Web Application Firewall for Detecting and Mitigation of Based DDoS Attacks Using Machine Learning and Blockchain

Abstract

Applications have rapidly transformed the data access, business operations, and communication methods. This sudden shift has resulted in significant security challenges such as Distributed Denial of Service (DDoS) attacks, which intensify Internet security issues. This paper introduces a novel approach to enhancing the Web Application Firewall (WAF) for detecting and mitigating botnet-based DDoS attacks through the use of Machine Learning (ML) and blockchain technologies. Legacy security systems often struggle to adapt to evolving digital threats, particularly with the rise of complex botnet designs. The integration of ML and blockchain within the WAF ecosystem represents a substantial advancement in cyber defense mechanisms. Insights are provided into the development of advanced ML algorithms for precise anomaly detection and the formulation of efficient blockchain protocols for streamlined threat intelligence sharing. The proposed approach addresses current challenges associated with botnet-driven DDoS attacks establishes a foundation for adaptive, future-proof cybersecurity strategies.