David Mohaisen

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 introduces the Mitigation-Aware Chain-of-Thought (MA-CoT) framework, which significantly enhances the security reliability of code generated by LLMs across multiple languages and models.

The paper empirically evaluates the security quality of LLM-generated code across various prompting methods, finding that while prompting alters the structure of weaknesses, it is insufficient to reliably reduce overall vulnerability levels.

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.

The paper develops a comprehensive, GDPR-aligned item bank of 527 statements to accurately measure user preferences regarding specific regulatory protections, addressing a gap left by older privacy measurement tools.