A Comprehensive Survey on Security and Privacy Challenges in Internet of Medical Things Applications: Deep Learning and Machine Learning Solutions, Obstacles, and Future Directions

Abstract

The Internet of Medical Things (IoMT), a specialized segment of the Internet of Things (IoT), is revolutionizing healthcare by enabling real-time data collection, transmission, and processing from connected medical devices. This system significantly enhances patient care by improving the accuracy, reliability, and efficiency of devices such as smart wearables, implantable sensors, and remote monitoring equipment. By capturing and transmitting medical data via networks to cloud storage, IoMT facilitates continuous patient oversight and innovative healthcare practices. As an advanced bio-analytical platform, IoMT integrates networked biomedical devices with software applications, addressing healthcare challenges through telemedicine, robotics, remote monitoring, and other technologies. However, its widespread adoption faces significant obstacles related to data management, privacy, security, scalability, and device upgrades. The resource constraints of IoMT devices make them particularly vulnerable to security and privacy threats, endangering both the devices and the broader healthcare ecosystem. These threats have evolved in complexity and scale, challenging detection and defense efforts. This paper reviews the security concerns surrounding IoMT, categorizes attacks, and discusses defense strategies, including privacy-preserving techniques, while presenting a taxonomy of these attacks and strategies tailored to the security needs of various IoMT applications. Detailed classifications of security protocols highlight each approach’s strengths and limitations. Finally, this work outlines key challenges and future research directions for developing sustainable security frameworks for IoMT.