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Intrusion Detection

A Data Mining Approach

  • Details dimension reduction techniques, which reduce the complexity of intrusion detection systems without sacrificing prediction accuracy
  • Sheds new light on real-time design of adaptive intrusion detection systems
  • Includes a special chapter on reinforcement learning used for intrusion detection systems and discretization techniques

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

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Table of contents (5 chapters)

  1. Front Matter

    Pages i-xx
  2. Introduction

    • Nandita Sengupta, Jaya Sil
    Pages 1-25
  3. Discretization

    • Nandita Sengupta, Jaya Sil
    Pages 27-46
  4. Data Reduction

    • Nandita Sengupta, Jaya Sil
    Pages 47-82
  5. Q-Learning Classifier

    • Nandita Sengupta, Jaya Sil
    Pages 83-111
  6. Conclusions and Future Research

    • 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.


Authors and Affiliations

  • Department of Information Technology, University College of Bahrain, Manama, Bahrain

    Nandita Sengupta

  • Department of Computer Science and Technology, Indian Institute of Engineering Science and Technology (IIEST), Shibpur, Howrah, India

    Jaya Sil

About the authors

Nandita Sengupta holds a Bachelor of Engineering degree from the Indian Institute of Engineering Science and Technology (IIEST), Shibpur, India (formerly known as Bengal Engineering College, Shibpur, Calcutta University). She completed a postgraduate management course in Information Technology at IMT, an M.Tech. (Information Technology) and Ph.D. in Engineering (Computer Science and Technology) at IIEST, Shibpur, India. She has worked in the field for 29 years, including 11 years in industry and 18 years teaching IT various subjects. She is currently an Associate Professor at the University College of Bahrain, Bahrain. Her areas of interest are analysis of algorithms, theory of computation, soft computing techniques, network computing and security.

Jaya Sil has been a Professor at the Department of Computer Science and Technology at the Indian Institute of Engineering Science and Technology, Shibpur, since 2003. She completed her B.E. in Electronics and Telecommunication Engineering at B.E. College, at Calcutta University, India, in 1984, and M.E. (Tele) at Jadavpur University, Kolkata, India, in 1986. She received her Ph.D. (Engg) degree in the field of artificial intelligence from Jadavpur University, Kolkata, in 1996, and started her teaching career in 1987 as a lecturer at the Department of Computer Science and Technology at B.E. College, Howrah. She worked as a Postdoctoral Fellow at Nanyang Technological University, Singapore, from 2002 to 2003. She undertook collaborative research in Husar at the Bioinformatics Lab, Heidelberg, Germany, and also visited Wroclaw University of Technology, Poland, in 2012. She was awarded an INSA Senior Scientist Fellowship. Prof. Sil has delivered tutorials and invited talks, and has also presented papers and chaired sessions at various international conferences in abroad and India. She has published more than 200 research papers (including conference papers) in the field of bioinformatics, machinelearning and image processing along with applications in a variety of engineering fields. She has published numerous books and several book chapters and acted as a reviewer for IEEE, Elsevier, and Springer Journals.

Bibliographic Information

  • Book Title: Intrusion Detection

  • Book Subtitle: A Data Mining Approach

  • Authors: Nandita Sengupta, Jaya Sil

  • Series Title: Cognitive Intelligence and Robotics

  • DOI: https://doi.org/10.1007/978-981-15-2716-6

  • Publisher: Springer Singapore

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: Springer Nature Singapore Pte Ltd. 2020

  • Hardcover ISBN: 978-981-15-2715-9Published: 25 January 2020

  • Softcover ISBN: 978-981-15-2718-0Published: 25 January 2021

  • eBook ISBN: 978-981-15-2716-6Published: 24 January 2020

  • Series ISSN: 2520-1956

  • Series E-ISSN: 2520-1964

  • Edition Number: 1

  • Number of Pages: XX, 136

  • Topics: Computer Communication Networks, Systems and Data Security, Cryptology

Buy it now

Buying options

eBook USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access