Machine Learning-Based SQL Injection Attack Detection: Intelligent Upgrade of Traditional Security Defense

This project aims to build a lightweight and implementable SQL injection detection system using Support Vector Machine (SVM) classifier and feature engineering techniques. Addressing the limitations of traditional defense solutions (such as parameterized queries and WAF rules), the system uses machine learning to adaptively identify malicious SQL injection attacks, providing efficient security protection for web applications. Key features include strong interpretability, fast training, and lightweight deployment, making it suitable for integration by small and medium teams.

SQL injection is a persistent threat in web application security, consistently ranking among the OWASP Top 10 vulnerabilities since it was documented in 1998. Attackers can steal data, tamper with databases, or even take control of servers by inserting malicious SQL code. Traditional defense methods have obvious shortcomings: parameterized queries require rewriting legacy code, which is costly; blacklist filtering is easily bypassed; WAF rules need continuous updates to deal with new variants. Machine learning technology provides a new approach to solving these problems.

The project adopts a three-layer architecture: data preprocessing layer (cleaning and standardizing SQL queries), feature engineering layer (extracting multi-dimensional features such as length, symbols, keywords, and structure), and classification decision layer (using SVM as the core classifier). The advantages of SVM include being friendly to small samples, strong generalization ability, fast inference speed, and good interpretability, making it suitable for real-time detection needs. Feature engineering captures the "behavioral fingerprint" of queries, which is more robust against variant attacks.

The project provides normal and attack query samples (e.g., normal query SELECT * FROM users WHERE id =1;, attack query SELECT * FROM users; DROP TABLE users; --). The detection process is: input reception → preprocessing → feature extraction → model inference → result output (normal/attack). The entire process is completed in milliseconds and can be seamlessly integrated into the web application request chain.

Compared to rule-matching WAFs, the ML solution can identify 0-day vulnerabilities and variant attacks; compared to parameterized queries, it can provide a security safety net without modifying existing code; compared to deep learning methods (such as LSTM and BERT), the SVM solution is lightweight, low-latency, more suitable for resource-constrained environments, and maintains a high detection rate.

Deployment scenarios include WAF enhancement (secondary verification), database access proxy (transparent protection), and SOC data source (improving response efficiency). Current limitations: lack of context awareness, adversarial sample risks, and false positive costs. Future improvement directions: context awareness, ensemble learning, continuous learning, and enhanced interpretability.