@Article{cmc.2025.065946,
AUTHOR = {Fangju Zhou, Hanbo Zhang, Na Ye, Jing Huang, Zhu Ren},
TITLE = {Optimized Attack and Detection on Multi-Sensor Cyber-Physical System},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {84},
YEAR = {2025},
NUMBER = {3},
PAGES = {4539--4561},
URL = {http://www.techscience.com/cmc/v84n3/63175},
ISSN = {1546-2226},
ABSTRACT = {This paper explores security risks in state estimation based on multi-sensor systems that implement a Kalman filter and a  detector. When measurements are transmitted via wireless networks to a remote estimator, the innovation sequence becomes susceptible to interception and manipulation by adversaries. We consider a class of linear deception attacks, wherein the attacker alters the innovation to degrade estimation accuracy while maintaining stealth against the detector. Given the inherent volatility of the detection function based on the  detector, we propose broadening the traditional feasibility constraint to accommodate a certain degree of deviation from the distribution of the innovation. This broadening enables the design of stealthy attacks that exploit the tolerance inherent in the detection mechanism. The state estimation error is quantified and analyzed by deriving the iteration of the error covariance matrix of the remote estimator under these conditions. The selected degree of deviation is combined with the error covariance to establish the objective function and the attack scheme is acquired by solving an optimization problem. Furthermore, we propose a novel detection algorithm that employs a majority-voting mechanism to determine whether the system is under attack, with decision parameters dynamically adjusted in response to system behavior. This approach enhances sensitivity to stealthy and persistent attacks without increasing the false alarm rate. Simulation results show that the designed leads to about a 41% rise in the trace of error covariance for stable systems and 29% for unstable systems, significantly impairing estimation performance. Concurrently, the proposed detection algorithm enhances the attack detection rate by 33% compared to conventional methods.},
DOI = {10.32604/cmc.2025.065946}
}