Graph-based anomaly detection

Authors:
Caleb C. Noble;Diane J. Cook
Affiliations:
University of Texas at Arlington, Arlington, TX;University of Texas at Arlington, Arlington, TX
Venue:
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Year:
2003

Citing 4
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Abstract

Anomaly detection is an area that has received much attention in recent years. It has a wide variety of applications, including fraud detection and network intrusion detection. A good deal of research has been performed in this area, often using strings or attribute-value data as the medium from which anomalies are to be extracted. Little work, however, has focused on anomaly detection in graph-based data. In this paper, we introduce two techniques for graph-based anomaly detection. In addition, we introduce a new method for calculating the regularity of a graph, with applications to anomaly detection. We hypothesize that these methods will prove useful both for finding anomalies, and for determining the likelihood of successful anomaly detection within graph-based data. We provide experimental results using both real-world network intrusion data and artificially-created data.