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.
