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<p>The Internet of Medical Things (IoMT) domain, also called clinical IoT systems, is quite helpful for the simulation of devices and applications concerned with clinical informatics. The execution of this platform needs a simulating model to be efficiently mapped towards the working functionality for efficient delivery of quality healthcare at any cost. This research presents a new model for web intrusion detection in IoMT systems by developing machine learning algorithms based on ensemble learning. Our approach collects, analyzes, and organizes relevant and suspect data traffic during training in machine learning algorithms. The experimentation was carried out using different cohort methods, and I learned how to better identify and deliver attacks more effectively by tabulating the findings. Our approach, which focuses on the medical system incorporating Internet Sensors, incorporates continuous traffic monitoring in and out of the system. Using properties such as source and destination IP addresses, packet sizes, protocols, etc., we have classified the traffic accordingly with signatures and anomalies. The ensemble modelling of our proposed approach using XG boost provides an improved accuracy of about 99.67% with a P-value &lt; 0.001, respectively. The visual data exploration has been made using ROC graph analysis for all models, with a roc-aug score of 0.9963 for KNN, 0.9965 for the Decision tree, 0.9966 for the random forest,0.9965 for XG-boost and 0.9946 for the Ada- Boost model. This analysis provides an intelligent attack, Practical insights, and a strategy that can be used to improve the cybersecurity of healthcare systems.</p>

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A New Model to Evaluate Signature and Anomaly Based Intrusion Detection in Medical IoT System Using Ensemble Approach

  • 期刊论文

  • 发表日期：

    2025年4月3日

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  • A. Sheik Abdullah,
  • Hridhik John Sunil,
  • Mohamed Saleem Haja Nazmudeen

摘要

The Internet of Medical Things (IoMT) domain, also called clinical IoT systems, is quite helpful for the simulation of devices and applications concerned with clinical informatics. The execution of this platform needs a simulating model to be efficiently mapped towards the working functionality for efficient delivery of quality healthcare at any cost. This research presents a new model for web intrusion detection in IoMT systems by developing machine learning algorithms based on ensemble learning. Our approach collects, analyzes, and organizes relevant and suspect data traffic during training in machine learning algorithms. The experimentation was carried out using different cohort methods, and I learned how to better identify and deliver attacks more effectively by tabulating the findings. Our approach, which focuses on the medical system incorporating Internet Sensors, incorporates continuous traffic monitoring in and out of the system. Using properties such as source and destination IP addresses, packet sizes, protocols, etc., we have classified the traffic accordingly with signatures and anomalies. The ensemble modelling of our proposed approach using XG boost provides an improved accuracy of about 99.67% with a P-value < 0.001, respectively. The visual data exploration has been made using ROC graph analysis for all models, with a roc-aug score of 0.9963 for KNN, 0.9965 for the Decision tree, 0.9966 for the random forest,0.9965 for XG-boost and 0.9946 for the Ada- Boost model. This analysis provides an intelligent attack, Practical insights, and a strategy that can be used to improve the cybersecurity of healthcare systems.