Radi Jarrar
3 indexed papers
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The study found that providing developers with a layer-based security training package significantly reduces the number and severity of security vulnerabilities in LLM-assisted web application development.
The paper proposes a cost-aware, adaptive maintenance framework using Reinforcement Learning (RL) and self-supervised learning to mitigate performance degradation (concept drift) in Android malware detectors without requiring full retraining.
This study longitudinally evaluates the adversarial robustness of Android malware detection systems over a decade, finding that temporal separation significantly degrades robustness due to concept drift.
Papers
Concept Drift Adaptation Using Self-Supervised and Reinforcement Learning In Android Malware Detection
The paper proposes a cost-aware, adaptive maintenance framework using Reinforcement Learning (RL) and self-supervised learning to mitigate performance degradation (concept drift) in Android malware de…