The Internet of Medical Things (IoMT) has revolutionized the healthcare industry by enabling physiological data collection using sensors, which are transmitted to remote servers for continuous analysis by physicians and healthcare professionals. This technology offers numerous benefits, including early disease detection and automatic medication for patients with chronic illnesses. However, IoMT technology also presents significant security risks, such as violating patient privacy or exposing sensitive data to interception attacks due to wireless communication, which could be fatal for the patient. Additionally, traditional security measures, such as cryptography, are challenging to implement in medical equipment due to the heterogeneous communication and their limited computation, storage, and energy capacity. These protection methods are also ineffective against new and zero-day attacks. It is essential to adopt robust security measures to ensure data integrity, confidentiality, and availability during data collection, transmission, storage, and processing. In this context, using Intrusion Detection Systems (IDS) based on Machine Learning (ML) can bring a complementary security solution adapted to the unique characteristics of IoMT systems. Therefore, this paper investigates how IDS based on ML can address security and privacy issues in IoMT systems. First, the generic three-layer architecture of IoMT is provided, and the security requirements of IoMT systems are outlined. Then, the various threats that can affect IoMT security are identified, and the advantages, disadvantages, methods, and datasets used in each solution based on ML at the three layers that make up IoMT are presented. Finally, the paper discusses the challenges and limitations of applying IDS based on ML at each layer of IoMT, which can serve as a future research direction.

翻译：医疗物联网（IoMT）通过传感器实现生理数据采集，并将这些数据传输至远程服务器，供医生和医疗专业人员进行持续分析，从而彻底变革了医疗行业。该技术带来诸多益处，包括对慢性病患者的早期疾病检测和自动用药。然而，IoMT技术也带来了显著的安全风险，例如由于无线通信而侵犯患者隐私或使敏感数据遭受拦截攻击，这可能导致患者致命后果。此外，由于异构通信以及医疗设备有限的计算、存储和能源能力，传统安全措施（如密码学）难以在医疗设备上实施。这些保护方法也无法有效应对新型和零日攻击。为确保数据在采集、传输、存储和处理过程中的完整性、机密性和可用性，必须采用强有力的安全措施。在此背景下，采用基于机器学习（ML）的入侵检测系统（IDS）可提供一种适应IoMT系统独特特性的补充安全解决方案。因此，本文探讨了基于ML的IDS如何解决IoMT系统中的安全和隐私问题。首先，给出了IoMT通用的三层架构，并概述了IoMT系统的安全需求。然后，识别了可能影响IoMT安全的各种威胁，并介绍了构成IoMT的三个层级中基于ML的每种解决方案所采用的方法、数据集及其优缺点。最后，本文讨论了在IoMT各层级应用基于ML的IDS所面临的挑战和局限性，这可作为未来的研究方向。