A data‐based private learning framework for enhanced security against replay attacks in cyber‐physical systems
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Publication:6089838
DOI10.1002/rnc.5040zbMath1526.93087OpenAlexW3035834054WikidataQ115150378 ScholiaQ115150378MaRDI QIDQ6089838
Lijing Zhai, Kyriakos G. Vamvoudakis
Publication date: 13 November 2023
Published in: International Journal of Robust and Nonlinear Control (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1002/rnc.5040
Noncooperative games (91A10) Control/observation systems involving computers (process control, etc.) (93C83) Linear-quadratic optimal control problems (49N10) Networked control (93B70)
Related Items (7)
Non-fragile \(H_\infty\) filtering for discrete time-delay systems with quantization, TOD protocol and deception attacks ⋮ Distributed filtering for nonlinear systems under Dempster–Shafer theory subject to malicious cyber attacks ⋮ A safety preserving control architecture for cyber‐physical systems ⋮ Output‐feedback Q‐learning for discrete‐time linear H∞ tracking control: A Stackelberg game approach ⋮ Distributed consensus of heterogeneous switched nonlinear multiagent systems with input quantization and DoS attacks ⋮ Adaptive non-singular finite time control of nonlinear disturbed cyber-physical systems with actuator cyber-attacks and time-varying delays ⋮ Separation of learning and control for cyber-physical systems
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- Our Data, Ourselves: Privacy Via Distributed Noise Generation
- Attack Detection and Identification in Cyber-Physical Systems
- Reinforcement Learning and Feedback Control: Using Natural Decision Methods to Design Optimal Adaptive Controllers
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