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Information-based stealthy attacks against distributed state estimation over sensor networks
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DOI：10.1016/j.neucom.2024.129087.png)
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Abstract
En 中文
In this article, the problem of information-based attacks on distributed state estimation over sensor networks is investigated. We consider a scenario where each sensor node obtains the state estimation by utilizing the measurements transmitted from local and neighboring nodes. An information-based attack is proposed to degrade the estimation performance of the remote estimator, which can be designed in two steps: First, the attacker runs a Kalman filter to estimate the compromised prediction error of remote estimator, then tampers with the transmitted measurements based on the obtained estimation. The attacker aims to maximize the estimation error of the estimator while remaining stealthy to the detector, which can be formulated as an optimization problem. The analytical expression of the optimal strict stealthy attack strategy is derived using the singular value decomposition technique. Moreover, the optimal epsilon-stealthy attack strategy can be obtained by solving a convex optimization problem. When attackers have limited access to all measurements due to resource constraints, extended discussions are provided on designing the optimal attack strategy with strict stealthiness and offering a selection method to further enhance the attack performance. Simulation results are presented to demonstrate the effectiveness of the designed attacks.
Keywords:
Cyber-physical systems
Information-based attacks
Distributed estimation
Kalman filters