TY - JOUR
T1 - Event-Based Optimal Stealthy False Data-Injection Attacks Against Remote State Estimation Systems
AU - Guo, Haibin
AU - Sun, Jian
AU - Pang, Zhong Hua
AU - Liu, Guo Ping
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023/10/1
Y1 - 2023/10/1
N2 - Security is a crucial issue for cyber–physical systems, and has become a hot topic up to date. From the perspective of malicious attackers, this article aims to devise an efficient scheme on false data-injection (FDI) attacks such that the performance on remote state estimation is degraded as much as possible. First, an event-based stealthy FDI attack mechanism is introduced to selectively inject false data while evading a residual-based anomaly detector. Compared with some existing methods, the main advantage of this mechanism is that it decides when to launch the FDI attacks dynamically according to real-time residuals. Second, the state estimation error covariance of the compromised system is used to evaluate the performance degradation under FDI attacks, and the larger the state estimation error covariance, the more the performance degradation. Moreover, under attack stealthiness constraints, an optimal strategy is presented to maximize the trace of the state estimation error covariance. Finally, simulation experiments are carried out to illustrate the superiority of the proposed method compared with some existing ones.
AB - Security is a crucial issue for cyber–physical systems, and has become a hot topic up to date. From the perspective of malicious attackers, this article aims to devise an efficient scheme on false data-injection (FDI) attacks such that the performance on remote state estimation is degraded as much as possible. First, an event-based stealthy FDI attack mechanism is introduced to selectively inject false data while evading a residual-based anomaly detector. Compared with some existing methods, the main advantage of this mechanism is that it decides when to launch the FDI attacks dynamically according to real-time residuals. Second, the state estimation error covariance of the compromised system is used to evaluate the performance degradation under FDI attacks, and the larger the state estimation error covariance, the more the performance degradation. Moreover, under attack stealthiness constraints, an optimal strategy is presented to maximize the trace of the state estimation error covariance. Finally, simulation experiments are carried out to illustrate the superiority of the proposed method compared with some existing ones.
KW - Cyber–physical systems (CPSs)
KW - event-based attack scheduling
KW - false data-injection (FDI) attacks
KW - remote state estimation
KW - stealthiness
UR - http://www.scopus.com/inward/record.url?scp=85151427217&partnerID=8YFLogxK
U2 - 10.1109/TCYB.2023.3255583
DO - 10.1109/TCYB.2023.3255583
M3 - Article
C2 - 37030790
AN - SCOPUS:85151427217
SN - 2168-2267
VL - 53
SP - 6714
EP - 6724
JO - IEEE Transactions on Cybernetics
JF - IEEE Transactions on Cybernetics
IS - 10
ER -