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InfoResearchPeer-reviewed
DriftTrace: Combating Concept Drift in Security Applications Through Detection and Explanation
- Published
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Summary
DriftTrace is a system that detects, explains, and adapts to concept drift in security machine learning models at the sample level, using a contrastive learning-based autoencoder and greedy feature selection for explanations. It reaches an average detection F1 score above 0.94 on Drebin, MalDroid2020 and IDS2018, beating the TRANSCENDENT baseline. Its explanation fidelity improves by an average of 76% over CADE.