Detecting Lateral Movement in Enterprise Computer Networks with Unsupervised Graph AI

Authors: 

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

Abstract: 

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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BibTeX
@inproceedings {259729,
author = {Benjamin Bowman and Craig Laprade and Yuede Ji and H. Howie Huang},
title = {Detecting Lateral Movement in Enterprise Computer Networks with Unsupervised Graph {AI}},
booktitle = {23rd International Symposium on Research in Attacks, Intrusions and Defenses ({RAID} 2020)},
year = {2020},
isbn = {978-1-939133-18-2},
address = {San Sebastian},
pages = {257--268},
url = {https://www.usenix.org/conference/raid2020/presentation/bowman},
publisher = {{USENIX} Association},
month = oct,
}