Anomaly Detection on Bipartite Graphs for Cyber Situational Awareness and Threat Detection

Data from cyber logs can often be represented as a bipartite graph (e.g. internal IP-external IP, user-application, or client-server). State-of-the-art graph based anomaly detection often generalizes across all types of graphs -- namely bipartite and non-bipartite. This confounds the interpretation and use of specific graph features such as degree, page rank, and eigencentrality that can provide a security analyst with rapid situational awareness of their network. Furthermore, graph algorithms applied to data collected from large, distributed enterprise scale networks require accompanying methods that allow them to scale to the data collected. In this paper, we provide a novel, scalable, directional graph projection framework that operates on cyber logs that can be represented as bipartite graphs. This framework computes directional graph projections and identifies a set of interpretable graph features that describe anomalies within each partite.