AUTHOR=Lin Xixiang , An Dou , Cui Feifei , Zhang Feiye 

TITLE=False data injection attack in smart grid: Attack model and reinforcement learning-based detection method

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 10 - 2022

YEAR=2023

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2022.1104989

DOI=10.3389/fenrg.2022.1104989

ISSN=2296-598X

ABSTRACT=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 attacks, it’s impractical to detect FDIAs by fixed methods. This paper proposed a FDIA
model and countering detection methods based on deep reinforcement learning (DRL). First, we
studied an attack model under the assumption of unlimited attack resources and information of
complete topology. Different types of FDIAs are also enumerated. Then, we formulated the attack
detection problem as a Markov decision process (MDP). A deep reinforcement learning-based
method is proposed to detect FDIAs. To address the sparse reward problem, experiences with
discrepant rewards are stored in different replay buffers to achieve efficiency. Moreover, the state
space is extended by considering the most recent states to improve the perception capability.
Simulations were performed on IEEE 9,14,30, and 57-bus systems, proving the validation of
attack model and efficiency of detection method. Results proved efficacy of the detection method
in different scenarios.