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Internet of Medical Things (IoMT) is the new generation of medical devices. It unleashes a confluence of technology that transforms the healthcare industry, where it facilitates in remote diagnostics, personalized treatment, and real-time health monitoring. These interconnected devices generate vast amounts of sensitive data, which, when unprotected, are prone to breaches. However, these devices' resource-constrained design and heterogeneous communication protocols lead to heterogeneous IoMT systems, making them vulnerable to substantial security attacks. Improvised approaches are needed because these problems are often beyond what conventional security mechanisms can adequately tackle. Machine learning might mitigate these issues by enabling IoMT devices to detect anomalies, anticipate risks, and effectively respond to attacks. The chapter discusses ML techniques and frameworks developed to protect IoMTs from cyberattacks. Federated learning and edge computing safeguard distributed IoMT networks and ensure the confidentiality of patient information. Data breaches, unauthorized access, denial-of-service attacks, and their implications for IoMT systems are outlined in detail. This chapter focuses on encryption, anonymization, and differential privacy during data sharing and usage. Many case studies are researched to test ML-based security solutions on IoMT systems in real life so that best practices and implementation methodologies are followed. This chapter reports how emerging technologies can bolster the IoMT systems to withstand advanced threats. Machine learning, cloud, fog, and edge computing should be incorporated throughout all the components of an IoMT system to provide a robust and flexible security paradigm. We also discuss how blockchain technology combined with ML can enhance data reliability and visibility in IoMT ecosystems. It will enable researchers, medical practitioners, and engineers to better protect IoMT systems from cyber threats by providing compelling analysis and detailing practical recommendations. It looks ahead to integrating machine learning with modern computing paradigms to secure and privacy-protect healthcare applications.

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Using Machine Learning for Protecting the Security and Privacy of Internet of Medical Things (IoMT) Systems

  • 章节

  • 发表日期：

    2026年

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  • Himanshu Sharma,
  • Prabhat Kumar,
  • Gulshan Shrivastava,
  • Kavita Sharma,
  • Amit Bhola

摘要

Internet of Medical Things (IoMT) is the new generation of medical devices. It unleashes a confluence of technology that transforms the healthcare industry, where it facilitates in remote diagnostics, personalized treatment, and real-time health monitoring. These interconnected devices generate vast amounts of sensitive data, which, when unprotected, are prone to breaches. However, these devices' resource-constrained design and heterogeneous communication protocols lead to heterogeneous IoMT systems, making them vulnerable to substantial security attacks. Improvised approaches are needed because these problems are often beyond what conventional security mechanisms can adequately tackle. Machine learning might mitigate these issues by enabling IoMT devices to detect anomalies, anticipate risks, and effectively respond to attacks. The chapter discusses ML techniques and frameworks developed to protect IoMTs from cyberattacks. Federated learning and edge computing safeguard distributed IoMT networks and ensure the confidentiality of patient information. Data breaches, unauthorized access, denial-of-service attacks, and their implications for IoMT systems are outlined in detail. This chapter focuses on encryption, anonymization, and differential privacy during data sharing and usage. Many case studies are researched to test ML-based security solutions on IoMT systems in real life so that best practices and implementation methodologies are followed. This chapter reports how emerging technologies can bolster the IoMT systems to withstand advanced threats. Machine learning, cloud, fog, and edge computing should be incorporated throughout all the components of an IoMT system to provide a robust and flexible security paradigm. We also discuss how blockchain technology combined with ML can enhance data reliability and visibility in IoMT ecosystems. It will enable researchers, medical practitioners, and engineers to better protect IoMT systems from cyber threats by providing compelling analysis and detailing practical recommendations. It looks ahead to integrating machine learning with modern computing paradigms to secure and privacy-protect healthcare applications.