AI-Driven Supply Chain Security Framework for Healthcare IoT Ecosystems: A Zero-Trust Approach to Medical Device Vulnerability Management

References

[1] Abdulmalek, S., Nasir, A., Jabbar, W. A., Almuhaya, M. A. M., Bairagi, A. K., Khan, M. A.M., & Kee, S.H. (2022). IoTbased healthcaremonitoring system towards improving quality of life: A review. Healthcare, 10(10), 1993.https://doi.org/10.3390/healthcare10101993

[2] Adahman, Z., Malik, A. W., & Anwar, Z. (2022). An analysis of zero-trust architecture and its cost-effectiveness for organizational security. Computers & Security, 122, 102911. https://doi.org/10.1016/j.cose.2022.102911

[3] Aledhari, M., Razzak, R., Qolomany, B., Al-Fuqaha, A., & Saeed, F. (2022). Biomedical IoT: Enabling technologies, architectural elements, challenges, and future directions. IEEE Access, 10, 31306–31339. 10.1109/ACCESS.2022.3159235

[4] Al-Garadi, M. A., Mohamed, A., Al-Ali, A. K., Du, X., Ali, I., & Guizani, M. (2020). A survey of machine and deep learning methods for Internet of Things (IoT) security. IEEE Communications Surveys & Tutorials, 22(3), 1646–1685. https://doi.org/10.1109/COMST.2020.2988293

[5] Ali, B., Gregory, M. A., & Li, S. (2021). Uplifting Healthcare Cyber Resilience with a Multi-access Edge Computing Zero-Trust Security model. 2021 31st International Telecommunication Networks and Applications Conference (ITNAC), 192–197. https://doi.org/10.1109/itnac53136.2021.9652141

[6] Alshehri, F., & Muhammad, G. (2021). A comprehensive survey of the internet of things (IOT) and AI-based Smart Healthcare. IEEE Access, 9, 3660–3678. https://doi.org/10.1109/access.2020.3047960

[7] Baker, S. D. (2022). The ironic state of cybersecurity in medical devices. Biomedical Instrumentation & Technology, 56(3), 98–101. https://doi.org/10.2345/0899-8205-56.3.98

[8] Belhadi, A., Kamble, S. S., Jabbour, C. J. C., Gunasekaran, A., Ndubisi, N. O., & Venkatesh, M. (2021). Artificial intelligence-driven supply chain resilience: A systematic literature review and a research agenda. International Journal of Production Research, 59(7), 2100–2123. https://doi.org/10.1080/00207543.2020.1824084

[9] Brady, E., Loseke, T., Potts, J., & Yuan, B. (2020). Securing the medical device supply chain: A systematic literature review. IEEE Access, 8, 209783–209800. https://doi.org/10.1109/ACCESS.2020.3038472

[10] Chen, B., Qiao, S., Zhao, J., Liu, D., Shi, X., Lyu, M., Chen, H., Lu, H., & Zhai, Y. (2021). A security awareness and protection system for 5G smart healthcare based on zero-trust architecture. IEEE Internet of Things Journal, 8(13), 10248–10263. https://doi.org/10.1109/jiot.2020.3041042

[11] Collier, Z. A., & Sarkis, J. (2021). The zero trust supply chain: Managing supply chain risk in the absence of trust. International Journal of Production Research, 59(11), 3430–3445. https://doi.org/10.1080/00207543.2021.1884311

[12] Elsayed, N., Abdelgawad, A., & Elsayed, Z. (2022). Cybersecurity and frequent cyber attacks on IoT devices in healthcare: Issues and solutions. IEEE Access, 10, 110802–110829. https://doi.org/10.1109/ACCESS.2022.3200213

[13] Ferretti, L., Marchetti, M., & Andreolini, M. (2021). Survivable zero trust for cloud computing environments. Computers & Security, 103, 102171. https://doi.org/10.1016/j.cose.2021.102171

[14] Ghubaish, A., Salman, T., Zolanvari, M., Unal, D., Al-Ali, A., & Jain, R. (2021). Recent advances in the internet-of-medical-things (IOMT) systems security. IEEE Internet of Things Journal, 8(11), 8707–8718. https://doi.org/10.1109/jiot.2020.3045653

[15] Hady, A. A., Ghubaish, A., Salman, T., Unal, D., & Jain, R. (2020). Intrusion detection system for healthcare systems using medical and network data: A comparison study. IEEE Access, 8, 106576–106584. https://doi.org/10.1109/access.2020.3000421

[16] He, Y., Aliyu, A., Evans, M., & Luo, C. (2021). Health care cybersecurity challenges and solutions under the climate of COVID19: Scoping review. Journal of Medical Internet Research, 23(4), e21747. https://doi.org/10.2196/21747

[17] Kioskli, K., Grigoriou, E., Islam, S., Yiorkas, A. M., Christofi, L., & Mouratidis, H. (2022). A risk and conformity assessment framework to ensure security and resilience of healthcare systems and medical supply chain. International Journal of Information Security, 21(6), 1339–1360. https://doi.org/10.1007/s10207-021-00587-4

[18] Køien, G. M. (2021). Zero-trust principles for legacy components: 12 rules for legacy devices—An antidote to chaos. Wireless Personal Communications, 121(2), 1169–1186. https://doi.org/10.1007/s11277-021-09055-1

[19] Li, S., Iqbal, M., & Saxena, N. (2022). Future industry Internet of Things with zerotrust security. Information Systems Frontiers, 24(5), 1583–1596. https://doi.org/10.1007/s10796-021-10152-3

[20] Liu, C., Tan, R., Wu, Y., Feng, Y., Jin, Z., Zhang, F., Liu, Y., & Liu, Q. (2022). Dissecting zero trust: Research landscape and its implementation in IoT. Cybersecurity, 5(1), 1–31. https://doi.org/10.1186/s42400-022-00110-2

[21] Markus, A. F., Kors, J. A., & Rijnbeek, P. R. (2021). The role of explainability in creating trustworthy artificial intelligence for health care: A comprehensive survey of the terminology, design choices, and evaluation strategies. Journal of Biomedical Informatics, 113, 103655. https://doi.org/10.1016/j.jbi.2020.103655

[22] Mavroeidakos, T., Georgiou, O., & Voulkidis, A. (2022). Zero trust based cybersecurity architecture for healthcare IoT using artificial intelligence. IEEE Access, 10, 75432–75445. https://doi.org/10.1109/ACCESS.2022.3187654

[23] Mushtaq, S., Mohsin, M., Mushtaq, M. M., & others. (2022). A systematic literature review on the implementation and challenges of zero trust architecture across domains. Sensors, 22(19), 1–30. https://doi.org/10.3390/s22197234

[24] Naz, F., Kumar, A., Majumdar, A., & Agrawal, R. (2022). Is artificial intelligence an enabler of supply chain resiliency post COVID-19? An exploratory state-of-the-art review for future research. Operations Management Research, 15(1–2), 378–398. https://doi.org/10.1007/s12063-021-00208-w

[25] Ngueajio, C., Bassirou, A. B., Abdou, A. G., & Ali, M. L. (2022). A comprehensive survey of intrusion detection systems using machine learning for IoT and cyber–physical systems. International Journal of Information Security and Privacy, 16(3), 1–27. https://doi.org/10.4018/IJISP.310110

[26] Pavlov, A., Bharadwaj, S. S., & Vasilyeva, E. (2022). Zero trust architecture for healthcare cyber-physical systems: A systematic review of design principles and implementation challenges. IEEE Access, 10, 45701–45725. https://doi.org/10.1109/ACCESS.2022.3172105

[27] Pise, A. A., Almuzaini, K. K., Ahanger, T. A., Farouk, A., Pant, K., Pareek, P. K., & Nuagah, S. J. (2022). Enabling Artificial Intelligence of Things (AIoT) healthcare architectures and listing security issues. Computational Intelligence and Neuroscience, 2022, Article 8421434. https://doi.org/10.1155/2022/8421434

[28] Qiu, S., Liu, Q., Zhou, S., & Huang, W. (2022). Adversarial attack and defense technologies in Natural Language Processing: A Survey. Neurocomputing, 492, 278–307. https://doi.org/10.1016/j.neucom.2022.04.020 3

[29] Radanliev, P., & De Roure, D. (2022). Advancing the cybersecurity of the healthcare system with self optimising and self adaptative artificial intelligence (part 2). Health and Technology, 12(5), 923–929. https://doi.org/10.1007/s12553-022-00691-6

[30] Rasool, R. U., Kausar, M. A., Shah, M. A., & Javaid, N. (2022). Health IoT threats: Survey of risks and vulnerabilities. Future Internet, 14(11), 389. https://doi.org/10.3390/fi14110389

[31] Said, A. M., Yahyaoui, A., & Abdellatif, T. (2021). Efficient anomaly detection for smart hospital IoT systems. Sensors, 21(4), 1026. https://doi.org/10.3390/s21041026

[32] Schwartz, S. M., Ross, A., Carmody, S., Chase, P., Coley, S. C., Connolly, J., Petrozzino, C., & Zuk, M. (2018). The evolving state of medical device cybersecurity. Biomedical Instrumentation & Technology, 52(2), 103–111. https://doi.org/10.2345/0899-8205-52.2.103

[33] Sfalionis, D., Kylilis, N., Gatzoulis, L., & Fysarakis, K. (2022). Cybersecurity risk management for medical devices and healthcare supply chains: Challenges and recommendations. Health Policy and Technology, 11(2), 100624. https://doi.org/10.1016/j.hlpt.2022.100624

[34] Shu, R., Zhang, K., Xiong, H., Wang, W., Zhu, H., & Zhang, Y. (2022). IoT device fingerprinting and abnormal behavior detection via deep learning. IEEE Internet of Things Journal, 9(10), 7164–7178. https://doi.org/10.1109/JIOT.2021.3119829

[35] Sultana, M., Hossain, A., Laila, F., Taher, K. A., & Islam, M. N. (2020). Towards developing a secure medical image sharing system based on zero trust principles and blockchain technology. BMC Medical Informatics and Decision Making, 20(1), 266. https://doi.org/10.1186/s12911-020-01300-4

[36] Susilo, B., & Sari, R. F. (2020). Intrusion detection in IoT networks using deep learning algorithm. Information, 11(6), 279. https://doi.org/10.3390/info11060279

[37] Thomasian, N. M., & Adashi, E. Y. (2021). Cybersecurity in the internet of medical things. Health Policy and Technology, 10(3), 100549. https://doi.org/10.1016/j.hlpt.2021.100549

[38] Tyler, D., & Viana, T. (2021). Trust no one? A framework for assisting healthcare organisations in transitioning to a zero-trust network architecture. Applied Sciences, 11(16), 7356. https://doi.org/10.3390/app11167499

@article{1712991,
      author = {Omowunmi Folashayo Makinde, Nathaniel Adeniyi Akande, Adetunji Oludele Adebayo, Sopuluchukwu Ani, Olatunde Ayomide Olasehan},
      title = {AI-Driven Supply Chain Security Framework for Healthcare IoT Ecosystems: A Zero-Trust Approach to Medical Device Vulnerability Management},
      journal = {Iconic Research And Engineering Journals},
      year = {2022},
      volume = {6},
      number = {5},
      pages = {300-317},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1712991.pdf},
      abstract = {The proliferation of Internet of Things (IoT) devices in healthcare environments has fundamentally transformed patient care delivery while simultaneously introducing significant cybersecurity vulnerabilities throughout medical device supply chains. This research proposes an integrated security framework combining artificial intelligence capabilities with Zero Trust architecture principles to address critical vulnerabilities affecting medical devices from manufacturing through deployment and ongoing operations. Through comprehensive analysis of supply chain weaknesses, AI-driven detection techniques, and Zero Trust controls, this study demonstrates how these complementary approaches create defense-in-depth protecting against firmware manipulation, counterfeit components, distribution vulnerabilities, and inconsistent update mechanisms. The framework provides healthcare organizations with actionable guidance for implementing continuous monitoring, device authentication, behavioral analytics, and policy enforcement while accommodating clinical operational requirements. Recommendations address stakeholder responsibilities across hospitals, device manufacturers, and regulatory bodies to strengthen medical device security posture comprehensively.},
      keywords = {Healthcare IoT, Medical Device Security, Supply Chain Vulnerability, Zero Trust Architecture, Artificial Intelligence, Cybersecurity Framework, Firmware Integrity, Device Authentication},
      month = {November},
      doi = {https://doi.org/10.64388/IREV6I5-1712991}
  }