SQL Injection Detection Using Fine-Tuned CodeBERT

SQL injection attacks continue to pose a serious threat to web application security, with millions of systems vulnerable worldwide despite ongoing research and mitigation efforts. This study addresses the limitations of current SQLi detection techniques, particularly their inability to adapt to evolving attack patterns and their tendency to high false positive rates. A systematic review of machine learning- and deep learning-based SQL injection detection studies, published between 2017 and 2023, examined performance metrics, methods, and common shortcomings. Building on these insights, this study proposes a novel transformer-based approach utilizing a fine-tuned CodeBERT model trained on 30,919 SQL queries. This method includes extensive preprocessing and rigorous evaluation to ensure robustness and applicability. The results show that while traditional machine learning approaches reach between 73.5% and 99% accuracy, and deep learning models achieve 85% to 98%, the proposed CodeBERT-based system significantly outperforms them, attaining 99.90% accuracy, 99.96% precision, 97.75% recall, and 99.86% F1-score. These findings underscore the effectiveness of transformer models trained on code for SQL injection detection, setting new benchmarks and offering a deployable solution to enhance web application security.