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Intrusion Detection : A Data Mining Approach / by Nandita Sengupta, Jaya Sil.
- Format:
- Book
- Author/Creator:
- Sengupta, Nandita, author.
- Sil, Jaya, author.
- Series:
- Computer Science (Springer-11645)
- Cognitive Intelligence and Robotics,. 2520-1956
- Cognitive Intelligence and Robotics, 2520-1956
- Language:
- English
- Subjects (All):
- Computer networks.
- Computer security.
- Data encryption (Computer science).
- Computer Communication Networks.
- Systems and Data Security.
- Cryptology.
- Local Subjects:
- Computer Communication Networks.
- Systems and Data Security.
- Cryptology.
- Physical Description:
- 1 online resource (XX, 136 pages).
- Edition:
- First edition 2020.
- Contained In:
- Springer eBooks
- Place of Publication:
- Singapore : Springer Singapore : Imprint: Springer, 2020.
- System Details:
- text file PDF
- 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.
- 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.
- Other Format:
- Printed edition:
- ISBN:
- 978-981-15-2716-6
- 9789811527166
- 9789811527159
- 9789811527173
- 9789811527180
- Access Restriction:
- Restricted for use by site license.
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