Overlapping Communities for Identifying Misbehavior in Network Communications

In this paper, we study the problem of identifying misbehaving network communications using community detection algorithms. Recently, it was shown that identifying the communications that do not respect community boundaries is a promising approach for network intrusion detection. However, it was also shown that traditional community detection algorithms are not suitable for this purpose. In this paper, we propose a novel method for enhancing community detection algorithms, and show that contrary to previous work, they provide a good basis for network misbehavior detection. This enhancement extends disjoint communities identified by these algorithms with a layer of auxiliary communities, so that the boundary nodes can belong to several communities. Although non-misbehaving nodes can naturally be in more than one community, we show that the majority of misbehaving nodes belong to multiple overlapping communities, therefore overlapping community detection algorithms can also be deployed for intrusion detection. Finally, we present a framework for anomaly detection which uses community detection as its basis. The framework allows incorporation of application-specific filters to reduce the false positives induced by community detection algorithms. Our framework is validated using large email networks and flow graphs created from real network traffic