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    This study introduces a novel security system for artificial pancreas systems (APS) to protect diabetes patients from cyber threats. The proposed method effectively detects malicious behavior, even in hidden modes, ensuring patient safety.

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    Area of Science:

    • Biomedical Engineering
    • Cybersecurity
    • Medical Informatics

    Background:

    • Implantable medical devices (IMDs), like artificial pancreas systems (APS), offer significant benefits for managing chronic diseases such as diabetes mellitus.
    • Despite advancements, APS are vulnerable to security threats, posing risks to patient safety and data integrity.
    • Existing security measures may not adequately address novel or hidden malicious behaviors.

    Purpose of the Study:

    • To propose and evaluate a specification-based misbehavior detection system (SMDS) for enhancing the security of APS.
    • To introduce an outlier detection algorithm for validating data integrity in APS communications.
    • To develop a trust-based scheme for assessing the trustworthiness of APS components.

    Main Methods:

    • Extended the UVA/Padova simulator for collecting glucose-insulin data.
    • Simulated well-behave and malicious APS scenarios using MATLAB.
    • Implemented a specification-based misbehavior detection system (SMDS) with an outlier detection algorithm and a smoothened-trust-based monitor agent.
    • Compared the proposed SMDS against traditional machine learning classifiers (Decision Tree, SVM, KNN).

    Main Results:

    • The proposed SMDS demonstrated superior detection performance compared to contemporary machine learning classifiers.
    • An optimal trust threshold was identified, achieving high specificity and sensitivity rates.
    • The system effectively detected habitual or hidden malicious behaviors in APS, outperforming existing methods.

    Conclusions:

    • The developed specification-based misbehavior detection system (SMDS) offers a robust solution for mitigating security threats in APS.
    • The approach ensures the integrity and trustworthiness of data transmitted within APS, crucial for patient safety.
    • This research highlights the importance of advanced security measures for protecting patients using connected medical devices.