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Learning-based Attacks in Cyber-Physical Systems

Khojasteh, Mohammad Javad and Khina, Anatoly and Franceschetti, Massimo and Javidi, Tara (2020) Learning-based Attacks in Cyber-Physical Systems. IEEE Transactions on Control of Network Systems . ISSN 2325-5870. (In Press) https://resolver.caltech.edu/CaltechAUTHORS:20201002-151458760

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Abstract

We introduce the problem of learning-based attacks in an abstraction of cyber-physical systems that may be subject to an attack that overrides the sensor readings and the controller actions. The attacker attempts to learn the dynamics of the plant and subsequently override the controller's actuation signal, to destroy the plant without being detected. The attacker can feed fictitious sensor readings to the controller using its estimate of the plant dynamics and mimic the legitimate plant operation. The controller, on the other hand, is constantly on the lookout for an attack; once the controller detects an attack, it immediately shuts the plant off. We derive lower bounds for the attacker's deception probability for linear plants by assuming a specific authentication test that inspects the empirical variance of the system disturbance. We also show how the controller can improve the security of the system by superimposing a carefully crafted privacy-enhancing signal on top of the control policy. Finally, for nonlinear scalar dynamics that belong to the Reproducing Kernel Hilbert Space, we investigate the performance of attacks based on Gaussian-processes regression.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/tcns.2020.3028035DOIArticle
ORCID:
AuthorORCID
Khojasteh, Mohammad Javad0000-0002-8459-6483
Khina, Anatoly0000-0003-2359-1678
Additional Information:© 2020 IEEE. The material in this paper was presented in part at the 8th IFAC Workshop on Distributed Estimation and Control in Networked Systems, 2019 [1]. This research was partially supported by NSF awards CNS-1446891 and ECCS-1917177. This work has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 708932.
Funders:
Funding AgencyGrant Number
NSFCNS-1446891
NSFECCS-1917177
Marie Curie Fellowship708932
Subject Keywords:Cyber-physical systems security, learning for dynamics and control, secure control, system identification, man-in-the-middle attack, physical-layer authentication
Record Number:CaltechAUTHORS:20201002-151458760
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20201002-151458760
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:105769
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:05 Oct 2020 14:27
Last Modified:05 Oct 2020 14:27

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