Limited device resources and an ever-changing cybersecurity landscape compound the challenges faced by the network protection infrastructure for Internet of medical things (IoMT) applications, which include different device ecosystems, privacy concerns, and problems with interoperability. Protecting private medical information in IoMT apps is challenging; a comprehensive strategy that provides user education, standard protocols, and robust security mechanisms is necessary to overcome these obstacles. With the advancement of IoMT, the network of clinical systems, gadgets, and sensors is integrated with the Internet of things (IoT) to enable intelligent healthcare solutions. However, the sensitive data sharing and the substantial connections in the IoMT systems raise security and privacy concerns in the network. Therefore, network security is critical in IoMT applications due to data breaches, vulnerabilities, and distributed denial of service attacks on medical data. This study reviews the network security techniques implemented in the existing studies for IoMT applications using machine learning and blockchain technology. This study presents an overview of IoMT healthcare applications by highlighting the security challenges encountered and the necessity of adopting advanced techniques to deal with complex threats. The research is mainly about how deep reinforcement learning (DRL), commonly used for intrusion detection, access control, and anomaly detection, works over time and how it can be used in IoMT applications. With the notion of providing robust security in IoMT applications, this study appraises the benefits of blockchain technology, such as data integrity, accountability, and confidentiality. Besides, this study addresses the limitations and challenges of various security techniques that IoMT systems employ. This work assesses the findings, research gaps, and future advancements for enhancing network security in IoMT applications. With an extensive analysis of existing research, this survey guides researchers, medical practitioners, and decision-makers to scale up the DRL and blockchain in IoMT systems more efficiently in the future.
