Worst-Case Stealthy Innovation-Based Linear Attacks on Remote State Estimation Under Kullback–Leibler Divergence

DOI10.1109/tac.2021.3125430OpenAlexW3211862199MaRDI QIDQ6077172

Hao Yu, Tongwen Chen, Unnamed Author

Publication date: 25 September 2023

Published in: IEEE Transactions on Automatic Control (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1109/tac.2021.3125430

zbMATH Keywords

Kullback-Leibler divergencecyber-physical systemsremote state estimationstealthy attacks

Mathematics Subject Classification ID

Systems theory; control (93-XX)

Related Items (5)

Vulnerability analysis of distributed state estimation under joint deception attacks ⋮ Security of networked control systems subject to deception attacks: a survey ⋮ Optimal deception attacks on remote state estimators equipped with interval anomaly detectors ⋮ Innovation-based stealthy attack against distributed state estimation over sensor networks ⋮ Resilient and constrained consensus against adversarial attacks: a distributed MPC framework

This page was built for publication: Worst-Case Stealthy Innovation-Based Linear Attacks on Remote State Estimation Under Kullback–Leibler Divergence