Directional Laplacian Centrality for Cyber Situational Awareness
This addresses the challenge of cyber situational awareness for analysts by providing a method to detect anomalies without prior signatures, though it appears incremental as it builds on spectral graph theory.
The authors tackled the problem of detecting malicious events in cyber operations by introducing a new graph-theoretic centrality measure based on the derivative of the graph Laplacian matrix, which identified important IP addresses in network flow data with noticeable changes even for small injected anomalies.
Cyber operations is drowning in diverse, high-volume, multi-source data. In order to get a full picture of current operations and identify malicious events and actors analysts must see through data generated by a mix of human activity and benign automated processes. Although many monitoring and alert systems exist, they typically use signature-based detection methods. We introduce a general method rooted in spectral graph theory to discover patterns and anomalies without a priori knowledge of signatures. We derive and propose a new graph-theoretic centrality measure based on the derivative of the graph Laplacian matrix in the direction of a vertex. To build intuition about our measure we show how it identifies the most central vertices in standard network data sets and compare to other graph centrality measures. Finally, we focus our attention on studying its effectiveness in identifying important IP addresses in network flow data. Using both real and synthetic network flow data, we conduct several experiments to test our measure's sensitivity to two types of injected attack profiles, and show that vertices participating in injected attack profiles exhibit noticeable changes in our centrality measures, even when the injected anomalies are relatively small, and in the presence of simulated network dynamics.