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

Anomaly detection principles and alg...

Mehrotra, Kishan G.

Linked to FindBook

Google Book

Amazon

博客來

Anomaly detection principles and algorithms

Record Type:	Electronic resources : Monograph/item
Title/Author:	Anomaly detection principles and algorithms/ by Kishan G. Mehrotra, Chilukuri K. Mohan, HuaMing Huang.
Author:	Mehrotra, Kishan G.
other author:	Mohan, Chilukuri K.
Published:	Cham :Springer International Publishing : : 2017.,
Description:	xxii, 217 p. :ill., digital ;24 cm.
[NT 15003449]:	1 Introduction -- 2 Anomaly Detection -- 3 Distance-based Anomaly Detection Approaches -- 4 Clustering-based Anomaly Detection Approaches -- 5 Model-based Anomaly Detection Approaches -- 6 Distance and Density Based Approaches -- 7 Rank Based Approaches -- 8 Ensemble Methods -- 9 Algorithms for Time Series Data -- Datasets for Evaluation -- Datasets for Time Series Experiments.
Contained By:	Springer eBooks
Subject:	Anomaly detection (Computer security) -
Online resource:	http://dx.doi.org/10.1007/978-3-319-67526-8
ISBN:	9783319675268

Anomaly detection principles and algorithms
Mehrotra, Kishan G.

Anomaly detection principles and algorithms[electronic resource] /by Kishan G. Mehrotra, Chilukuri K. Mohan, HuaMing Huang. - Cham :Springer International Publishing :2017. - xxii, 217 p. :ill., digital ;24 cm. - Terrorism, security, and computation,2197-8778. - Terrorism, security, and computation..

1 Introduction -- 2 Anomaly Detection -- 3 Distance-based Anomaly Detection Approaches -- 4 Clustering-based Anomaly Detection Approaches -- 5 Model-based Anomaly Detection Approaches -- 6 Distance and Density Based Approaches -- 7 Rank Based Approaches -- 8 Ensemble Methods -- 9 Algorithms for Time Series Data -- Datasets for Evaluation -- Datasets for Time Series Experiments.

This book provides a readable and elegant presentation of the principles of anomaly detection,providing an easy introduction for newcomers to the field. A large number of algorithms are succinctly described, along with a presentation of their strengths and weaknesses. The authors also cover algorithms that address different kinds of problems of interest with single and multiple time series data and multi-dimensional data. New ensemble anomaly detection algorithms are described, utilizing the benefits provided by diverse algorithms, each of which work well on some kinds of data. With advancements in technology and the extensive use of the internet as a medium for communications and commerce, there has been a tremendous increase in the threats faced by individuals and organizations from attackers and criminal entities. Variations in the observable behaviors of individuals (from others and from their own past behaviors) have been found to be useful in predicting potential problems of various kinds. Hence computer scientists and statisticians have been conducting research on automatically identifying anomalies in large datasets. This book will primarily target practitioners and researchers who are newcomers to the area of modern anomaly detection techniques. Advanced-level students in computer science will also find this book helpful with their studies.

ISBN: 9783319675268

Standard No.: 10.1007/978-3-319-67526-8doiSubjects--Topical Terms:

3269771
Anomaly detection (Computer security)

LC Class. No.: TK5105.59

Dewey Class. No.: 005.8

Anomaly detection principles and algorithms
LDR:02814nmm a2200337 a 4500 001 2112266
003 DE-He213
005 20171118175901.0
006 m d
007 cr nn 008maaau
008 180719s2017 gw s 0 eng d
020 $a 9783319675268 $q (electronic bk.)
020 $a 9783319675244 $q (paper)
024 7 $a 10.1007/978-3-319-67526-8 $2 doi
035 $a 978-3-319-67526-8
040 $a GP $c GP
041 0 $a eng
050 4 $a TK5105.59
072 7 $a UNF $2 bicssc
072 7 $a UYQE $2 bicssc
072 7 $a COM021030 $2 bisacsh
082 0 4 $a 005.8 $2 23
090 $a TK5105.59 $b .M498 2017
100 1 $a Mehrotra, Kishan G. $3 3269769
245 1 0 $a Anomaly detection principles and algorithms $h [electronic resource] / $c by Kishan G. Mehrotra, Chilukuri K. Mohan, HuaMing Huang.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2017.
300 $a xxii, 217 p. : $b ill., digital ; $c 24 cm.
490 1 $a Terrorism, security, and computation, $x 2197-8778
505 0 $a 1 Introduction -- 2 Anomaly Detection -- 3 Distance-based Anomaly Detection Approaches -- 4 Clustering-based Anomaly Detection Approaches -- 5 Model-based Anomaly Detection Approaches -- 6 Distance and Density Based Approaches -- 7 Rank Based Approaches -- 8 Ensemble Methods -- 9 Algorithms for Time Series Data -- Datasets for Evaluation -- Datasets for Time Series Experiments.
520 $a This book provides a readable and elegant presentation of the principles of anomaly detection,providing an easy introduction for newcomers to the field. A large number of algorithms are succinctly described, along with a presentation of their strengths and weaknesses. The authors also cover algorithms that address different kinds of problems of interest with single and multiple time series data and multi-dimensional data. New ensemble anomaly detection algorithms are described, utilizing the benefits provided by diverse algorithms, each of which work well on some kinds of data. With advancements in technology and the extensive use of the internet as a medium for communications and commerce, there has been a tremendous increase in the threats faced by individuals and organizations from attackers and criminal entities. Variations in the observable behaviors of individuals (from others and from their own past behaviors) have been found to be useful in predicting potential problems of various kinds. Hence computer scientists and statisticians have been conducting research on automatically identifying anomalies in large datasets. This book will primarily target practitioners and researchers who are newcomers to the area of modern anomaly detection techniques. Advanced-level students in computer science will also find this book helpful with their studies.
650 0 $a Anomaly detection (Computer security) $3 3269771
650 1 4 $a Computer Science. $3 626642
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
650 2 4 $a Pattern Recognition. $3 891045
650 2 4 $a Security. $3 3134865
700 1 $a Mohan, Chilukuri K. $3 761940
700 1 $a Huang, HuaMing. $3 3269770
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Terrorism, security, and computation. $3 2139163
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-67526-8
950 $a Computer Science (Springer-11645)