Intrusion detection system for IoMT in smart hospitals: a systematic literature review

Fatima, Sadia and Kashif, Muhammad Ismail and Zahra, Khadija Tuz and Adnan, Muhammad (2026) Intrusion detection system for IoMT in smart hospitals: a systematic literature review. Internet of Things, 39. p. 102008. ISSN 2542-6605

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Abstract

With the quick development of smart healthcare, the Internet of Medical Things (IoMT) has emerged as a paradigm-changing technology. It connects wearable devices, smart sensors, and medical systems to enable real-time, patient-centric care. To protect these life-saving systems, their security is essential and often requires the use of specific Intrusion Detection Systems (IDS). However, such solutions have yet to address complex medical networks. A systematic literature review is conducted to reveal the current state and future trends in the IDS framework for IoMT. The 32 studies identified in prominent digital databases were selected for in-depth qualitative and quantitative analysis. The total number of datasets used in the selected studies is 11; some of these achieve high accuracy (98%-99.9%). The frequently used models are XG Boost (XGB), K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), and Convolutional Neural Network. (CNN). IDS with AI methods, primarily ML, DL, and hybrid models, are very much popular. High accuracy and low latency are the priorities of such systems. However, most are poorly adapted to IoMT energy and resource constraints. This review aims to bridge the gap between algorithmic innovations and clinical utilities. Numerous frameworks also overlook privacy, real-time implementation, and regulatory compliance. We advocate for context-aware systems that center on patient safety.

Item Type:	Article
Identification Number:	10.1016/j.iot.2026.102008
Dates:	Date Event 1 July 2026 Accepted 13 July 2026 Published Online
Uncontrolled Keywords:	Intrusion detection systems (IDS) Internet of medical things (IoMT) Healthcare IoMT security Privacy protection Decentralized learning Primary cybersecurity threats Edge computing Federated learning (FL)
Subjects:	CAH10 - engineering and technology > CAH10-01 - engineering > CAH10-01-01 - engineering (non-specific)
Divisions:	Architecture, Built Environment, Computing and Engineering > Engineering
Depositing User:	Gemma Tonks
Date Deposited:	26 Aug 2026 10:29
Last Modified:	26 Aug 2026 10:29
URI:	https://www.open-access.bcu.ac.uk/id/eprint/17193

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