Vulnerability Data

Vulnerability data research focuses on improving the detection and analysis of software vulnerabilities, primarily by addressing the scarcity and quality issues of existing datasets. Current efforts utilize various deep learning models, including large language models (LLMs) and graph neural networks (GNNs), often coupled with techniques like data augmentation and active learning to enhance model training and performance. These advancements aim to improve the accuracy and efficiency of vulnerability detection, ultimately contributing to more secure software systems and more effective vulnerability management practices. The field also emphasizes developing more realistic evaluation metrics and datasets to better reflect real-world scenarios.