Wireless 6G Cloud Communication Based Security Analysis Using Machine Learning in Internet of Medical Things (IoMT)

In order to safeguard confidentiality of patient data, healthcare data should not be made available to unauthorized individuals. By 2030, the so-called sixth generation (6G) of communication technology is predicted to deliver essential healthcare infrastructure. AI-driven healthcare that is dependent on 6G connectivity technologies will improve healthcare services and quality of life. However, in emerging technologies like cloud computing, which are susceptible to cyber gaps that compromise the privacy and security of patients' electronic health records, wireless network security issues must be carefully understood and taken into account. Concerns about cloud computing security have become increasingly pressing in recent years. In this paper, we propose a framework that incorporates most significant cloud 6G network security procedures for the healthcare industry. This examination proposes novel method in tolerant clinical information assortment and upgrade the distributed storage with security. The 6G cloud network, which is based on Internet of Medical Things (IoMT), has gathered the medical data. Then, multi-key federated privacy preserving with ranked authentication is used to boost the security of the network. For a variety of patient datasets, experimental analysis is conducted in terms of security analysis, training accuracy, MAPE (mean absolute percentage error), end-end delay, and latency.