Advanced Detection and Prevention of SQL Injection Attacks Using Machine Learning Techniques for Enhanced Web Security

Abstract

In this paper the SQL injection attacks (SQLIAs) continue to pose significant threats to web applications, exploiting vulnerabilities in poorly secured databases. Traditional detection and prevention mechanisms often struggle to adapt to the evolving techniques employed by attackers. This paper presents an advanced framework for detecting and preventing SQL injection attacks using machine learning techniques to enhance web security. The proposed system employs a combination of supervised and unsupervised learning models to analyze query patterns, identify anomalies, and classify malicious inputs in real time. Our methodology involves preprocessing web application traffic, feature extraction from SQL queries, and model training using labeled datasets. Various algorithms, including decision trees, support vector machines, and neural networks, were evaluated to determine their effectiveness in detecting SQLIAs. The results demonstrate that machine learning-based approaches can significantly improve the detection and prevention of SQL injection attacks compared to traditional rule-based methods. This study also highlights the importance of continuous learning and adaptation in cyber security frameworks. The proposed solution provides a robust and scalable tool for enhancing web application security, paving the way for further research into the integration of artificial intelligence in cyber security.