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Anomaly detection principles and alg...

Mehrotra, Kishan G.

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Anomaly detection principles and algorithms

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Anomaly detection principles and algorithms/ by Kishan G. Mehrotra, Chilukuri K. Mohan, HuaMing Huang.
作者:	Mehrotra, Kishan G.
其他作者:	Mohan, Chilukuri K.
出版者:	Cham :Springer International Publishing : : 2017.,
面頁冊數:	xxii, 217 p. :ill., digital ;24 cm.
內容註:	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
標題:	Anomaly detection (Computer security) -
電子資源:	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
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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.
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