Intrusion detection : a data mining approach / Nandita Sengupta, Jaya Sil.
Sengupta, Nandita.; Sil, Jaya.
2020
TK5105.59
Available Online

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Title Intrusion detection : a data mining approach / Nandita Sengupta, Jaya Sil.
Author Sengupta, Nandita.
ISBN 9789811527166 (electronic book)
9811527164 (electronic book)
9789811527159
Published Singapore : Springer Singapore, 2020.
Language English
Description 1 online resource (xx, 136 pages)
Item Number 10.1007/978-981-15-2
Call Number TK5105.59
Dewey Decimal Classification 005.8/3
004.6
Summary 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.
Bibliography, etc. Note Includes bibliographical references and index.
Access Note Access limited to authorized users.
Added Author Sil, Jaya.
Series Cognitive intelligence and robotics.
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Record Appears in Online Resources > Ebooks
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Chapter 1. Introduction
Chapter 2. Discretization
Chapter 3. Data Reduction
Chapter 4. Q-Learning Classifiers
Chapter 5. Hierarchical Q
Learning Classifier
Chapter 6. Conclusions and Future Research.

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