Detection of False Data Injection Attacks in Smart Grid: A Secure Federated Deep Learning Approach. (arXiv:2209.00778v1 [cs.CR])

As an important cyber-physical system (CPS), smart grid is highly vulnerable
to cyber attacks. Amongst various types of attacks, false data injection attack
(FDIA) proves to be one of the top-priority cyber-related issues and has
received increasing attention in recent years. However, so far little attention
has been paid to privacy preservation issues in the detection of FDIAs in smart
grid. Inspired by federated learning, a FDIA detection method based on secure
federated deep learning is proposed in this paper …

attacks data deep learning detection grid injection injection attacks smart