Identification of SQL Injection Security Vulnerabilities in Web applications Based on Binary Code Similarity

The existing SQL injection security vulnerability identification technology for Web applications has inherent flaws, which are relatively passive in defense methods, and cannot deal with increasingly changeable attack methods. In order to improve the accuracy of SQL injection security vulnerability identification of Web applications, this paper uses an improved skip-gram model to realize unsupervised learning of the embedding process, converts the information related to program functions contained in the vertices of the basic block into feature vectors to obtain the ACFG vector of the basic block, and measures the similarity of binary functions by evaluating the similarity of feature vectors. The experimental results show that the technical processing route proposed in this paper can effectively compare binary functions with different architectures and optimization levels, and use the advantages of neural networks to obtain higher accuracy and better analysis efficiency, thereby effectively improving the identification effect of SQL injection security vulnerabilities in Web applications. Therefore, it can play a certain role in the security management of subsequent Web applications.