Stealthy Insider Attack on Stochastic Event-Triggered Scheduler: Dealing With Non-Gaussian Components

This article considers malicious attacks on a stochastic event-based state estimation where a smart sensor equipped with the standard Kalman filter is utilized to transmit the local estimate. A novel attack strategy called stealthy insider attack is proposed, which can compromise remote state estimation by hacking the scheduler, reversing the triggering condition, and tampering with the schedule parameter. The discovery of the complete Gaussian crater (CGC) distribution is significant for analyzing various properties of the innovation under the stochastic event-triggered scheme (ETS). An extended CGC distribution is developed to explore the probability distribution of innovation sequences with successive packet losses, and a closed-form expression is derived for the estimation error covariance under attack. Furthermore, to bypass the communication rate detector, a method is presented for tampering with the schedule parameter based on the ergodicity of the underlying Markov chain. Finally, two numerical simulations demonstrate the efficacy of the proposed attack strategy in diminishing the estimation performance of the remote estimator.