Abstract
This paper investigates the problem of deception attacks against remote estimation in cyber-physical systems. The Kullback Leibler divergence is employed to characterize the attack stealthiness. Different from the innovation based attacks in the related works which require an extra filter to calculate the true innovations and manipulated measurements online with all historical data, a novel attack strategy is proposed to deteriorate the estimation performance, which injects the off-line generated signals into the sensor measurements, such that the attack is more easily to be implemented. In the presence of the attacks, the remote estimation error is analyzed and the optimal attack strategy is derived by solving a quadratic optimization problem with inequality constraints, which maximizes the degradation of the estimation performance and keeps stealthy to the detector simultaneously. Finally, simulation examples are given to demonstrate the results.
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