Yangzhou University

Sicong Cao

Wei Liu

Bin Li

Xiaobing Sun

Lili Bo

Xiaoxue Wu

Disclosed in the present disclosure are an explainable vulnerability detection method and system based on dual-view causal reasoning. The vulnerability detection method includes: S, obtaining code samples, where the code samples include a training sample and a sample to be detected, sequentially performing data augmentation, static analysis, code property graph construction and feature extraction on the training sample, and obtaining a training data set; and sequentially performing static analysis, code property graph construction and feature extraction on the sample to be detected, and obtaining a data set to be detect; S, processing the training data set through a hybrid contrastive learning method, and establishing a vulnerability detection model; and inputting the data set to be detected into the vulnerability detection model, and outputting a vulnerability code; and S, performing causal reasoning on the vulnerability code, and outputting a vulnerability detection explanation.

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Jiangsu, China

Sicong Cao