Enhanced DDoS Attack Mitigation using Machine Learning and Blockchain-Powered Blacklist Management

Distributed Denial-of-Service (DDoS) attacks are a critical cybersecurity threat, capable of overwhelming online services and causing significant disruptions. The increasing frequency and sophistication of these attacks highlight the need for more advanced and reliable mitigation strategies. This paper explores a novel approach to DDoS detection and prevention by integrating machine learning and blockchain technology. Our approach involves using the AutoKeras library to develop a machine learning classifier that identifies malicious packets within network traffic. Additionally, a blockchain-based application, implemented in PHP, securely manages and stores blacklisted IP addresses. This combined method enhances the accuracy and integrity of DDoS defences, offering a robust and scalable solution to this growing problem. By combining the predictive power of machine learning with the decentralized and tamper-proof nature of blockchain, this solution offers a robust defence against one of the most challenging threats in the field of cybersecurity.