Understanding and improving deep learning models for vulnerability detection
Published in Iowa State University, ProQuest Dissertations & Theses Global, 2024
Recommended citation: Benjamin Steenhoek. 2024. Understanding and improving deep learning models for vulnerability detection (Publication No. 31562057). Available from Dissertations & Theses @ Iowa State University; ProQuest Dissertations & Theses Global. https://benjijang.com/files/2024-12-19-dissertation.pdf
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.