Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/166617
Title: Synthesis of the supremal covert attacker against unknown supervisors by using observations
Authors: Tai, Ruochen
Lin, Liyong
Zhu, Yuting
Su, Rong
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Source: Tai, R., Lin, L., Zhu, Y. & Su, R. (2022). Synthesis of the supremal covert attacker against unknown supervisors by using observations. IEEE Transactions On Automatic Control. https://dx.doi.org/10.1109/TAC.2022.3191393
Project: A19D6a0053 
Journal: IEEE Transactions on Automatic Control 
Abstract: In this paper, we consider the problem of synthesizing the supremal covert damage-reachable attacker, in the setup where the model of the supervisor is unknown to the adversary but the adversary has recorded a (prefix-closed) finite set of observations of the runs of the closed-loop system. The synthesized attacker needs to ensure both the damage-reachability and the covertness against all the supervisors which are consistent with the given set of observations. There is a gap between the de facto supremality, assuming the model of the supervisor is known, and the supremality that can be attained with a limited knowledge of the model of the supervisor, from the adversary's point of view. We consider the setup where the attacker can exercise sensor replacement/deletion attacks and actuator enablement/disablement attacks. The solution methodology proposed in this work is to reduce the synthesis of the supremal covert damage-reachable attacker, given the model of the plant and the finite set of observations, to the synthesis of the supremal safe supervisor for certain transformed plant, which shows the decidability of the observation-assisted covert attacker synthesis problem. The effectiveness of our approach is illustrated on a water tank example adapted from the literature.
URI: https://hdl.handle.net/10356/166617
ISSN: 0018-9286
DOI: 10.1109/TAC.2022.3191393
Schools: School of Electrical and Electronic Engineering 
Rights: © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TAC.2022.3191393.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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