Anomaly Detection Principles and Algorithms / by Kishan G. Mehrotra, Chilukuri K. Mohan, HuaMing Huang.

Format:

Book

Author/Creator:

Mehrotra, Kishan G., author.

Mohan, Chilukuri K., author.

Huang, Huaming, author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Terrorism, security, and computation 2197-8778

Terrorism, Security, and Computation, 2197-8778

Language:

English

Subjects (All):

Data mining.

Pattern perception.

Data protection.

Data Mining and Knowledge Discovery.

Pattern Recognition.

Security.

Local Subjects:

Data Mining and Knowledge Discovery.

Pattern Recognition.

Security.

Physical Description:

1 online resource (XXII, 217 pages) : 66 illustrations, 55 illustrations in color.

Edition:

First edition 2017.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2017.

System Details:

text file PDF

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 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.

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.

Other Format:

Printed edition:

ISBN:

978-3-319-67526-8

9783319675268

Access Restriction:

Restricted for use by site license.