Year: 2007
Type: report
Abstract: Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.
Authors: Varun Chandola, Arindam Banerjee, Vipin Kumar
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: Collaborative Research: CRI - Scalable Benchmarks, Software and Data for Data Mining, Analytics and Scientific Discoveries, UNIVERSITY OF MINNESOTAPI- JAIDEEP SRIVASTAVAAERONAUTICAL SYSTEMS ARE COMPLEX AND HEAVILY INSTRUMENT, III-CTX: Collaborative Research: Spatio-Temporal Data Mining For Global Scale Eco-Climatic Data, Data Mining for Rare Class Analysis, ITR: COLLABORATIVE RESEARCH: A Data Mining and Exploration Middleware for Grid and Distributed Computing
OpenAlex ID: W7064111672