An AI‐Enabled Hybrid Lightweight Authentication Model for Digital Healthcare Using Industrial Internet of Things Cyber‐Physical Systems

In the era of smart healthcare, Internet of Medical Things (IoMT)‐ based Cyber‐Physical Systems (CPS) play an important role, while accessing, monitoring, assessing, and prescribing patients ubiquitously. Efficient authentication and secure data transmission are the influential impediments of these networks that need to be addressed to maintain credence among clients, healthcare specialists, pharmacologists, and other associated entities. To address the authentication and data privacy issues in smart healthcare, in this paper we propose a lightweight hybrid deep learning protocol to achieve security and privacy. To achieve better results, we enabled the decentralized authentication of legitimate patient wearable devices to minimize computation cost, authentication time, and communication overheads with the help of an ML technique to predicate and forward the authentication attributes of patient wearable devices to the next concerned trusted authority, when it is shifted from region to another region. Simulation up-shots of the ML scheme exhibited extraordinary security features with the cost‐effective validation of legal patient wearable devices accompanied by worthwhile communication function-alities compared with previous work. However, the application of IoT‐based medical devices and managing such a broad, sophisticated medical IoT system on standard Single Cloud platforms (CP) would be extremely tough. We propose a scalable FC with a blockchain‐based architecture for a 5G‐enabled IoMT platform. To work on an FC architecture with flowing effects, low over-heads, and secure storage (SS), this research proposes a secured blockchain‐based fogBMIoMT communication mechanism.