4 papers
LLMVul: A Vulnerability-Labeled Dataset of LLM-Generated C/C++ Functions from Real Production Repositories
Mohammad Farhad, Shuvalaxmi Dass
Large language models (LLMs) are increasingly used to generate and assist with software development, yet existing vulnerability datasets largely focus on human-written code or cont…
MTD-Playground: An Attacker-Aware Evaluation Framework for Network Moving Target Defense
Mohammad Farhad, Mohoshin Ara Tahera, Padam Jung Thapa +2
Moving Target Defense (MTD) has emerged as a proactive network cyber defense paradigm that increases attacker uncertainty through dynamic network reconfiguration techniques such as…
Residual Risk Assessment in Benign Code: How Far Are We? A Multi-Model Semantic and Structural Similarity Approach
Mohammad Farhad, Shuvalaxmi Dass
Software security assurance relies on effective vulnerability detection and patching, yet determining whether a patch fully eliminates risk remains an underexplored challenge. Exis…
HYDRA: A Hybrid Heuristic-Guided Deep Representation Architecture for Predicting Latent Zero-Day Vulnerabilities in Patched Functions
Mohammad Farhad, Sabbir Rahman, Shuvalaxmi Dass
Software security testing, particularly when enhanced with deep learning models, has become a powerful approach for improving software quality, enabling faster detection of known f…