PROJECT TITLE :
Two-Channel False Data Injection Attacks Against Output Tracking Control of Networked Systems
This paper addresses the planning downside of false knowledge injection (FDI) attacks against the output tracking control of networked systems, where the network-induced delays within the feedback and forward channels are thought of. The most contributions of this paper are as follows. one) To actively compensate for the 2-channel network-induced delays, a Kalman filter-based mostly networked predictive management theme is meant for stochastic linear discrete-time systems. 2) From an attacker's perspective, stealthy FDI attacks are proposed for both the feedback and forward channels thus on disrupt the steadiness of the resulting closed-loop system while avoiding the detection of a Kalman filter-based attack detector. 3) Each numerical simulations and practical experiments are administrated to point out the effectiveness of the proposed method.
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