Detection of False Data Injection Attacks in Smart Grid: A Secure Federated Deep Learning Approach
Northeast Electric Power University · Huazhong University of Science and Technology · +3 more institutions
Abstract
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 grids. Inspired by federated learning, a FDIA detection method based on secure federated deep learning is proposed in this paper by combining Transformer, federated learning and Paillier cryptosystem. The Transformer, as a detector deployed in edge nodes, delves deep into the connection between individual electrical quantities…
Citation impact
- FWCI
- 37.12
- Percentile
- 100%
- References
- 38
Authors
5- YLYang LiCorresponding
Northeast Electric Power University, Huazhong University of Science and Technology
- XWXinhao Wei
Northeast Electric Power University
- YLYuanzheng Li
Northeast Electric Power University, Huazhong University of Science and Technology
- ZYZhao Yang Dong
Nanyang Technological University
- MSMohammad Shahidehpour
Illinois Institute of Technology, King Abdulaziz University
Topics & keywords
- Paillier cryptosystem
- Computer science
- Smart grid
- Cryptosystem
- Federated learning
- Computer security
- Cyber-physical system
- Deep learning