Researchers crack code to spot hacked glucose sensors in insulin pumps
Scientists have developed a method to distinguish between sensor errors and cyberattacks on wireless glucose monitors used in artificial pancreas systems. The advance matters because these devices—used by millions with diabetes—face growing security risks, and false alarms could delay treatment while missing real threats.
Originaltitel: Quickest detection of bias injection attacks on the glucose sensor in the artificial pancreas under meal disturbances
<p>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.</p>