A Decentralized Framework for the Detection and Prevention of Distributed Denial of Service Attacks Using Federated Learning and Blockchain Technology
Mao-Hsiu Hsu, Chia-Chun Liu · 2025
With the rapid development of the internet of things (IoT) and smart cities, the risk of network attacks, particularly distributed denial of service (DDoS) attacks, has significantly increased. Traditional centralized security systems struggle to address large-scale attacks while simultaneously safeguarding privacy. In this study, we created a decentralized security framework that integrates federated learning (FL) with blockchain technology for DDoS attack detection and prevention. Federated learning enables devices to collaboratively learn without sharing raw data and ensures data privacy, while blockchain provides immutable event logging and distributed monitoring to enhance the overall security of the system. The created framework leverages multi-layer encryption and Hashgraph technology for event recording, ensuring data integrity and efficiency. Additionally, software-defined networking (SDN) was employed for dynamic resource management and rapid responses to attacks. This system improves the accuracy of DDoS detection and effectively reduces communication costs and resource consumption. It has significant potential for large-scale attack defense in IoT and smart city environments.