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Anomaly Detection Principles and Algorithms

  • Kishan G. Mehrotra
  • Chilukuri K. Mohan
  • HuaMing Huang

Part of the Terrorism, Security, and Computation book series (TESECO)

Table of contents

  1. Front Matter
    Pages i-xxii
  2. Principles

    1. Front Matter
      Pages 1-1
    2. Kishan G. Mehrotra, Chilukuri K. Mohan, HuaMing Huang
      Pages 3-19
    3. Kishan G. Mehrotra, Chilukuri K. Mohan, HuaMing Huang
      Pages 21-32
    4. Kishan G. Mehrotra, Chilukuri K. Mohan, HuaMing Huang
      Pages 33-39
    5. Kishan G. Mehrotra, Chilukuri K. Mohan, HuaMing Huang
      Pages 41-55
    6. Kishan G. Mehrotra, Chilukuri K. Mohan, HuaMing Huang
      Pages 57-94
  3. Algorithms

    1. Front Matter
      Pages 95-95
    2. Kishan G. Mehrotra, Chilukuri K. Mohan, HuaMing Huang
      Pages 97-117
    3. Kishan G. Mehrotra, Chilukuri K. Mohan, HuaMing Huang
      Pages 119-134
    4. Kishan G. Mehrotra, Chilukuri K. Mohan, HuaMing Huang
      Pages 135-152
    5. Kishan G. Mehrotra, Chilukuri K. Mohan, HuaMing Huang
      Pages 153-189
  4. Back Matter
    Pages 191-217

About this book

Introduction

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.

Keywords

Anomaly Detection Data Mining Rank Based Approach Security Applications Time Series Anomaly Detection Outlier Detection Clustering Classification Algorithms Machine Learning Statistical Pattern Recognition Time Series Ensemble Methods

Authors and affiliations

  • Kishan G. Mehrotra
    • 1
  • Chilukuri K. Mohan
    • 2
  • HuaMing Huang
    • 3
  1. 1.Department of Electrical Engineering and Computer ScienceSyracuse UniversitySyracuseUSA
  2. 2.Department of Electrical Engineering and Computer ScienceSyracuse UniversitySyracuseUSA
  3. 3.Department of Electrical Engineering and Computer ScienceSyracuse UniversitySyracuseUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-67526-8
  • Copyright Information Springer International Publishing AG 2017
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-67524-4
  • Online ISBN 978-3-319-67526-8
  • Series Print ISSN 2197-8778
  • Series Online ISSN 2197-8786
  • Buy this book on publisher's site