APPLIED MACHINE LEARNING PREDICTIVE ANALYSIS TO SQL INJECTION ATTACK DETECTION AND PREVENTION

Abstract

SQL Injection (SQLi) attacks are among the most critical security threats to web applications, allowing attackers to manipulate database queries and gain unauthorized access to sensitive information. This project focuses on applying machine learning techniques for predictive analysis to detect and prevent SQL injection attacks effectively. The system analyzes web request data and query patterns to identify malicious inputs that may compromise database security. By using supervised machine learning algorithms such as Decision Tree, Random Forest, and Support Vector Machine (SVM), the model learns to distinguish between normal and malicious queries based on extracted features. The proposed system includes data preprocessing steps such as tokenization, feature extraction, and normalization to improve model accuracy. The trained model is capable of detecting SQL injection patterns in real-time and preventing attacks before they reach the database layer. Additionally, the system can adapt to new attack patterns by continuously learning from updated datasets. Visualization techniques are also used to analyze attack trends and model performance. Experimental results demonstrate that machine learning-based approaches can achieve high detection accuracy and significantly reduce false positives compared to traditional rule-based systems. However, challenges such as evolving attack techniques and dataset imbalance may affect performance. Overall, this project highlights the effectiveness of integrating machine learning into cybersecurity systems for proactive threat detection and prevention.