Security Vulnerability Detection Method of Open-source Software Based on Graph Attention Network

With the popularity of open source software, software security problems have become increasingly prominent. Traditional vulnerability detection methods are no longer able to cope with today’s complex virus vulnerabilities, and the detection results often have certain limitations. Therefore, an open source software security vulnerability detection method based on graph attention network is proposed. Firstly, the graph attention network model is constructed, and the source code detection is used to identify and detect the graph attention vulnerability. In order to verify the performance of the graph attention network model in the proposed method, the paper compares the proposed model with Drebin algorithm. The results show that the graph attention network model can effectively detect the number of security vulnerabilities in open source software, and its maximum error value is 4. Compared with Drebin algorithm, the security vulnerability detection method has higher accuracy and efficiency.