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In a variety of applications, one desires to detect groups of anomalous datasamples, with a group potentially manifesting its atypicality (relative to areference model) on a low-dimensional subset of the full measured set offeatures. Samples may only be weakly atypical individually, whereas they may bestrongly atypical when considered jointly. What makes this group anomalydetection problem quite challenging is that it is a priori unknown which subsetof features jointly manifests a particular group of anomalies. Moreover, it isunknown how many anomalous groups are present in a given data batch. In thiswork, we develop a group anomaly detection (GAD) scheme to identify the subsetof samples and subset of features that jointly specify an anomalous cluster. Weapply our approach to network intrusion detection to detect BotNet andpeer-to-peer flow clusters. Unlike previous studies, our approach captures andexploits statistical dependencies that may exist between the measured features.Experiments on real world network traffic data demonstrate the advantage of ourproposed system, and highlight the importance of exploiting feature dependencystructure, compared to the feature (or test) independence assumption made inprevious studies.

Detecting Clusters of Anomalies on Low-Dimensional Feature Subsets with Application to Network Traffic Flow Data