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 efforts have attempted to apply graph embeddings or graph neural networks to vulnerability detection. This survey systematically investigates the vulnerability detection based on graph deep learning. Firstly, we summarize the four main stages of the vulnerability detection process, including data set, graph data preparation, graph deep learning model construction, and result evaluation. Then, starting from the effectiveness of graph-based deep learning vulnerability detection, we respectively expound the research results based on code patterns, code similarity and specific application scenarios. Finally, by sorting out and summarizing the existing research works, we analyze the challenges and foresee the trends in this research field.

cybersecurity  /  vulnerability detection  /  graph-based deep learning  /  graph embedding  /  graph neural networks