Concept Drift Adaptation Using Self-Supervised and Reinforcement Learning In Android Malware Detection
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-supervised learning during initialization, freezes the encoder, measures latent drift in the fixed representation space, and performs lightweight downstream adaptation using a trainable adapter and classification head. A proximal policy optimization controller selects low-cost maintenance actions based on the detector state, including current utility, reten...
| Authors | Ahmed Sabbah, Mohammad Kharma, Mohammad Alkhanafseh et al. |
|---|---|
| Primary category | cs.CR |
| Published | 22 May 2026 |
| Last updated at source | 22 May 2026 |
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