A Hierarchical Federated Learning-Based Intrusion Detection System for 5G Smart Grids

Abstract

As the core component of smart grids, advanced metering infrastructure (AMI) provides the communication and control functions to implement critical services, which makes its security crucial to power companies and customers. An intrusion detection system (IDS) can be applied to monitor abnormal information and trigger an alarm to protect AMI security. However, existing intrusion detection models exhibit a low performance and are commonly trained on cloud servers, which pose a major threat to user privacy and increase the detection delay. To solve these problems, we present a transformer-based intrusion detection model (Transformer-IDM) to improve the performance of intrusion detection. In addition, we integrate 5G technology into the AMI system and propose a hierarchical federated learning intrusion detection system (HFed-IDS) to collaboratively train Transformer-IDM to protect user privacy in the core networks. Finally, extensive experimental results using a real-world intrusion detection dataset demonstrate that the proposed approach is superior to other existing approaches in terms of detection accuracy and communication cost for an IDS.