Intrusion Detection
A Data Mining Approach
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Beschreibung
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.
Details dimension reduction techniques, which reduce the complexity of intrusion detection systems without sacrificing prediction accuracy Sheds new light on real-time design of adaptive intrusion detection systems Includes a special chapter on reinforcement learning used for intrusion detection systems and discretization techniques
Autor*in
Nandita Sengupta
Themen in »Intrusion Detection«
Data Discretization Dimension Reduction Intrusion Detection Reinforcement Learning Rough Set Theory