國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Anomaly-detection and health-analysi...

Chakrabarty, Krishnendu.

Anomaly-detection and health-analysis techniques for core router systems

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Anomaly-detection and health-analysis techniques for core router systemsby Shi Jin, Zhaobo Zhang, Krishnendu Chakrabarty, Xinli Gu.
作者:	Jin, Shi.
其他作者:	Zhang, Zhaobo.
出版者:	Cham :Springer international Publishing :2020.
面頁冊數:	xiii, 148 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Routing (Computer network management)
電子資源:	https://doi.org/10.1007/978-3-030-33664-6
ISBN:	9783030336646$q(electronic bk.)

Anomaly-detection and health-analysis techniques for core router systems
Jin, Shi.

Anomaly-detection and health-analysis techniques for core router systems[electronic resource] /by Shi Jin, Zhaobo Zhang, Krishnendu Chakrabarty, Xinli Gu. - Cham :Springer international Publishing :2020. - xiii, 148 p. :ill., digital ;24 cm.

introduction -- Anomaly Detection Using Correlation-Based Time-Series Analysis -- Changepoint-based Anomaly Detection -- Hierarchical Symbol-based Health-Status Analysis -- Self-Learning Health-Status Analysis -- Conclusion.

This book tackles important problems of anomaly detection and health status analysis in complex core router systems, integral to today's internet Protocol (iP) networks. The techniques described provide the first comprehensive set of data-driven resiliency solutions for core router systems. The authors present an anomaly detector for core router systems using correlation-based time series analysis, which monitors a set of features of a complex core router system. They also describe the design of a changepoint-based anomaly detector such that anomaly detection can be adaptive to changes in the statistical features of data streams. The presentation also includes a symbol-based health status analyzer that first encodes, as a symbol sequence, the long-term complex time series collected from a number of core routers, and then utilizes the symbol sequence for health analysis. Finally, the authors describe an iterative, self-learning procedure for assessing the health status. Enables Accurate Anomaly Detection Using Correlation-Based Time-Series Analysis; Presents the design of a changepoint-based anomaly detector; includes Hierarchical Symbol-based Health-Status Analysis; Describes an iterative, self-learning procedure for assessing the health status.

ISBN: 9783030336646$q(electronic bk.)

Standard No.: 10.1007/978-3-030-33664-6doiSubjects--Topical Terms:

339251
Routing (Computer network management)

LC Class. No.: TK5105.543 / .J567 2020

Dewey Class. No.: 004.665

Anomaly-detection and health-analysis techniques for core router systems
LDR:02514nmm a2200325 a 4500 001 575683
003 DE-He213
005 20200518153725.0
006 m d
007 cr nn 008maaau
008 201027s2020 sz s 0 eng d
020 $a 9783030336646$q(electronic bk.)
020 $a 9783030336639$q(paper)
024 7 $a 10.1007/978-3-030-33664-6 $2 doi
035 $a 978-3-030-33664-6
040 $a GP $c GP
041 0 $a eng
050 4 $a TK5105.543 $b .J567 2020
072 7 $a TJFC $2 bicssc
072 7 $a TEC008010 $2 bisacsh
072 7 $a TJFC $2 thema
082 0 4 $a 004.665 $2 23
090 $a TK5105.543 $b .J61 2020
100 1 $a Jin, Shi. $3 798721
245 1 0 $a Anomaly-detection and health-analysis techniques for core router systems $h [electronic resource] / $c by Shi Jin, Zhaobo Zhang, Krishnendu Chakrabarty, Xinli Gu.
260 $a Cham : $b Springer international Publishing : $b imprint: Springer, $c 2020.
300 $a xiii, 148 p. : $b ill., digital ; $c 24 cm.
505 0 $a introduction -- Anomaly Detection Using Correlation-Based Time-Series Analysis -- Changepoint-based Anomaly Detection -- Hierarchical Symbol-based Health-Status Analysis -- Self-Learning Health-Status Analysis -- Conclusion.
520 $a This book tackles important problems of anomaly detection and health status analysis in complex core router systems, integral to today's internet Protocol (iP) networks. The techniques described provide the first comprehensive set of data-driven resiliency solutions for core router systems. The authors present an anomaly detector for core router systems using correlation-based time series analysis, which monitors a set of features of a complex core router system. They also describe the design of a changepoint-based anomaly detector such that anomaly detection can be adaptive to changes in the statistical features of data streams. The presentation also includes a symbol-based health status analyzer that first encodes, as a symbol sequence, the long-term complex time series collected from a number of core routers, and then utilizes the symbol sequence for health analysis. Finally, the authors describe an iterative, self-learning procedure for assessing the health status. Enables Accurate Anomaly Detection Using Correlation-Based Time-Series Analysis; Presents the design of a changepoint-based anomaly detector; includes Hierarchical Symbol-based Health-Status Analysis; Describes an iterative, self-learning procedure for assessing the health status.
650 0 $a Routing (Computer network management) $3 339251
650 0 $a Time-series analysis. $3 181890
650 1 4 $a Circuits and Systems. $3 274416
650 2 4 $a Cyber-physical systems, ioT. $3 863769
650 2 4 $a Communications Engineering, Networks. $3 273745
700 1 $a Zhang, Zhaobo. $3 863811
700 1 $a Chakrabarty, Krishnendu. $3 255103
700 1 $a Gu, Xinli. $3 863812
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
856 4 0 $u https://doi.org/10.1007/978-3-030-33664-6
950 $a Engineering (Springer-11647)