Dual Perspective Secure Analysis for Local Estimate-Based FDI Attacks in Networked Systems

This article discusses the security concerns related to networked systems, where the sensor sends the local estimate to the remote estimator, which may be attacked. Traditionally, in the remote state estimation with the innovation or raw measurement case, denial of service (DoS) and false data injection (FDI) attacks are investigated thoroughly. Notably, for remote state estimation with local estimate cases considered in this article, most existing works consider DoS attacks but not FDI attacks, negatively affecting remote state estimation performance. Furthermore, current detection mechanisms encounter challenges when identifying such attacks due to the unavailable innovation or raw measurement. As such, we study FDI attacks under this framework and provide the corresponding secure analysis using a dual-perspective approach. Specifically, we propose a detector to detect such attacks using the prior information extracted from the remote estimator. Then, we analyze the existence of stealthy attacks and characterize the corresponding performance evaluation for the remote estimation under such attacks. Following this, we construct the optimal attack scheme, maximizing the expected average and terminal estimation error covariances, respectively. To reduce the above vulnerability, we develop a co-design transmission strategy and offer an analytical detection performance evaluation under different attack scenarios. Finally, simulations are provided to illustrate the proposed results.