Intrusion detection : a data mining approach / Nandita Sengupta, Jaya Sil.
2020
TK5105.59
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Details
Title
Intrusion detection : a data mining approach / Nandita Sengupta, Jaya Sil.
Author
Sengupta, Nandita.
ISBN
9789811527166 (electronic book)
9811527164 (electronic book)
9789811527159
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
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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Table of Contents
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.
Chapter 2. Discretization
Chapter 3. Data Reduction
Chapter 4. Q-Learning Classifiers
Chapter 5. Hierarchical Q
Learning Classifier
Chapter 6. Conclusions and Future Research.