Deep learning (DL) models of code have recently reported great progress for vulnerability
detection. In some cases, DL-based models have outperformed static analysis tools …

Prior studies have demonstrated the effectiveness of Deep Learning (DL) in automated
software vulnerability detection. Graph Neural Networks (GNNs) have proven effective in …

Improving and enhancing the effectiveness of software vulnerability detection methods is
urgently needed today. In this study, we propose a new source code vulnerability detection …

Automatic software vulnerability detection has caught the eyes of researchers as because
software vulnerabilities are exploited vehemently causing major cyber-attacks. Thus …

摘要图深度学习技术在处理非欧氏结构数据中显示了巨大潜力, 大量研究工作尝试将图嵌入或图
神经网络应用到漏洞检测中. 梳理了基于图深度学习的漏洞检测方法, 按其一般流程 …

Code similarity analysis quantitatively measures the similarity between two existing source
codes by matching their signatures defined by code analyst. It is proven to be a promising …

Software vulnerabilities pose a significant threat to computer security, and accurate
identification of vulnerabilities has become a challenging task. Although previous work on …

The recent advances made by graph-based deep learning have demonstrated its great
potential in processing non-Euclidean structured data, and a large number of research …