Automated Anomaly Detection and Mitigation in IEC 61850-based Substation Control Systems using Federated Reinforcement Learning

**Abstract:** This paper introduces a novel approach to enhancing the security of SCADA systems, specifically focusing on anomaly detection and mitigation within IEC 61850-based substation control environments. Leveraging a federated reinforcement learning (FRL) framework, the system dynamically adapts to evolving cyber threats by collaboratively learning from decentralized data sources without compromising data privacy. Our methodology enhances existing intrusion detection systems by offering proactive mitigation strategies, demonstrating a significant improvement in resilience and response time compared to traditional reactive approaches. We provide detailed analysis of the model architecture, the FRL training process, and demonstrate performance improvements through simulation results using a realistic substation model. --- *This document is a part of the Freederia Research Archive. Explore our complete collection of advanced research at [en.freederia.com](https://en.freederia.com), or visit our main portal at [freederia.com](https://freederia.com) to learn more about our mission and other initiatives.*