東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Intrusion detection = a data mining ...

Sengupta, Nandita.

Linked to FindBook

Google Book

Amazon

博客來

Intrusion detection = a data mining approach /

Record Type:	Electronic resources : 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.
Published:	Singapore :Springer Singapore : : 2020.,
Description:	xx, 136 p. :ill., digital ;24 cm.
[NT 15003449]:	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
Subject:	Intrusion detection systems (Computer security) -
Online resource:	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 /
LDR:02199nmm a2200337 a 4500 001 2215517
003 DE-He213
005 20200124103647.0
006 m d
007 cr nn 008maaau
008 201120s2020 si s 0 eng d
020 $a 9789811527166 $q (electronic bk.)
020 $a 9789811527159 $q (paper)
024 7 $a 10.1007/978-981-15-2716-6 $2 doi
035 $a 978-981-15-2716-6
040 $a GP $c GP
041 0 $a eng
050 4 $a TK5105.59 $b .S45 2020
072 7 $a UKN $2 bicssc
072 7 $a COM075000 $2 bisacsh
072 7 $a UKN $2 thema
082 0 4 $a 005.83 $2 23
090 $a TK5105.59 $b .S476 2020
100 1 $a Sengupta, Nandita. $3 3447090
245 1 0 $a Intrusion detection $h [electronic resource] : $b a data mining approach / $c by Nandita Sengupta, Jaya Sil.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2020.
300 $a xx, 136 p. : $b ill., digital ; $c 24 cm.
490 1 $a Cognitive intelligence and robotics, $x 2520-1956
505 0 $a 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.
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.
650 0 $a Intrusion detection systems (Computer security) $3 1085454
650 0 $a Data mining. $3 562972
650 1 4 $a Computer Communication Networks. $3 775497
650 2 4 $a Systems and Data Security. $3 898223
650 2 4 $a Cryptology. $3 3382308
700 1 $a Sil, Jaya. $3 3308764
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Cognitive intelligence and robotics. $3 3338190
856 4 0 $u https://doi.org/10.1007/978-981-15-2716-6
950 $a Computer Science (Springer-11645)