FedDiSC: A Computation-efficient Federated Learning Framework for Power Systems Disturbance and Cyber Attack Discrimination. (arXiv:2304.03640v1 [cs.CR])

With the growing concern about the security and privacy of smart grid
systems, cyberattacks on critical power grid components, such as state
estimation, have proven to be one of the top-priority cyber-related issues and
have received significant attention in recent years. However, cyberattack
detection in smart grids now faces new challenges, including privacy
preservation and decentralized power zones with strategic data owners. To
address these technical bottlenecks, this paper proposes a novel Federated
Learning-based privacy-preserving and communication-efficient attack detection
framework, …

address attack attention challenges communication computation critical cyber cyberattack cyberattacks data decentralized detection discrimination federated learning framework grid novel power power grid power systems preservation privacy security smart state strategic systems technical