國立虎尾科技大學 |

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 DetectionA 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 /
LDR:02536nam a22004095i 4500 001 1017878
003 DE-He213
005 20200701091651.0
007 cr nn 008mamaa
008 210318s2020 si | s |||| 0|eng d
020 $a 9789811527166 $9 978-981-15-2716-6
024 7 $a 10.1007/978-981-15-2716-6 $2 doi
035 $a 978-981-15-2716-6
050 4 $a TK5105.5-5105.9
072 7 $a UKN $2 bicssc
072 7 $a COM075000 $2 bisacsh
072 7 $a UKN $2 thema
082 0 4 $a 004.6 $2 23
100 1 $a Sengupta, Nandita. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1312774
245 1 0 $a Intrusion Detection $h [electronic resource] : $b A Data Mining Approach / $c by Nandita Sengupta, Jaya Sil.
250 $a 1st ed. 2020.
264 1 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2020.
300 $a XX, 136 p. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
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 Computer communication systems. $3 1115394
650 0 $a Computer security. $3 557122
650 0 $a Data encryption (Computer science). $3 1051084
650 1 4 $a Computer Communication Networks. $3 669310
650 2 4 $a Systems and Data Security. $3 677062
650 2 4 $a Cryptology. $3 1211076
700 1 $a Sil, Jaya. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1202454
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9789811527159
776 0 8 $i Printed edition: $z 9789811527173
776 0 8 $i Printed edition: $z 9789811527180
830 0 $a Cognitive Intelligence and Robotics, $x 2520-1956 $3 1279326
856 4 0 $u https://doi.org/10.1007/978-981-15-2716-6
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)