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Intrusion Detection = A Data Mining ...
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Sengupta, Nandita.
Intrusion Detection = A Data Mining Approach /
Record Type:
Language materials, printed : Monograph/item
Title/Author:
Intrusion Detection/ by Nandita Sengupta, Jaya Sil.
Reminder of title:
A Data Mining Approach /
Author:
Sengupta, Nandita.
other author:
Sil, Jaya.
Description:
XX, 136 p.online resource. :
Contained By:
Springer Nature eBook
Subject:
Computer communication systems. -
Online resource:
https://doi.org/10.1007/978-981-15-2716-6
ISBN:
9789811527166
Intrusion Detection = A Data Mining Approach /
Sengupta, Nandita.
Intrusion Detection
A Data Mining Approach /[electronic resource] :by Nandita Sengupta, Jaya Sil. - 1st ed. 2020. - XX, 136 p.online resource. - 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:
1115394
Computer communication systems.
LC Class. No.: TK5105.5-5105.9
Dewey Class. No.: 004.6
Intrusion Detection = A Data Mining Approach /
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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. 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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