Concept Drift Adaptation Using Self-Supervised and Reinforcement Learning In Android Malware Detection 事件

Concept Drift Adaptation Using Self-Supervised and Reinforcement Learning In Android Malware Detection arXiv:2605.24294v1 Announce Type: cross Abstract: Android malware detectors often degrade after deployment because of concept drift, while full retraining at each maintenance step is costly. We propose a chronological adaptive maintenance framework that models deployment-time maintenance as a sequential decision problem. The framework learns a stable latent representation through self-supervis