Details

Subject(s)

• Computer networks [Browse]
• Data protection [Browse]
• Cryptography [Browse]
• Data encryption (Computer science) [Browse]

Author

• Sil, Jaya [Browse]
• Sil, Jaya [Browse]

Sil, Jaya [Browse]

Series

• Cognitive Intelligence and Robotics, [More in this series]
• Cognitive Intelligence and Robotics, 2520-1964 [More in this series]

Summary note

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.

Bibliographic references

Includes bibliographical references and index.

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.

Show 3 more Contents items

ISBN

981-15-2716-4

Doi

10.1007/978-981-15-2716-6

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