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Title	Intrusion Detection System For Healthcare Environment
ID_Doc	33341
Authors	Karthick Mani Raja J.; Anish S.; Ashwin B.; Sasidharan D.; Vanitha V.
Year	2025
Published	2025 3rd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2025
DOI	http://dx.doi.org/10.1109/ICAECA63854.2025.11012630
Abstract	All items that have the ability to communicate, either directly or indirectly, with Internet-connected electronic devices are included in the Internet of Things (IoT), which is an expansion of the existing Internet. IoT provides services in many facets of human life, including smart cities, homes, transportation, and health. One of the main concerns is the security of these parts and data transfers. For operations in the medical field, medical staff processes orders through the Internet of Medical Things (IoMT). Users not authorized may also upload dummy orders with the intent of disabling the operability of devices. The problem of abuse is that an adequate security system capable of tracking actions at any given time throughout the entire process is required to prevent such attempts. This paper introduces a supervised machine-learning driven intrusion detection system (IDS) for the Internet of Medical Things (IoMT). We have applied some feature selection methods in attempts to improve the performance of the system. This ML-based IDS was designed in order to mitigate risks or breaches on IoMT devices suspected to be under malicious or suspicious activities. © 2025 IEEE.
Author Keywords	Feature Selection; Healthcare; Internet of Medical Things (IoMT); Intrusion Detection System (IDS); Machine Learning (ML)

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