Mini-Issue on Anomaly Detection

Abstract

This issue of Technometrics includes three articles that highlight interesting applications of statistical methods for detecting unusual circumstances, or anomalies. The articles explore the methods that have been developed to address these problems and describe additional challenges that lie ahead. Our purpose in publishing these articles is to inform readers of some of the exciting applications and research in anomaly detection outside the bounds of traditional industrial statistics. Vijay Nair recruited and edited these articles and I want to thank Vijay for bringing this important topic to Technometrics. We begin with two papers on fraud detection. The first paper, by Sudjianto et al., provides an overview of the problems faced by the financial industry. The goal is to identify fraudulent activity within a massive stream of transaction data, which could be from a bank account, a credit card, an organization, etc. The paper describes some of the statistical techniques currently used, and the research challenges in the field. The second paper, by Becker, Volinsky, and Wilks, reviews fraud detection in telecommunications, with a focus on work that has been done at AT&T. It discusses statistical techniques, visualization tools, computing environment, and data management issues that have been important in improving fraud detection capability. These two papers are followed by a commentary from David Hand, who expands on the contributions and provides his own perspectives from involvement in a number of related applications. The third paper, by Shmueli and Burkom, deals with modern biosurveillance, where the goal is early detection of the outbreak of diseases. The paper describes various types of new data sources, the current state of monitoring, and the statistical challenges in modern biosurveillance. There are many other applications areas where anomaly detection issues arise. Some examples include early detection of warranty problems, intrusion detection in computer networks, and fault detection in sensor networks. While all of these can be viewed at a basic level as a problem of monitoring underlying processes and detecting changes, the issues and statistical challenges are quite different from traditional statistical process monitoring. We are pleased to use this special collection of articles to expose Technometrics readers to some of the fascinating problems and statistical research questions currently stimulated by anomaly detection.