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Intrusion Detection : A Data Mining Approach - 2020 ed.

By: (Author) Jaya Sil , (Author) Nandita Sengupta

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Ksh 23,400.00

Format: Hardback or Cased Book

ISBN-10: 9811527156

ISBN-13: 9789811527159

Edition: 2020 ed.

Series: Cognitive Intelligence and Robotics

Publisher: Springer Verlag, Singapore

Imprint: Springer Verlag, Singapore

Country of Manufacture: GB

Country of Publication: GB

Publication Date: Jan 25th, 2020

Print length: 136 Pages

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This book presents state-of-the-art research on intrusion detection using reinforcement learning, fuzzy and rough set theories, and genetic algorithm.

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.



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