Feature Fusion-Based Detection of SQL Injection and XSS Attacks

With the rapid development of the internet, network security issues are becoming increasingly severe. SQL injection attacks and XSS attacks are two common network attack methods that pose serious threats to the security of websites and applications. Traditional feature-matching-based detection methods struggle to cope with increasingly complex attack techniques, necessitating the development of more effective detection methods. This paper proposes a deep learning-based model for detecting SQL injection and XSS attacks, which integrates local and global features, captures long-term dependencies in sequential data, and focuses more on the relevant parts of the input data. Experimental results show that this model achieves superior performance compared to other mainstream methods across multiple datasets.