Detecting Lateral Movement: An Ensemble Learning and Data Visualization Approach

Longo, Madeleine C.; Vossoughi, Ian; Martin, Jeffrey Alan

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Detecting Lateral Movement: An Ensemble Learning and Data Visualization Approach

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In this Major Qualifying Project, we explored utilizing ensemble learning and data visualization to detect lateral movement from Advanced Persistent Threats (APTs) in enterprise networks. We developed a detection framework for analysts to pinpoint malicious events within a cybersecurity dataset from Los Alamos National Laboratory. Our project produced two primary findings: ensemble learning significantly improved the detection rate of malicious events, and a heatmap visualization can provide promising indications of suspicious activity, but remains ultimately insufficient for reliably identifying APTs.

This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.

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