ISBN 978-981-15-2715-9
  • Online ISBN 978-981-15-2716-6
  • Series Print ISSN 2520-1956
  • Series Online ISSN 2520-1964
  • Buy this book on publisher's site
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