This article investigates stealthy attack strategies that leverage auxiliary innovations in distributed state estimation. We propose a malicious attacker who exploits both local innovations and auxiliary innovations from neighboring nodes to construct the attack. Under such attacks, the dynamics of the compromised estimation error covariance are derived, and the attacker aims to maximize the trace while maintaining stealthiness. By leveraging singular value decomposition, we derive an analytical solution to the optimal stealthy attack, together with a degraded counterpart that does not exploit auxiliary innovations. Furthermore, to alleviate computational complexity for the attacker, a lower bound of the compromised estimation error covariance trace is established, based on which a numerical solution for a suboptimal stealthy attack is developed. Finally, the theoretical results are verified through simulations on a buoyant air turbine (BAT) model.

This paper addresses the problem of designing stealthy attacks on distributed estimation using historical data. The distributed sensors transmit innovations to remote state estimators and neighboring nodes, which attackers can intercept and tamper with. To bypass the configured false data detectors, the attack parameters must satisfy the stealthiness constraints. The determination of the optimal stealthy attack strategy is reformulated as a series of convex optimization problems. Additionally, a lower bound on the compromised estimation error covariance is derived, and analytical solutions for the suboptimal stealthy attack strategy that maximizes the bound are provided. These solutions are proven to be piecewise constant with smaller computational complexity. Finally, numerical simulations validate the theoretical results.