document: 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 .
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