This paper presents the design and implementation of an AI-powered Web Application Firewall (WAF) aimed at enhancing web security through intelligent threat detection. Traditional WAFs rely on static rule-based systems, which are often ineffective against evolving cyber threats such as zero-day attacks and sophisticated injection techniques. The proposed system integrates machine learning algorithms to dynamically analyze incoming web traffic and identify malicious patterns in real time. By leveraging anomaly detection and pattern recognition techniques, the system can detect SQL injection, cross-site scripting (XSS), and other application-layer attacks with improved accuracy. The solution is designed to continuously learn from new data, thereby improving its detection capabilities over time. Experimental results demonstrate that the AI-based WAF outperforms traditional approaches in terms of detection rate and adaptability, making it a robust solution for modern web security challenges.
BIN et al. (Wed,) studied this question.