AI-Based web application firewall for real-time malicious query detection
Shashi Bhushan, Harsh Mishra, Ramya Sharma, Abhiraj Krishna Babu, Harshit Verma · 2025
This research paper explores the development and implementation of an AI-driven Web Application Firewall (AI-WAF) designed to identify and mitigate malicious web queries effectively. By using deep learning, the AI-WAF has achieved high performance, such as the accuracy and balanced F1 scores over 98% and above 97%, respectively. The paper shows the advantage of artificial intelligence-based (AI-based) methods over conventional rule-based methods, highlighting their flexibility, scalability, and robustness in terms of real-time threat detection. The paper also makes a comparison between the AI-WAF and current models, in this way showing its superior performance in characterizing new attack patterns. Principal contributions are represented by novel methods for the strengthening of the security of web applications, and by suggestions for the strengthening of scalability, dataset heterogeneity, and complexity of hybrid defence systems. AI-WAF&s;s potential future uses in IoT security, as well as integration with threat intelligence systems, are highlighted.