Optimal Innovation-Based Stealthy Attacks in Networked LQG Systems With Attack Cost
研究了网络化线性二次高斯系统中，攻击者如何在隐蔽性约束下通过优化攻击矩阵来最大化控制成本并最小化攻击成本，提出了严格隐蔽和ε-隐蔽的最优攻击策略。
In this article, the optimal innovation-based attack strategy is investigated for the networked linear quadratic Gaussian (LQG) systems. To bypass the detector, the attacks are required to follow strict stealthiness or ϵ -stealthiness described by the Kullback-Leibler divergence. The attackers aim to increase the quadratic control cost and decrease the attack cost, which is formulated as a nonconvex optimization problem. Then, based on the cyclic property of the matrix trace, the nonconvex objective function is transformed into a linear function related to attack matrices and covariance matrices of the tampered innovations. The optimal strictly stealthy attack is obtained by utilizing the matrix decomposition technique. Furthermore, the optimal ϵ -stealthy attack is derived to achieve a higher-attack effect by an integrated convex optimization, which distinguishes from the existing suboptimal attacks developed by a two-stage optimization. Simulation results are provided to show the effectiveness of the designed attacks.