Securing Internet of Medical Things (IoMT) Devices with Deep Learning Techniques

The extensive utilization of biosensors exemplifies how the Internet of Things has propelled individuals toward the use of entirely digitalized e-health services. A diverse array of commercially available sensors now assists individuals in monitoring their fitness development, blood glucose levels, and various intelligent home diagnostics consistently. Issues such as protracted governmental approvals, inadequate communication between patients and physicians, and cultural opposition to the integration of sensors into daily life arise from an improper classification of the devices. Currently, IoMT is utilized across numerous clinical environments and applications. In particular important applications, machine intelligence has transitioned from an academic curiosity to practical technology due to the swift advancement of Deep Learning (DL) in recent years. Consequently, deep learning approaches are crucial to the integrity of the Internet of Medical Things, transitioning from merely enabling secure device interactions to establishing security as a fundamental principle for information systems. The objective is to deliver a comprehensive study and advanced deep learning techniques for improving the security of IoMT systems. Imminent threats and potential hazards inside the IoT framework are delineated at each instance, whereas security vulnerabilities associated with inherent or emerging risks are presented in the IoMT system. These opportunities and difficulties may serve as the foundation for future research direction. The IMT-DL approach yields findings of data security preservation at 84.25%, healthcare system assessment at 92.30%, memory space enhancement at 93.25%, patient energy constraint resolution at 92.70%, and personal surveillance system improvement at 84.50%.