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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.

Type 1 Diabetes (T1D) has challenged humanity for over 3,500 years, from its earliest descriptions in ancient medical texts to today’s cutting-edge biotechnological solutions. Even today, T1D remains a growing global health concern and is the second most common chronic disease among children in Sweden. Although there is no cure, significant progress has been made in treatment strategies, particularly through the development of artificial pancreas (AP) systems. An AP is a closed-loop insulin delivery system that integrates a glucose sensor, an insulin pump, and a control algorithm to mimic the glucose-regulating function of a healthy pancreas. By continuously adjusting insulin infusion based on real-time glucose measurements, AP systems reduce the burden of diabetes management and improve long-term health outcomes.

However, as AP systems rely on sensor data and wireless communication, they are susceptible to cyber threats. One such threat is sensor deception attacks, where an attacker manipulates the sensor readings to mislead the insulin delivery algorithm, potentially causing excessively low or high glucose levels. Detecting such attacks is particularly challenging due to natural glucose fluctuations caused by meal intake, which can mask adversarial manipulation.

The need for computationally efficient and reliable anomaly detection algorithms is paramount, particularly in wearable medical devices such as the AP. To this end, model-based anomaly detection schemes offer a mathematically rigorous and lightweight alternative, enabling timely and accurate detection of anomalies, including cyberattacks, while meeting the real-time constraints of safety-critical systems. This thesis aims to advance model-based detection methods by integrating residual generation and evaluation techniques, optimizing the trade-off between detection speed and false alarm minimization. By contributing to the development of secure AP systems, this research aims to enhance patient safety and improve the quality of life for individuals with T1D.

This paper considers constant bias injection attacks on the glucose sensor deployed in an artificial pancreas system. The main challenge with such apparently simple attacks is that they are detectable for only a limited duration if the system is linear and has an integrator. More formally put, such attacks are steady-state stealthy. To address this issue, we propose a method to design a bias-sensitive Kalman filter based on the Kullback-Leibler divergence metric. The resulting filter outperforms the nominal Kalman filter for attack detection as illustrated by numerical simulations on a realistic model.

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.

The artificial pancreas is an emerging concept of closed-loop insulin delivery that aims to tightly regulate the blood glucose levels in patients with type 1 diabetes. This paper considers bias injection attacks on the glucose sensor deployed in an artificial pancreas. Modern glucose sensors transmit measurements through wireless communication that are vulnerable to cyber-attacks, which must be timely detected and mitigated. To this end, we propose a model-based anomaly detection scheme using a Kalman filter and a chi(2) test. One key challenge is to distinguish cyber-attacks from large unknown disturbances arising from meal intake. This challenge is addressed by an online meal estimator, and a novel time-varying detection threshold. More precisely, we show that the ordinary least squares is the optimal unbiased estimator of the meal size under certain modelling assumptions. Moreover, we derive a novel time-varying threshold for the chi(2) detector to avoid false alarms during meal ingestion. The results are validated by means of numerical simulations.

Series elastic actuation (SEA) has become prevalent in applications involving physical human-robot interaction, as it provides considerable advantages over traditional stiff actuators in terms of stability robustness and force control fidelity. Several impedance control architectures have been proposed for SEA. Among these alternatives, the cascaded controller with an innermost velocity loop, an intermediate torque loop, and an outermost impedance loop is particularly favored for its simplicity, robustness, and performance. In this article, we derive the necessary and sufficient conditions for passively rendering null impedance and virtual springs with this cascade-controller architecture. Based on the newly established conditions, we provide nonconservative passivity design guidelines to haptically display these two impedance models, which serve as the basic building blocks of various virtual environments, while ensuring the safety of interaction. We also demonstrate the importance of including physical damping in the actuator model for deriving the passivity conditions, when integrators are utilized. In particular, we prove the unintuitive adversary effect of physical damping on the passivity of the system by noting that the damping term reduces the system Z-width, as well as introducing an extra passivity constraint. Finally, we experimentally validate our theoretical results using an SEA brake pedal.