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Intrusion detection = a data mining ...

Sengupta, Nandita.

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Intrusion detection = a data mining approach /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Intrusion detection/ by Nandita Sengupta, Jaya Sil.
其他題名:	a data mining approach /
作者:	Sengupta, Nandita.
其他作者:	Sil, Jaya.
出版者:	Singapore :Springer Singapore : : 2020.,
面頁冊數:	xx, 136 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer eBooks
標題:	Intrusion detection systems (Computer security) -
電子資源:	https://doi.org/10.1007/978-981-15-2716-6
ISBN:	9789811527166

Intrusion detection = a data mining approach /
Sengupta, Nandita.

Intrusion detectiona data mining approach /[electronic resource] :by Nandita Sengupta, Jaya Sil. - Singapore :Springer Singapore :2020. - xx, 136 p. :ill., digital ;24 cm. - Cognitive intelligence and robotics,2520-1956. - Cognitive intelligence and robotics..

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.

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.

ISBN: 9789811527166

Standard No.: 10.1007/978-981-15-2716-6doiSubjects--Topical Terms:

1085454
Intrusion detection systems (Computer security)

LC Class. No.: TK5105.59 / .S45 2020

Dewey Class. No.: 005.83

Intrusion detection = a data mining approach /
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520 $a 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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