7 papers
Enhancing Reliability in LLM-Based Secure Code Generation
Mohammed F. Kharma, Mohammad Alkhanafseh, Ahmed Sabbah +1
Large language models (LLMs) are widely used for code generation, but their security reliability remains inconsistent across languages and prompting strategies. Existing prompt eng…
An Empirical Evaluation of LLM-Generated Code Security Across Prompting Methods
Mohammed Kharma, Ahmed Sabbah, Mohammad Alkhanafseh +2
The growing use of Large Language Models (LLMs) for automated code generation has enhanced software development efficiency, but often at the cost of security. Generated code freque…
Concept Drift Adaptation Using Self-Supervised and Reinforcement Learning In Android Malware Detection
Ahmed Sabbah, Mohammad Kharma, Mohammad Alkhanafseh +3
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 mai…
Adversarial Vulnerability Under Temporal Concept Drift: A Longitudinal Study of Android Malware Detection
Ahmed Sabbah, Mohammed Kharma, Radi Jarrar +2
We present a longitudinal, drift-aware evaluation of adversarial robustness across more than a decade of Android applications using static and dynamic feature representations extra…
A Quasi-Experimental Developer Study of Security Training in LLM-Assisted Web Application Development
Mohammed Kharma, Ahmed Sabbah, Radi Jarrar +3
This paper presents a controlled quasi-experimental developer study examining whether a layer-based security training package is associated with improved security quality in LLM-as…
Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection
Ahmed Sabbah, Radi Jarrar, Samer Zein +1
Despite outstanding results, machine learning-based Android malware detection models struggle with concept drift, where rapidly evolving malware characteristics degrade model effec…