Taschenbuch. Condici�n: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 156 pp. Englisch. N� de ref. del art�culo: 9789811527180

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Intrusion Detection: A Data Mining Approach

Nandita Sengupta|Jaya Sil

Publicado por Springer Nature Singapore, 2021

ISBN 10: 9811527180 ISBN 13: 9789811527180

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Librería: moluna, Greven, Alemania

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Condici�n: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 systemsIncludes a spec. N� de ref. del art�culo: 438820605

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Intrusion Detection : A Data Mining Approach

Jaya Sil

Publicado por Springer Nature Singapore, Springer Nature Singapore, 2021

ISBN 10: 9811527180 ISBN 13: 9789811527180

Nuevo Taschenbuch

Librería: AHA-BUCH GmbH, Einbeck, Alemania

Calificaci�n del vendedor: 5 de 5 estrellas

Taschenbuch. Condici�n: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. N� de ref. del art�culo: 9789811527180

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