A secure insulin infusion system using verification monitors

Abstract

Wearable and implantable medical devices are being increasingly deployed for diagnosis, monitoring, and to provide therapy for critical medical conditions. Such medical devices are examples of safety-critical, cyber-physical systems. In this paper we focus on insulin infusion systems (IISs), which are used by diabetics to maintain safe blood glucose levels. These systems support wireless features introducing potential vulnerabilities. Although these devices go through rigorous safety certification processes, these are not able to mitigate security threats. Based on published literature, attackers can remotely command to inject an incorrect amount of insulin thereby posing threat to a patient's life. While prior work based on formal methods have been proposed to detect potential attack vectors using different forms of static analysis, these have limitations in preventing attacks at run-time. Also, as these devices are safety critical, it is not possible to apply security patches, when new types of attacks are detected, due to the need for recertification. This paper addresses these limitations by developing a formal framework for the detection of cyber-physical attacks on an IIS. First, we propose a wearable device that senses the familiar ECG to detect attacks. Thus, this device is separate from the insulin infusion system, ensuring no need for recertification of IISs. To facilitate the design of this device, we establish a correlation of ECG intervals and blood glucose levels using statistical analysis. This helps us in proposing a framework for security policy mining using the developed statistical analysis. This paves the way for the design of formal verification monitors for IISs for the first time. We perform performance evaluation of the verification monitor, which proves the technical feasibility for the design of wearable devices for attack detection of IISs. Our approach is amenable to the application of security patches, when new attack vectors are detected, making the approach ideal for run-time monitoring of medical CPSs. � 2021 ACM.