Predictive Web Application Security Using Intelligent SQL Injection Detection

Abstract

Web applications have become an essential part of modern businesses, making them a common target for cyberattacks. Among various security threats, SQL injection remains one of the most dangerous because it allows attackers to manipulate database queries and gain unauthorized access to sensitive information. Traditional detection techniques often rely on predefined rules or signatures, which are less effective against newly emerging attack patterns. This work presents an intelligent approach for improving web application security through the prediction and detection of SQL injection attacks using machine learning techniques. The proposed framework processes SQL queries by performing data cleaning, feature extraction, and text preprocessing before training an ensemble classification model. The trained model distinguishes normal queries from malicious ones with high accuracy, enabling early identification of potential attacks. Performance is evaluated using standard metrics such as accuracy, precision, recall, and F1-score to verify the effectiveness of the system. The developed framework also provides a simple interface for analyzing new SQL queries and predicting their security status. This approach supports proactive protection of web applications by enabling faster, more reliable, and automated detection of SQL injection vulnerabilities.