Blockchain-Assisted Cross-silo Graph Federated Learning for Network Intrusion Detection
Hang Shen, Yanjing Zhou, Tianjing Wang, Yu Zhang, Juan Huang, Guangwei Bai, Xiaodong Miao · 2025
In this paper, a blockchain-assisted cross-silo graph federated learning (B-CGFL) framework is presented for large-scale network intrusion detection, aiming to break down barriers among different organizations and achieve a secure and transparent multi-party collaboration ecosystem. The network scenario is divided into multiple regions. Organizations in each region leverage graph neural networks to analyze local network flow topology information and identify traffic types accurately. With cross-silo graph federated learning, coordinators and organizations collaboratively complete the training and updating of the global model. Multiple coordinators jointly maintain a chain for model storage security. Oracles bridge the off-chain data provider and on-chain smart contracts, enabling Secure and trusted data access in off-chain model testing. For fair competition, a reputation-aware incentive mechanism is designed to boost global model quality. Security analysis confirms that B-CGFL can defend against model plagiarism and tampering with model test results. Experiments on three challenging datasets ToN-IoT, CSE-CIC-IDS2018, and BoT-IoT demonstrate that compared with benchmark methods, B-CGFL exhibits superior performance.