What do the detection of credit card fraud, seizures in electro-encephalogram data and malware in computer systems have in common? In each of these examples, the main task is to detect an abnormality that may not have been seen before, based on its comparison to known “normal” data. Anomaly detection algorithms address such tasks. These are the focus of the new book authored by Research and Emeritus Professor Kishan Mehrotra, Professor Chilukuri Mohan and HuaMing Huang G ’09, G ’13, just published by Springer, titled “Anomaly Detection Principles and Algorithms.”
The new book “Anomaly Detection Principles and Algorithms” teaches how to find anomalies in digital data.
Their book provides an introduction for newcomers to the field and covers algorithms that address different kinds of problems of interest with single and multiple time series data and multidimensional 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.
Springer says, “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.”
