Ai-Based Web Application Firewall

Web applications face an increasing risk of webbased attacks such as SQL Injection (SQLi) and Cross-Site Scripting (XSS), making the need for securing them more important than ever. This project focuses on building an AIpowered Web Application Firewall (WAF) that can identify and block such attacks using machine learning in real-time. We used publicly available datasets from Kaggle and performed preprocessing steps such as text cleaning and count vectorization to make the data analysis-ready. The models used include Logistic Regression, Random Forest, and Support Vector Machine (SVM). For SQLi, Logistic Regression achieved 96.83% accuracy, while for XSS, Random Forest achieved 99.88%. These models were embedded in a web application that takes user input, and the backend evaluates it to determine if it is a safe or malicious query, providing quick feedback. These results demonstrate that AI-powered firewalls can effectively detect and block SQLi and XSS attacks in web applications, helping secure them against data loss. We approached the project of protecting web apps from vulnerabilities through machine learning to provide a scalable solution and explore ML within cybersecurity.