False data injection attack in smart grid: Attack model and reinforcement learning-based detection method
The smart grid, as a cyber-physical system, is vulnerable to attacks due to the diversified and open environment. The false data injection attack (FDIA) can threaten the grid security by constructing and injecting the falsified attack vector to bypass the system detection. Due to the diversity of at...
Main Authors: | Xixiang Lin, Dou An, Feifei Cui, Feiye Zhang |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2023-01-01
|
Series: | Frontiers in Energy Research |
Subjects: | |
Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2022.1104989/full |
Similar Items
-
Deep learning-based identification of false data injection attacks on modern smart grids
by: Debottam Mukherjee, et al.
Published: (2022-11-01) -
Detection of False Data Injection Attacks in Smart Grids Based on Expectation Maximization
by: Pengfei Hu, et al.
Published: (2023-02-01) -
False Data Injection Attacks With Incomplete Network Topology Information in Smart Grid
by: Yuancheng Li, et al.
Published: (2019-01-01) -
A Novel Sparse Attack Vector Construction Method for False Data Injection in Smart Grids
by: Meng Xia, et al.
Published: (2020-06-01) -
False data injection attack detection in dynamic power grid: A recurrent neural network-based method
by: Feiye Zhang, et al.
Published: (2022-09-01)