Security vulnerabilities, attacks, countermeasures, and regulations of networked medical devices – a review
Abstract
Over the last few years, healthcare administrations have been digitizing their provision of care that led to an increased number of networked medical devices and medical telemetry. Due to such digitization, medical devices have made phenomenal strides in the course of the last half-century. These networked medical devices have enhanced the quality and accessibility of health treatments by achieving pervasive healthcare vision. Moreover, these devices have transformed the canvas of medical treatments and improved the lives of the masses. Such innovation, as a result, assisted in paving the way for reliable healthcare facilities through the introduction of new areas of therapeutic and diagnostic treatments. Medical devices, nowadays, are portable, networked, and capable enough to facilitate human lives. The refined quality and variety of these devices put forward a promising future. However, on the other hand, the healthcare sector is experiencing the greatest amount of security breaches due to the presence of security flaws in medical devices. As these devices are no longer standalone systems and are network-connected, the attack surface has increased profoundly. Actually, devices in practice were designed, developed, and disseminated long ago. Therefore, they were not developed from the ground up with security as a vital design constraint. The flaws present in these devices have acquired the consideration of researchers from both industry and academia. In this paper, we studi…
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Any cyber-attack may have a drastic impact, risking the life of patients.
“Potentially, any cyber-attack may have a drastic impact, risking the life of patients (Yaacoub et al., 2020; Yaqoob et al., 2019).”
From IoMT amid COVID-19 pandemic: Application, architecture, technology, and security (2021) · cites this paper as Yaqoob et al., 2019
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Fouad, M. M., El-Bendary, N., Ramadan, R. Α., et al. (2013) · CRC Press eBooks · doi:10.1201/b15425-32 · Cited by 8 · see citing works
Towards an Inherently Secure Run-Time Environment for Medical Devices
Bresch, C., Chollet, S., & Hély, D. (2018) · IEEE International Congress on Internet of Things (ICIOT) · doi:10.1109/iciot.2018.00027 · Cited by 6 · see citing works
Barlas, S. (2017) · PubMed · Cited by 6 · see citing works
Program Anomaly Detection Against Data-Oriented Attacks
Long, C. (2018) · VTechWorks (Virginia Tech) · Cited by 5 · see citing works
Principles of Data Flow Integrity: Specification and Enforcement
Ramezanifarkhani, T., & Razzazi, M. (2015) · doi:10.6688/jise.2015.31.2.9 · Cited by 4 · see citing works
Jagannathan, S., & Sorini, A. (2016) · IEEE Symposium on Product Compliance Engineering (ISPCE) · doi:10.1109/ispce.2016.7492841 · Cited by 4 · see citing works
DEMİR, A., & Tatlı, E. İ. (2018) · Bilişim Teknolojileri Dergisi · doi:10.17671/gazibtd.301668 · Cited by 2 · see citing works
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IoMT amid COVID-19 pandemic: Application, architecture, technology, and security
Mohd Aman, A. H., Hassan, W. H., Sameen, S., Attarbashi, Z. S., Alizadeh, M., & Latiff, L. A. (2021) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2020.102886 · Cited by 237 · see citing works
A Survey on Security and Privacy Issues in Modern Healthcare Systems: Attacks and Defenses
Newaz, A. I., Sikder, A. K., Rahman, M. A., & Uluagac, A. S. (2021) · ACM Transactions on Computing for Healthcare · doi:10.1145/3453176 · Cited by 225 · see citing works
Rasool, R. U., Ahmad, H. F., Rafique, W., Qayyum, A., & Qadir, J. (2022) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2022.103332 · Cited by 177 · see citing works
Security of Blockchain and AI-Empowered Smart Healthcare: Application-Based Analysis
Alabdulatif, A., Khalil, I., & Saidur Rahman, M. (2022) · Applied Sciences · doi:10.3390/app122111039 · Cited by 117 · see citing works
Dew-Cloud-Based Hierarchical Federated Learning for Intrusion Detection in IoMT
Singh, P., Gaba, G. S., Kaur, A., Hedabou, M., & Gurtov, A. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3186250 · Cited by 103 · see citing works
Blockchain Enabled Anonymous Privacy-Preserving Authentication Scheme for Internet of Health Things
Rajasekaran, A. S., Maria, A., Rajagopal, M., & Lorincz, J. (2022) · Sensors · doi:10.3390/s23010240 · Cited by 26 · see citing works
Boussi Rahmouni, H., Hassine, N. B. E. H., Chouchen, M., Ceylan, H. İ., Muntean, R. I., Bragazzi, N. L., & Dergaa, I. (2025) · Healthcare · doi:10.3390/healthcare13202553 · Cited by 25 · see citing works
Hong, A. E., Malinovsky, P. P., & Damodaran, S. K. (2024) · Digital Threats: Research and Practice · doi:10.1145/3604434 · Cited by 11 · see citing works
- Authors
- Yaqoob, T., Abbas, H., & Atiquzzaman, M.
- Affiliations
- National University of Sciences and Technology (PK)
- University of Oklahoma (US)
- Venue
- IEEE Communications Surveys & Tutorials
- Published
- 2019
- Volume
- 21
- Issue
- 4
- Pages
- 3723-3768
- ISSN
- 1553-877X (ISSN-L)
- 2373-745X
- OpenAlex
- W2943017035
- Cited by
- 236
- References
- 115
- Citations by year
- 2026: 21 · 2025: 40 · 2024: 40 · 2023: 43 · 2022: 33 · 2021: 38 · 2020: 19 · 2019: 2
- FWCI
- 12.59
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Communications Surveys & Tutorials (publishedVersion)
- Funders
- Higher Education Commission, Pakistan (2(1078)/HEC/M&E/2018/707)
- 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 › Computer Science › Information Systems › Information and Cyber Security
- Keywords
- networked medical devices, medical device security, security vulnerabilities, security countermeasures, healthcare cybersecurity, biotelemetry, security regulations
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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