Concept Drift Adaptation Using Self-Supervised and Reinforcement Learning In Android Malware Detection
📰 ArXiv cs.AI
Learn to adapt Android malware detectors to concept drift using self-supervised and reinforcement learning, improving detection accuracy over time
Action Steps
- Initialize a self-supervised learning model to learn a stable latent representation of Android malware data
- Freeze the encoder and measure latent drift in the fixed representation over time
- Use reinforcement learning to adapt the model to concept drift by selecting optimal maintenance actions
- Test and evaluate the adapted model on a held-out dataset to measure its improved detection accuracy
- Deploy the adapted model in a production environment and continuously monitor its performance
Who Needs to Know This
Machine learning engineers and cybersecurity professionals can benefit from this approach to maintain and improve the accuracy of their Android malware detection systems
Key Insight
💡 Self-supervised learning and reinforcement learning can be combined to adapt Android malware detectors to concept drift, improving detection accuracy over time
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Adapt Android malware detectors to concept drift using self-supervised & reinforcement learning #AI #cybersecurity
Key Takeaways
Learn to adapt Android malware detectors to concept drift using self-supervised and reinforcement learning, improving detection accuracy over time
Full Article
Title: Concept Drift Adaptation Using Self-Supervised and Reinforcement Learning In Android Malware Detection
Abstract:
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-supervised learning during initialization, freezes the encoder, measures latent drift in the fixed representatio
Abstract:
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-supervised learning during initialization, freezes the encoder, measures latent drift in the fixed representatio
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