A Statistical Framework for Handling Network Anomalies

This paper proposes a statistical framework to automatically identify anomalous nodes in static networks. In our approach, we first associate to each node a neighborhood cohesiveness feature vector such that each element of this vector corresponds to a score quantifying the node's neighborhood connectivity, as estimated by a specific similarity measure. Next, based on the estimated node's feature vectors, we view the task of identifying anomalous nodes from a mixture modeling perspective, based on which we elaborate a statistical approach that exploits the Dirichlet distribution to automatically identify anomalies. The suitability of the proposed method is illustrated through experiments on both synthesized and real networks.