Intelligent authentication framework for internet of medical things (IoMT)

The rapid growth of smart wearables and body sensor networks is expected to increase over the years. The reducing cost of manufacturing, deployment and the small and unobtrusive nature of most of the wearables available have intensified the acceptability for deployment in areas such as medical devices for healthcare monitoring. This work explored the use of artificial intelligence to enhance authentication of Internet of Medical Things (IoMT) through a design of a framework. The framework is designed using wearable and or with a mobile device for extracting bioelectrical signals and context awareness data. The framework uses bioelectrical signals for authentication while artificial intelligence is applied using the contextual data to enhance the patient data integrity. The framework applied different security levels to balance between usability and security on the bases of False Acceptance Rate (FAR) and False Rejection Rate (FRR). 30 people are used for the evaluation of the different security levels and the security level 1 achieved a result based on usability vs security obtaining FAR of 5.6% and FRR of 9% but when the FAR is at 0% the FRR stood at 29%. The Intelligent Authentication Framework for Internet of Medical Things (IoMT) will be of advantage in increasing the trust of data extracted for the purpose of user authentication by reducing the FRR percentage.