A Federated Learning Architecture for Blockchain DDoS Attacks Detection

Abstract

The rapid development of blockchain technology has led to a constant increase in its financial and technological value. However, this has also led to malicious attacks. Distributed denial-of-service attacks pose a considerable threat to blockchain technology out of many attacks due to its effectiveness and distributed nature. To protect the blockchain from DDoS attacks, researchers have proposed a large number of defensive schemes. However, these schemes are not well-suited for use in practical situations. In this work, we propose a DDoS attack detection scheme based on centralized federated learning, where multiple participating nodes locally train models and upload them to a central node for aggregation. Additionally, we propose a more suitable method for blockchain scenarios, using decentralized federated learning technology, where multiple nodes exchange models in a peer-to-peer manner to complete model training without a central server. We simulate DDoS attacks in blockchain and generate a large dataset by combining it with traditional network layer DDoS attack data to evaluate the effectiveness of our schemes. The experimental results show that the proposed schemes perform well in classification accuracy, demonstrating that our techniques can detect DDoS attacks effectively.