In recent years, deep learning has completely changed many machine learning tasks, and the data in these tasks is usually expressed in Euclidean space. However, as more and more applications need to use non-Euclidean data, vulnerability mining is becoming more and more important. With the successful development of neural networks, many machine learning tasks, such as object detection, image classification, and speech recognition, once relied heavily on manual feature engineering to extract features, and can now be completed with various end-to-end deep learning models, such as Convolutional neural network, long and short-term memory networketc.Vulnerability mining is an important way to prevent and control system vulnerabilities. Traditional methods of vulnerability mining can no longer meet people's needs. In order to enable the vulnerability mining application to meet people's needs, we established a related source code vulnerability mining model based on graph neural networks. By investigating relevant literature, conducting interviews with professionals, etc., collected data from databases such as HowNet, Wanfang Database, SSCI, etc., and built a model of source code vulnerability mining based on graph neural networks using parallel algorithms. Through simulation, we found that the method of mining source code vulnerabilities based on graph neural networks is becoming more and more accepted by people, and the increase in 2016 reached 0.16. Moreover, the efficiency of source code vulnerability mining based on graph neural network is much higher than other vulnerability mining methods, and the mining speed is more than 20% ahead of other mining methods. This shows that source code vulnerability mining based on graph neural network can play an important role in preventing system vulnerabilities.

Guo Li. Source Code Vulnerability Mining Method Based on Graph Neural Network. International Journal of Frontiers in Engineering Technology (2022), Vol. 4, Issue 4: 21-32. https://doi.org/10.25236/IJFET.2022.040404.

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