A smart agent-based approach for privacy preservation and threat mitigation to enhance security in the Internet of Medical Things

Abstract

Integrating medical sensors and the Internet of Things (IoT) within smart healthcare has facilitated the development of an advanced framework known as the Internet of Medical Things (IoMT). This framework enables the detection and assessment of the severity of participants’ conditions. Nevertheless, local IoMT devices’ constrained storage capacity and computational capabilities necessitate transferring participants’ health data to different devices for investigation. However, this transfer poses a significant risk of privacy breaches due to the absence of complete power over the participant’s health information and the system’s susceptibility to various attacks. This research presents a Smart Agent-based Privacy Preservation and Threat Mitigation Framework (SAPPTMF) for augmenting security in IoMT using an intelligent agent system. The framework involves the development of a complete system model that spans a range of components and interactions within the IoMT ecosystem. An attacker model is developed to simulate various threat situations. A thorough assessment framework is used to assess the efficacy of security measures, encompassing both the evaluation of security measures and the decision-making process. The analytic hierarchy process (AHP) provides suitable weights to various security needs or criteria. The findings provide the following performance metrics: accuracy (94.5%), precision (91.0%), recall (93.4%), F-score (92.4%), and mean squared error (MSE) of 0.09.