Combating Concept Drift with Explanatory Detection and Adaptation for Android Malware Classification

Machine learning-based Android malware classifiers struggle with concept drift: the rapid evolution of malware, especially with new families, can depress classification accuracy to near-random levels. Previous research has largely centered on detecting drift samples, with expert-led label revisions on these samples to guide model retraining. However, these methods often lack a comprehensive understanding of malware concepts and provide limited guidance for effective drift adaptation, leading to high human labeling costs.