School of Artificial Intelligence, Shenyang University of Technology, 110870, China.
College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China; State Key Laboratory of Synthetical Automation of Process Industries, Northeastern University, Shenyang, 110819, China; Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
ISA Trans. 2023 Jun;137:1-12. doi: 10.1016/j.isatra.2023.01.020. Epub 2023 Jan 18.
This paper studies the issue of developing the optimal deception attacks on the multiple channels in cyber-physical systems, where the attackers are limited by energy constraints. To fully utilize the eavesdropped data, by linearly combining the innovations from the different channels, a fusion attack model is proposed under the stealthiness condition. According to the statistical characteristics of the correlated stochastic variables and the orthogonality principle, the state estimation error is quantified and analyzed by deriving the iteration of the error covariance matrices of the remote estimators under the proposed attack framework. Moreover, by analyzing the correlations of the decision variables in the objective function, it is shown that the attack parameters and energy allocation strategy can be derived by two steps without loss of optimality, such that the optimal attack scheme is acquired by solving a multivariate semi-definite programming (SDP) problem and a linear 0-1 programming problem respectively. Finally, simulation examples are provided to illustrate the effectiveness of the proposed method.
本文研究了在能量受限的情况下，针对多通道网络物理系统中的最优欺骗攻击问题。为了充分利用截获的数据，通过对来自不同通道的新息进行线性组合，在隐蔽性条件下提出了一种融合攻击模型。根据相关随机变量的统计特性和正交原理，通过推导远程估计器在提出的攻击框架下的误差协方差矩阵的迭代，对状态估计误差进行了量化和分析。此外，通过分析目标函数中决策变量的相关性，表明可以通过两步不损失最优性的方法导出攻击参数和能量分配策略，从而通过分别求解多元半定规划（SDP）问题和线性 0-1 规划问题获得最优攻击方案。最后，通过仿真示例验证了所提方法的有效性。
