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Anomaly detection principles and algorithms

Author: Kishan G Mehrotra; Chilukuri K Mohan; HuaMing Huang
Publisher: Cham : Springer, 2017.
Series: Terrorism, security, and computation.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
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  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Mehrotra, Kishan G.
Anomaly Detection Principles and Algorithms.
Cham : Springer International Publishing, ©2017
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Kishan G Mehrotra; Chilukuri K Mohan; HuaMing Huang
ISBN: 9783319675268 3319675265
OCLC Number: 1012883414
Description: 1 online resource (229 pages).
Contents: 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.
Series Title: Terrorism, security, and computation.
Responsibility: Kishan G. Mehrotra, Chilukuri K. Mohan, HuaMing Huang.

Abstract:

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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"This book presents the interesting topic of anomaly detection for a very broad audience. ... The presentation is really useful: for each technique, some motivation is given, including real-life Read more...

 
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