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- W3186352207 abstract "With the expansion in communication networks, vulnerability to cyber intrusions also increases. This work investigates the effect of different types of standard cyber-attacks on the operation of nonlinear processes under decentralized and distributed model predictive control (MPC) systems. The robustness of the decentralized control architecture over distributed control architecture is analyzed. Moreover, a machine-learning-based detector is trained using sensor data to monitor the cyber security of the overall system. Specifically, detectors built using feed-forward neural networks are used to detect the presence of an attack. A nonlinear chemical process example is simulated to demonstrate the robustness of decentralized control architectures and the effectiveness of the neural-network detection scheme in maintaining the closed-loop stability of the system." @default.
- W3186352207 created "2021-08-02" @default.
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- W3186352207 date "2021-05-25" @default.
- W3186352207 modified "2023-09-26" @default.
- W3186352207 title "Cyber-Security of Decentralized and Distributed Control Architectures with Machine-Learning Detectors for Nonlinear Processes" @default.
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- W3186352207 doi "https://doi.org/10.23919/acc50511.2021.9482664" @default.
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