PROJECT TITLE :

Two-Channel False Data Injection Attacks Against Output Tracking Control of Networked Systems

ABSTRACT:

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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