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Quickest detection of bias injection attacks on the glucose sensor in the artificial pancreas under meal disturbances

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  • معلومة اضافية
    • Publisher Information:
      Uppsala universitet, Signaler och system Uppsala universitet, Avdelningen för systemteknik Uppsala universitet, Reglerteknik Swiss Fed Inst Technol, Automat Control Lab, Phys Str 3, CH-8092 Zurich, Switzerland. 2024
    • نبذة مختصرة :
      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.
    • الموضوع:
      Type 1 diabetes mellitus; Artificial pancreas; Quickest change detection; Control-theoretic security; Sensor deception attack; Control Engineering; Reglerteknik; Article in journal; info:eu-repo/semantics/article; text
    • الرقم المعرف:
      10.1016.j.jprocont.2024.103162
    • Availability:
      Open access content. Open access content
      info:eu-repo/semantics/openAccess
    • Note:
      application/pdf
      English
    • Other Numbers:
      UPE oai:DiVA.org:uu-525038
      0000-0003-3044-8810
      0000-0001-5491-4068
      0000-0001-9066-5468
      0000-0003-0762-5743
      doi:10.1016/j.jprocont.2024.103162
      ISI:001164643000001
      1428119900
    • Contributing Source:
      UPPSALA UNIV LIBR
      From OAIster®, provided by the OCLC Cooperative.
    • الرقم المعرف:
      edsoai.on1428119900
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