Yan-Shuai Yin

Geometry-Aware Risk-Guided Active Learning for Malware Concept Drift Adaptation

Mobile malware evolution induces concept drift, degrading the performance of static supervised detectors. Concept Drift Adaptation with Active Learning (CDA-AL) mitigates this problem by querying informative samples for model updating, but existing methods still face two limitations. First, uncertainty-based selection...