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Current signature-based malware detection systems are heavily reliant on fixed patterns that struggle with unknown or evasive applications, while behavior-based solutions usually leave most of the interpretative work to a human analyst. In this paper, we propose a system able to explain anomalous behavior within a user session by considering anomalies identified through their deviation from a set of baseline process graphs. To minimize computational requirements we adapt star structures, a bipartite representation used to approximate the edit distance between two graphs. Baseline templates are generated automatically and adapt to the nature of the respective process. We prototypically implement smart anomaly explication through a number of competency questions derived and evaluated using the decision tree algorithm. The determined key factors are ultimately mapped to a dedicated APT attack stage ontology that considers actions, actors, as well as target assets.

Design of an Anomaly-based Threat Detection & Explication System