Iowa State University, ProQuest Dissertations & Theses Global '24
Understanding and improving deep learning models for vulnerability detection
In this dissertation, we comprehensively evaluate state-of-the-art (SOTA) DL vulnerability detection models and propose a body of approaches for improving DL for vulnerability detection using static and dynamic analysis.
In this dissertation, we comprehensively evaluate state-of-the-art (SOTA) DL vulnerability detection models, including Graph Neural Networks (GNNs), fine-tuned transformer models, and Large Language Models (LLMs), yielding a deeper understanding of their benefts and limitations and a body of approaches for improving DL for vulnerability detection using static and dynamic analysis.