Towards enhancing security for upcoming 6G-ready smart grids through federated learning and cloud solutions

Abstract The forthcoming 6G technology offers significant potential for the advancement of the smart grid domain. 6G promises ultra-low latency, higher data transfer rates, native Artificial Intelligence (AI) support, enhanced connectivity, and improved security for smart grids. Smart grids are vulnerable to cyberattacks, such as Distributed Denial-of-Service (DDoS) attacks, posing a significant threat to grid functionality. To address security concerns, smart grids implement intrusion detection systems (IDS), but detecting novel attacks such as subtle multi-domain DDoS attacks through traditional IDS is challenging. To enhance grid security, Deep Learning (DL) techniques can be utilized to identify deviations from normal network traffic and detect cyberattacks. However, training DL models with sensitive user data may violate data privacy regulations, necessitating novel approaches. Federated Learning (FL) offers a privacy-focused solution enabling smart meters to train DL models with locally generated data and make predictions at the edge. In this work, we implement a novel approach, integrating AWS cloud and FL for privacy-preserving DDoS attack detection in 6G-ready smart grids. Our approach aims to leverage the scalability of AWS cloud and advanced communication capabilities of 6G for efficient, secure, and cost-effective cyberattack detection. By implementing our approach in a local environment and the AWS cloud, we investigate the stability and robustness of the approach for large-scale cloud deployments. In addition, using statistical tests, we confirm that the performance between local and cloud implementations is consistent, and that the proposed approach is suitable for deployment in the upcoming 6G-enabled smart grids, where consistent performance is critical.