Abstract
In recent years, various approaches have been proposed to design control systems that directly utilize data without mathematical plant models. Data-driven control involves updating or redesigning a controller using actual operating data, enabling fine-tuning control systems and achieving desired characteristics. However, the increasing prevalence of cyber-attacks targeting control systems presents significant societal challenges. A study by Russo and Proutiere (in Proceeding of American Control Conference (ACC), 2021) showed a poisoning approach targeting virtual reference feedback tuning, a data-driven control method. The study suggests that compromising the data used in the data-driven method may result in the closed-loop performance failing to achieve desired specifications and, in the worst case, destabilizing the control system. Hence, investigating the adverse effects of cyber-attacks on data employed in data-driven methods becomes crucial. This study explores the impact of a poisoning attack on the data used in the data-driven control method, specifically emphasizing virtual internal model tuning as a representative data-driven control approach.
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The data that support the findings of this study are available from corresponding author. Restrictions apply to the availability of these data, which were used under license for this study. Data are available corresponding author with the permission of Hitachi Ltd.
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04 December 2023
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References
Russo A, Proutiere A (2021) Poisoning attack against data-driven control methods. In: Proceeding of American control conference (ACC), pp 3234–3241
Kogiso K, Fujita T (2015) Cyber-security enhancement of networked control systems using homomorphic encryption. IEEE Conference on Decision and Control, pp 6838–6843
Fujita S, Hata K, Mochizuki A, Sawada K, Shin S, Hosokawa S (2021) OpenPLC-based control system testbed for PLC whitelisting system. Artif Life Robot 26:149–154
Fujita J, Ogura T, Okochi K, Matsumoto N, Sawada K, Kaneko O (2021) The structured cyber-attack scenario expression model based on diamond model and adversarial states. IEEJ Trans Electron Inf Syst 142(3):328–338
Hjalmarsson H, Gevers M, Gunnarsson S, Lequin O (1998) Iterative feedback tuning: theory and applications. IEEE Control Syst Mag 18(4):26–41
Campi MC, Lecchini A, Savaresi SM (2002) Virtual reference feedback tuning—a direct method for the design of feedback controllers—. Automatica 38(8):1337–1446
Souma S, Kaneko O, Fujii T (2004) A new approach to parameter tuning of controllers by using one-shot experimental data. Trans Inst Syst Control Inf Eng 17(12):528–536
Ikezaki T, Kaneko O (2019) A new approach to parameter tuning of controllers by using output data of closed loop system—a proposal of virtual internal model tuning—. IEEJ Trans Electron Inf Syst Jpn 39(7):780–785
Barreno M, Nelson B, Sears R, Joseph AD, Tygar JD (2006) Can machine learning be secure?. In: Proceedings of the 2006 ACM symposium on information, computer and communications security, pp 16–25
Biggio B, Nelson B, Laskov P (2012) Poisoning attacks against support vector machines. In: Proceedings of the 29th international conference on machine learning (ICML), pp 1467–1474
Teixeria A, Sou KC, Sandberg H, Johansson KH (2015) Secure control systems: a quantitative risk management approach. IEEE Control Syst Mag 35(1):24–45
The MathWorks, Inc., https://jp.mathworks.com/
Acknowledgements
This work is partially supported by JSPS Grants-in-Aid (B) 20H02169.
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This work was presented in part at the joint symposium of the 27th International Symposium on Artificial Life and Robotics, the 7th International Symposium on BioComplexity, and the 5th International Symposium on Swarm Behavior and Bio-Inspired Robotics (Online, January 25–27, 2022).
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Ikezaki, T., Kaneko, O., Sawada, K. et al. Poisoning attack on VIMT and its adverse effect. Artif Life Robotics 29, 168–176 (2024). https://doi.org/10.1007/s10015-023-00914-7
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DOI: https://doi.org/10.1007/s10015-023-00914-7