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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.

This paper considers deterministic sensor deception attacks in closed-loop insulin delivery. Since the quality of decision-making in control systems heavily relies on accurate sensor measurements, timely detection of attacks is imperative. To this end, we consider a model-based anomaly detection scheme based on Kalman filtering and sequential change detection. In particular, we derive the minimax robust CUSUM and Shewhart tests that minimizes the worst-case mean detection delay and maximizes the instant detection rate, respectively. As a byproduct of our analysis, we show that the notorious.2 test shares an interesting optimality property with the twosided Shewhart test. Finally, we show that one-sided sequential detectors can significantly improve sensor anomaly detection for preventing overnight hypoglycemia which can be fatal.

This paper considers observer-based detection of sensor bias injection attacks (BIAs) on linear cyber-physical systems with single output driven by Gaussian noise. Despite their simplicity, BIAs pose a severe risk to systems with integrators, which we refer to as integrator vulnerability. Specifically, the residual generated by any linear observer is indistinguishable under attack and normal operation at steady-state, making BIAs detectable only during transients. To address this, we propose a principled method based on Kullback-Liebler divergence to design a residual generator that significantly increases the signal-to-noise ratio against BIAs. For systems without integrator vulnerability, our method also enables a trade-off between transient and steady-state detectability. The effectiveness of the proposed method is demonstrated through numerical comparisons with three state-of-the-art residual generators.