.png){kind=icon}
Return
A Zonotopic Estimation Framework for Malicious Attack Detection
.png){kind=icon}
PRE
AI
DOI：10.1002/asjc.70196.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This technical note studies the issue of attack detection for uncertain linear discrete-time systems. A novel attack detection approach is proposed based on zonotopic state estimation, which is unnecessary to know the exact attack type in advance. Moreover, this detection method can not only detect the existence of attacks but also locate the corresponding channels. Then, the attack sensitivity is analyzed for the following three types of attacks: denial-of-service (DoS) attacks, replay attacks (RAs) and false-data-injection (FDI) attacks. Furthermore, the mean square error (MSE) under attacks is discussed, and the measurement calibration and the detection probability are analyzed as well. In addition, the performance of the radius of the intersection zonotope is guaranteed, and the corresponding parameter can be obtained by solving the optimization problem. Finally, the effectiveness of the zonotopic estimation countermeasures in attack detection is demonstrated through theoretical analysis and simulation illustration.
Keywords:
attack detection
interval uncertainty
state estimation
zonotopic set
Journal
IF:
2.7
Papers:
598
Citations:
4.7K
Organization
Cited Papers
.png){kind=icon}
Adversarial Artificial Intelligence in Blind False Data Injection in Smart Grid AC State Estimation
PREAI
ShareSaveShareSaveShareSaveShareSave
View more