Federated Learning-Based Intrusion Detection Method for Smart Grid

Power systems have revealed serious security problems in the process of gradual opening, and intrusion detection as an important security defense measure can detect potential intrusions in a timely manner. In the big data environment of electric power, there are information silos between different electric power data owners, and in order to obtain intrusion detection models with better performance, traditional methods need to fuse data from all parties, which often brings difficulties in information security and data privacy protection. In this paper, we propose a distributed intrusion detection framework based on federated learning and apply it to network traffic data analysis. The framework aims to ensure the information security of each local power data while establishing a collection of decentralized data and completing the joint training of models from multiple data sources. The experimental results show that the scheme achieves 98.1% accuracy on the simulated data set, which is better than other commonly used intrusion detection algorithms. In addition, the method well ensures the security and privacy of data because the data are not interoperable among each participant under the federated learning mechanism.