Patient Infusion Pattern based Access Control Schemes for Wireless Insulin Pump System

Wireless insulin pumps have been widely deployed in hospitals and home healthcare systems. Most of them have limited security mechanisms embedded to protect them from malicious attacks. In this paper, two attacks against insulin pump systems via wireless links are investigated: a single acute overdose with a significant amount of medication and a chronic overdose with a small amount of extra medication over a long time period. They can be launched unobtrusively and may jeopardize patients' lives. It is very urgent to protect patients from these attacks. We propose a novel personalized patient infusion pattern based access control scheme (PIPAC) for wireless insulin pumps. This scheme employs supervised learning approaches to learn normal patient infusion patterns in terms of the dosage amount, rate, and time of infusion, which are automatically recorded in insulin pump logs. The generated regression models are used to dynamically configure a safe infusion range for abnormal infusion identification. This model includes two sub models for bolus (one type of insulin) abnormal dosage detection and basal abnormal rate detection. The proposed algorithms are evaluated with real insulin pump. The evaluation results demonstrate that our scheme is able to detect the two attacks with a very high success rate.

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