A Survey on Security Threats and Countermeasures in Internet of Medical Things (IoMT)
Abstract
Internet of medical things (IoMT) is an emerging technology aiming to improve the patient's quality of life by enabling personalized e‐health services without limitations on time and location. Nevertheless, IoMT devices (eg, medical sensors) that constitute the key underlying elements of the IoMT edge network are vulnerable to various types of security threats and thus, they pose a significant risk to patient's privacy and safety. Based on that and the fact that the security is a critical factor for the successful integration of IoMT technology into pervasive healthcare systems, there is an urgent need for novel security mechanisms to preserve the security of the IoMT edge network. Toward this direction, the first step is the comprehensive understanding of existing and potential threats to the IoMT edge network environment. Thus, in this article, we provide a categorization of security threats to the edge network environment based on the major security objectives that they target. Moreover, we present a categorization of security countermeasures, derived from the literature, against threats to IoMT edge networks. The authors' intent is to provide a foundation for organizing research efforts toward the development of proper security countermeasures for protecting IoMT edge networks against internal and external threats.
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Attacks from an intruder can access information sent from the hardware system by directly attacking at the decryption point or by weakening the authentication procedure, such as eavesdropping, reply, MITM, rouge, DoS, and DDoS attacks, and these hamper the network layer of the IoMT.
“Then, some attacks from the intruder can access the information sent from the hardware system by directly attacking at the decryption point or by weakening the authentication procedure such as Eavesdropping attack, reply attack, MITM attack, rouge attack, DoS attack, DDoS attack and these type of attack causes to hamper the network layer of the IoMT [200].”
From Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues (2023) · cites this paper as [200]
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- Authors
- Papaioannou, M., Karageorgou, M., Mantas, G., Sucasas, V., Essop, I., Rodriguez, J., & Lymberopoulos, D.
- Affiliations
- Instituto de Telecomunicações Aveiro Portugal
- University of Patras (GR)
- Instituto de Telecomunicações (PT)
- University of Greenwich (GB)
- Venue
- Transactions on Emerging Telecommunications Technologies
- Publisher
- Wiley
- Published
- 2020-07-23
- Volume
- 33
- Issue
- 6
- ISSN
- 2161-3915 (ISSN-L)
- OpenAlex
- W3045149779
- Cited by
- 245
- References
- 73
- Citations by year
- 2026: 32 · 2025: 44 · 2024: 42 · 2023: 61 · 2022: 46 · 2021: 17 · 2020: 3
- FWCI
- 14.92
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Transactions on Emerging Telecommunications Technologies (publishedVersion)
- Greenwich Academic Literature Archive (University of Greenwich) (acceptedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Physical Sciences › Engineering › Biomedical Engineering › Wireless Body Area Networks
- Keywords
- Internet of Medical Things, security countermeasures, biomedical sensors, patient privacy, pervasive healthcare, threat categorization, security objectives
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