Ephemeral Elliptic Curve Diffie-Hellman to Secure Data Exchange in Internet of Medical Things

Abstract

The COVID-19 epidemic has accelerated the deployment of remote healthcare monitoring with the overload of hospitals with patients requiring immediate care and oxygen therapy. To unload hospitals, several medical interventions have been postponed, and the places occupied by patients kept under monitoring have been freed up and replaced by remote monitoring. Some COVID-19 patients have been offered home oxygen therapy with remote monitoring using the Internet of Medical Things (IoMT). The medical data has stringent security requirements for exchanged data between connected objects. In this chapter, we propose a new framework to secure the collected data of healthcare monitoring using the Internet of Medical Things (IoMT). In spite of their deployment, these devices are vulnerable to several cyber-attacks, ranging from unauthorized access to private medical data to data modification and injection. These attacks can compromise the privacy of the monitored patient, reduce the reliability of the monitoring system, and may harm the life of monitored patient. In this chapter, we propose a new framework to detect attacks and secure the communications in IoMT. To prevent eavesdropping and modification attacks, we propose the Elliptic Curves Diffie-Hellman Ephemeral (ECDHE) to derive a session key used to provide confidentiality and authenticity. To detect injected measurements, flooding triggered by compromised devices and medical changes in physiological data, we applied the sequential change point detection algorithm Pruned Exact Linear Time (PELT) followed by a boxplot. Our experimental results show that our approach is able to increase the reliability and the accuracy of remote monitoring system, while reducing the false alarms triggered by injected measurements.