Work

Blockchain-Assisted Cross-silo Graph Federated Learning for Network Intrusion Detection

Year: 2025

Type: conference-paper

Abstract: 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 global intrusion detection models. Multiple coordinators jointly maintain a chain to improve the global model’s scalability and storage security. Oracle nodes bridge the off-chain data provider and on-chain smart contracts, enabling secure transmission in off-chain model accuracy testing. For fair competition, a reputation-aware model incentive mechanism is designed to improve global model quality. Security analysis confirms that B-CGFL can defend against inference attacks, 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 machine learning methods, B-CGFL exhibits superior performance in accuracy and F1-score and facilitates model quality improvement.

DOI: 10.1109/gbc60041.2025.11134474