Graph-based anomaly detection

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. Categories and Subject Descriptors H.2.8 [Database Management]: Database Applications - Data Mining. Keywords...

• Type: Conference
• Year: 2003
• Where: KDD
• Authors: Caleb C. Noble, Diane J. Cook
• Keywords: Data Mining, Graph-based Anomaly Detection, KDD 2003, Network Intrusion Detection, Successful Anomaly Detection