LATIDIA · Ciberseguridad

HYDRA: Adaptación proactiva de la deriva del malware de Android a través del aprendizaje contrastivo de gráficos jerárquicos

arXiv:2609.26352v1 Tipo de anuncio: nuevo Resumen: la deriva conceptual, impulsada por la rápida evolución del malware para Android, degrada gravemente el rendimiento de los detectores de aprendizaje automático. Las estrategias de adaptación actuales a menudo son re

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arXiv:2609.26352v1 Announce Type: new Abstract: 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 models applications using a hybrid graph structure, combining fine-grained Control Flow Graphs (CFGs) and coarse-grained Function Call Graphs (FCGs) to capture comprehensive behavioral patterns. It then introduces a novel