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HYDRA: Proactive Android Malware Drift Adaptation via Hierarchical Graph Contrastive Learning

arXiv · AI, language, vision and robotics · article · Sep 22, 2026 · UTC

Concept drift, driven by the rapid evolution of Android malware, severely degrades the performance of machine learning detectors. Current adaptation strategies are often reactive, responding only after performance has dropped and imposing a significant manual annotation burden, or they are proactive but rely on unstable adversarial training and incomplete, single-level graph representations. To overcome these limitations, we propose HYDRA (Hybrid Drift Adaptation), a proactive adaptation framework that learns drift-invariant representations from hierarchically structured data. HYDRA first mode

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