Detecting Lateral Movement: An Ensemble Learning and Data Visualization ApproachPublic
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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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