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Data-Driven Control and Data-Poisoning attacks in Buildings: the KTH Live-In Lab case study
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).ORCID iD: 0000-0001-9083-5260
KTH, School of Industrial Engineering and Management (ITM), Energy Technology, Applied Thermodynamics and Refrigeration.ORCID iD: 0000-0002-2300-2581
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).ORCID iD: 0000-0002-4679-4673
2021 (English)In: 2021 29th Mediterranean Conference on Control and Automation (MED), Institute of Electrical and Electronics Engineers (IEEE) , 2021, p. 53-58, article id 9480238Conference paper, Published paper (Refereed)
Abstract [en]

This work investigates the feasibility of using input-output data-driven control techniques for building control and their susceptibility to data-poisoning techniques. The analysis is performed on a digital replica of the KTH Live- in Lab, a non-linear validated model representing one of the KTH Live-in Lab building testbeds. This work is motivated by recent trends showing a surge of interest in using data- based techniques to control cyber-physical systems. We also analyze the susceptibility of these controllers to data poisoning methods, a particular type of machine learning threat geared towards finding imperceptible attacks that can undermine the performance of the system under consideration. We consider the Virtual Reference Feedback Tuning (VRFT), a popular data- driven control technique, and show its performance on the KTH Live-In Lab digital replica. We then demonstrate how poisoning attacks can be crafted and illustrate the impact of such attacks. Numerical experiments reveal the feasibility of using data-driven control methods for finding efficient control laws. However, a subtle change in the datasets can significantly deteriorate the performance of VRFT.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2021. p. 53-58, article id 9480238
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-304670DOI: 10.1109/MED51440.2021.9480238ISI: 000811823100006Scopus ID: 2-s2.0-85113657291OAI: oai:DiVA.org:kth-304670DiVA, id: diva2:1609996
Conference
29th Mediterranean Conference on Control and Automation, MED 2021, Bari, Puglia 22 June 2021 through 25 June 2021
Projects
https://strategiska.se/en/research/ongoing-research/cyber-security-2017/project/9222/
Funder
Swedish Foundation for Strategic Research, RIT17-0046
Note

QC 20211201

Part of conference proceedings: ISBN 978-166542258-1

Available from: 2021-11-09 Created: 2021-11-09 Last updated: 2022-09-23Bibliographically approved

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fulltext(2378 kB)111 downloads
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Russo, AlessioMolinari, MarcoProutiere, Alexandre

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CiteExportLink to record
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Citation style
  • apa
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Output format
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