Web Application Firewall Using Machine Learning

In today's digital landscape, web applications play a crucial role in various aspects of daily life, from online shopping to social networking. However, their widespread use also makes them attractive targets for cyber-attacks. Web Application Firewalls (WAFs) act as a frontline defense mechanism, monitoring and filtering incoming HTTP traffic to detect and block malicious requests. Traditional rule-based WAFs, while effective in many cases, may struggle to keep pace with evolving attack techniques and can produce false positives, leading to unnecessary disruptions for legitimate users. To address these challenges, this paper proposes an innovative approach to enhance WAFs using machine learning techniques. By integrating Multinomial Naive Bayes and Random Forest classifiers into the WAF architecture, we aim to improve detection accuracy and reduce false alarms. Our experimental results on synthetic HTTP request data demonstrate promising outcomes, showing the potential of machine learning in bolstering web application security.