by Dena Abu Laila, Ibrahim Moh’d Obeidat, Mohammed Aman, Bandar Z. Altubaishe,
Mahmoud Odeh, Ghassan Samara
ABSTRACT
Web Application Firewalls (WAFs) perform as a dangerous security component protecting web applications from various attack vectors. However, their effectiveness against modern evasion techniques remains a significant concern in cybersecurity. This paper presents an intelligent WAF fuzzing framework that incorporate blockchain technology for secure logging, traceability, and comprehensive analysis of payload evasion attempts. Our proposed system employs an adaptive payload gen- eration engine that utilizes machine learning algorithms to create modern attack vectors that target SQL injection, cross-site scripting (XSS), and command injection vulnerabilities. The framework conducts systematic fuzzing attacks against many WAF-protected sites while logging each attempt in an unchangeable blockchain record, ensuring tamper-proof audit trails. Through comprehensive experimental evaluation in 12 commercial and open-source WAF solutions using more than 50,000 generated payloads, our framework demonstrates superior capability to identify previously unknown bypass techniques. The integrated analytics dashboard provides a comparative real-time analysis of WAF effectiveness, allowing security researchers to determine protection gaps and understand emerging attack patterns. The results indicate that our intelligent approach achieves a 34% enhancement in evasion detection compared to conventional fuzzing methods while maintaining complete traceability through blockchain integration. The framework successfully identified 347 novel bypass techniques and achieved consensus validation with 99.8% accuracy across distributed invalidator nodes
