A Distributed Intrusion Detection System based on Blockchain and Federated Learning

In the face of rapidly evolving network attacks and the large volume of network traffic data, distributed intrusion detection has garnered significant research interest. However, distributed intrusion detection requires multiple parties to share data, which may pose a challenge of sensitive data leakage. This paper presents IDS-BF, a distributed intrusion detection system based on blockchain and federated learning. To improve the effectiveness of federated learning, the proposed system adopts a contribution-based method to dynamically adjust the weights of clients when aggregating the local gradients. Specially, to ensure the fairness of contribution evaluation, the proposed system utilizes two parachains to perform model aggregation and contribution evaluation respectively. With the help of a relay chain, the two parachains can communicate with each other. Simulation results on real-world data show that the proposed aggregation strategy can help to improve the accuracy of global model, achieving an increase of up to 8% compared to traditional strategies.