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 numberof algorithms are succinctly described, along with a presentation of theirstrengths and weaknesses.
The authors also cover algorithms that addressdifferent kinds of problems of interest with single and multiple time seriesdata and multi-dimensional data. New ensemble anomaly detectionalgorithms are described, utilizing the benefits provided by diversealgorithms, each of which work well on some kinds of data.
With advancements in technology and the extensive use of the internet asa medium for communications and commerce, there has been atremendous increase in the threats faced by individuals and organizationsfrom attackers and criminal entities. Variations in the observable behaviorsof individuals (from others and from their own past behaviors) have beenfound to be useful in predicting potential problems of various kinds. Hencecomputer 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.
Table of Contents1 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.