Securing Web Applications Against SQL Injection and XSS Attacks

This paper presents a comprehensive approach to enhancing web application security by mitigating two of the most prevalent and dangerous threats: SQL Injection (SQLi) and Cross-Site Scripting (XSS) attacks. Traditional defense mechanisms such as Web Application Firewalls (WAFs) and rule-based filtering often fall short due to their static nature and limited adaptability to novel or obfuscated attack vectors. To address these shortcomings, the proposed methodology integrates machine learning-based models trained on diverse datasets to accurately detect and classify malicious inputs. Extensive experiments were conducted in both controlled and real-time environments, evaluating the system’s performance using key metrics including accuracy, precision, recall, and F1 score. The results demonstrate that the machine learning model significantly outperforms traditional methods, achieving a detection accuracy of 96.4%, with high precision and recall values, thus offering both effectiveness and efficiency. The system also exhibits scalability and adaptability, making it suitable for deployment in live web applications. This research highlights the critical role of intelligent, data-driven systems in modern cybersecurity frameworks and establishes a strong foundation for future work focused on developing proactive and resilient web application defenses.