Detecting Lateral Movement in Enterprise Computer Networks with Unsupervised Graph AI


Benjamin Bowman, Craig Laprade, Yuede Ji, and H. Howie Huang, Graph Computing Lab, George Washington University


In this paper we present a technique for detecting lateral movement of Advanced Persistent Threats inside enterprise-level computer networks using unsupervised graph learning. Our detection technique utilizes information derived from industry standard logging practices, rendering it immediately deployable to real-world enterprise networks. Importantly, this technique is fully unsupervised, not requiring any labeled training data, making it highly generalizable to different environments. The approach consists of two core components: an authentication graph, and an unsupervised graph-based machine learning pipeline which learns latent representations of the authenticating entities, and subsequently performs anomaly detection by identifying low-probability authentication events via a learned logistic regression link predictor. We apply this technique to authentication data derived from two contrasting data sources: a small-scale simulated environment, and a large-scale real-world environment. We are able to detect malicious authentication events associated with lateral movement with a true positive rate of 85% and false positive rate of 0.9%, compared to 72% and 4.4% by traditional rule-based heuristics and non-graph anomaly detection algorithms. In addition, we have designed several filters to further reduce the false positive rate by nearly 40%, while reducing true positives by less than 1%.

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