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@article{196106,
        author = {Abdul Rahman K and Ajmal Ahamed A and J Maria Shyla},
        title = {A Machine Learning Approach for Detecting SQL Injection Attacks in Database System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {1479-1483},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196106},
        abstract = {SQL Injection (SQLi) remains a pervasive threat to database-driven applications, often bypassing traditional signature-based defenses through polymorphic and obfuscated query patterns. This paper proposes an intelligent detection framework leveraging supervised Machine Learning (ML) to classify SQL queries as either benign or malicious. By transitioning from rigid rule-based filtering to dynamic feature extraction and pattern recognition, the proposed system demonstrates high classification accuracy and adaptability to zero-day vulnerabilities. Our methodology utilizes a multi-algorithmic approach—including Random Forest and Support Vector Machines—to provide a scalable, real-time security layer for modern database architectures.},
        keywords = {SQL Injection, Machine Learning, Cybersecurity, Database Security, Anomaly Detection, Supervised Learning.},
        month = {April},
        }