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

arXiv: https://arxiv.org/abs/2605.24294 · HTML: https://mlsanity.com/paper/2605.24294/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 39 references that are in the map.
- First posted on arXiv 22 May 2026.

## Builds on (most cited first, 14 of 14)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time](https://mlsanity.com/paper/1807.07838/) (2018)
- [Transcending Transcend: Revisiting Malware Classification in the Presence of Concept Drift](https://mlsanity.com/paper/2010.03856/) (2020)
- [Continuous Learning for Android Malware Detection](https://mlsanity.com/paper/2302.04332/) (2023)
- [MORPH: Towards Automated Concept Drift Adaptation for Malware Detection](https://mlsanity.com/paper/2401.12790/) (2024)
- [Combating Concept Drift with Explanatory Detection and Adaptation for Android Malware Classification](https://mlsanity.com/paper/2405.04095/) (2024)
- [DRMD: Deep Reinforcement Learning for Malware Detection under Concept Drift](https://mlsanity.com/paper/2508.18839/) (2025)
- [LAMDA: A Longitudinal Android Malware Benchmark for Concept Drift Analysis](https://mlsanity.com/paper/2505.18551/) (2025)
- [Efficient Concept Drift Handling for Batch Android Malware Detection Models](https://mlsanity.com/paper/2309.09807/) (2023)
- [ActDroid: An active learning framework for Android malware detection](https://mlsanity.com/paper/2401.16982/) (2024)
- [Regression-aware Continual Learning for Android Malware Detection](https://mlsanity.com/paper/2507.18313/) (2025)
- [Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection](https://mlsanity.com/paper/2507.22772/) (2025)
- [MADCAT: Combating Malware Detection Under Concept Drift with Test-Time Adaptation](https://mlsanity.com/paper/2505.18734/) (2025)
- [ADAPT: A Pseudo-labeling Approach to Combat Concept Drift in Malware Detection](https://mlsanity.com/paper/2507.08597/) (2025)

## Abstract (arXiv)

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, retention on a fixed memory set, latent drift indicators, and update cost. We evaluate the framework under a causal deployment-style protocol on emulator and real Android malware datasets with static and dynamic features. Results show that the RL controller provides a strong cost-aware adaptation strategy, consistently remaining among the top-performing policies while achieving a favorable balance between temporal performance, memory retention, and maintenance cost under non-stationary deployment conditions.