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Quickest detection of bias injection attacks on the glucose sensor in the artificial pancreas under meal disturbances
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Electrical Engineering, Signals and Systems.ORCID iD: 0000-0003-3044-8810
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.ORCID iD: 0000-0001-5491-4068
Swiss Fed Inst Technol, Automat Control Lab, Phys Str 3, CH-8092 Zurich, Switzerland..
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Electrical Engineering, Signals and Systems.ORCID iD: 0000-0001-9066-5468
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2024 (English)In: Journal of Process Control, ISSN 0959-1524, E-ISSN 1873-2771, Vol. 135, article id 103162Article in journal (Refereed) Published
Abstract [en]

Modern glucose sensors deployed in closed -loop insulin delivery systems, so-called artificial pancreas use wireless communication channels. While this allows a flexible system design, it also introduces vulnerability to cyberattacks. Timely detection and mitigation of attacks are imperative for device safety. However, large unknown meal disturbances are a crucial challenge in determining whether the sensor has been compromised or the sensor glucose trajectories are normal. We address this issue from a control -theoretic security perspective. In particular, a time -varying Kalman filter is employed to handle the sporadic meal intakes. The filter prediction error is then statistically evaluated to detect anomalies if present. We compare two state-of-the-art online anomaly detection algorithms, namely the ᅵᅵᅵᅵᅵᅵ2 and CUSUM tests. We establish a robust optimal detection rule for unknown bias injections. Even if the optimality holds only for the restrictive case of constant bias injections, we show that the proposed model -based anomaly detection scheme is also effective for generic non -stealthy sensor deception attacks through numerical simulations.

Place, publisher, year, edition, pages
Elsevier, 2024. Vol. 135, article id 103162
Keywords [en]
Type 1 diabetes mellitus, Artificial pancreas, Quickest change detection, Control-theoretic security, Sensor deception attack
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:uu:diva-525038DOI: 10.1016/j.jprocont.2024.103162ISI: 001164643000001OAI: oai:DiVA.org:uu-525038DiVA, id: diva2:1847446
Part of project
Analysis and design of secure and resilient control systems, Swedish Research Council
Funder
Swedish Research Council, 2018-04396Swedish Foundation for Strategic ResearchAvailable from: 2024-03-27 Created: 2024-03-27 Last updated: 2024-03-27Bibliographically approved

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Tosun, Fatih EmreTeixeira, AndréAhlén, AndersDey, Subhrakanti

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