Mitigating Cyber–Intrusions in Medical Devices with Agent–Based Self–Healing
References
- Ahmed, S.F., Alam, M. S.B., Afrin, S., Rafa, S.J., Rafa, N. and Gandomi, A.H. (2024). Insights into Internet of medical things (IoMT): Data fusion, security issues and potential solutions, Information Fusion 102: 102060.
- Baker, S.D. (2022). The ironic state of cybersecurity in medical devices, Biomedical Instrumentation & Technology 56(3): 98–101.
- Camara, C., Peris-Lopez, P., de Fuentes, J.M. and Marchal, S. (2021). Access control for implantable medical devices, IEEE Transactions on Emerging Topics in Computing 9(3): 1126–1138.
- Carreon-Rascon, A.S. and Rozenblit, J.W. (2022). Towards requirements for self-healing as a means of mitigating cyber-intrusions in medical devices, 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Prague, Czech Republic, pp. 1500–1505.
- Chen, X., Zhang, H., Wu, C., Mao, S., Ji, Y. and Bennis, M. (2019). Optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning, IEEE Internet of Things Journal 6(3): 4005–4018.
- Deja R., Froelich W., and Deja G. (2021). Mining clinical pathways for daily insulin therapy of diabetic children, International Journal of Applied Mathematics and Computer Science 31(1): 107–121, DOI: 10.34768/amcs-2021-0008.
- Dénes-Fazakas, L., Fazakas, G.D., Eigner, G., Kovács, L. and Szilágyi, L. (2024). Review of reinforcement learning-based control algorithms in artificial pancreas systems for diabetes mellitus management, 2024 IEEE 18th International Symposium on Applied Computational Intelligence and Informatics (SACI), Timisoara, Romania, pp. 565–572.
- Dong, X., Hariri, S., Xue, L., Chen, H., Zhang, M., Pavuluri, S. and Rao, S. (2003). Autonomia: An autonomic computing environment, Conference Proceedings of the 2003 IEEE International Performance, Computing, and Communications Conference, Phoenix, USA, pp. 61–68.
- FDA (2023). Content of premarket submissions for device software functions, US Food & Drug Administration, Rockville, https://www.fda.gov/regulatoryinformation/search-fda-guidance-documents/content-premarket-submissions-device-software-functions.
- FDA (2025). Cybersecurity in medical devices: Quality system considerations and content of premarket submissions, US Food & Drug Administration, Rockville, https://www.fda.gov/regulatory-information/search-fda-guidance-documents/cybersecuritymedical-devices-quality-system-considerations-and-content-premarket-submissions.
- Fox, I., Lee, J., Pop-Busui, R. and Wiens, J. (2020). Deep reinforcement learning for closed-loop blood glucose control, Proceedings of Machine Learning Research 2020(5): 508–536.
- Hassija, V., Chamola, V., Bajpai, B.C., Naren and Zeadally, S. (2021). Security issues in implantable medical devices: Fact or fiction?, Sustainable Cities and Society 66: 102552.
- HGV Research (2024a). Implantable medical device market size, share & trends analysis report by type, by end use, and segment forecasts, 2024–2030, Horizon Grand View Research, San Francisco, https://www.grandviewresearch.com/horizon/outlook/implantable-medical-device-market-size/global .
- HGV Research (2024b). Implantable medical devices market analysis report, Horizon Grand View Research, San Francisco, https://www.grandviewresearch.com/industry-analysis/implantable-medical-devices-market-report.
- IBM (2024). Cost of a data breach report 2024, IBM, Armonk, https://www.ibm.com/reports/data-breach.
- Johnphill, O., Sadiq, A.S., Al-Obeidat, F., Al-Khateeb, H., Taheir, M.A., Kaiwartya, O. and Ali, M. (2023). Self-healing in cyber–physical systems using machine learning: A critical analysis of theories and tools, Future Internet 15(7), Article no. 244.
- Kala, R. (2023). Autonomous Mobile Robots: Planning, Navigation, and Simulation, Academic Press, Cambridge, pp. 669–713.
- Kegyes, T., Süle, Z. and Abonyi, J. (2021). The applicability of reinforcement learning methods in the development of industry 4.0 applications, Complexity 2021(1): 7179374.
- Kingma, D.P. (2014). Adam: A method for stochastic optimization, arXiv: 1412.6980.
- Koopman, P. (2003). Elements of the self-healing system problem space, Workshop on Architecting Dependable Systems, Portland, USA, pp. 31–36.
- Kuehn, B.M. (2018). Pacemaker recall highlights security concerns for implantable devices, Circulation 138(15): 1597–1598.
- Muhammad, G., Alshehri, F., Karray, F., Saddik, A.E., Alsulaiman, M. and Falk, T.H. (2021). A comprehensive survey on multimodal medical signals fusion for smart healthcare systems, Information Fusion 76: 355–375.
- NRC (2001). Embedded, Everywhere: A Research Agenda for Networked Systems of Embedded Computers, National Research Council/National Academies Press, Washington, pp. 77–79.
- Pirbhulal, S., Zhang, H., Wu, W., Mukhopadhyay, S.C. and Zhang, Y.-T. (2018). Heartbeats based biometric random binary sequences generation to secure wireless body sensor networks, IEEE Transactions on Biomedical Engineering 65(12): 2751–2759.
- Pritika, Shanmugam, B. and Azam, S. (2023). Risk assessment of heterogeneous IoMT devices: A review, Technologies 11(1), Article no. 31.
- Psaier, H. and Dustdar, S. (2011). A survey on self—healing systems: Approaches and systems, Computing 91(1): 43–73.
- Rafajłowicz, W. (2022). Learning Decision Sequences For Repetitive Processes—Selected Algorithms, Springer, Cham.
- Rao, A., Carreón, N., Lysecky, R. and Rozenblit, J. (2017). Probabilistic threat detection for risk management in cyber-physical medical systems, IEEE Software 35(1): 38–43.
- Rathore, H., Mohamed, A. and Guizani, M. (2020). Deep learning-based security schemes for implantable medical devices, in A. Mohamed (Ed.), Energy Efficiency of Medical Devices and Healthcare Applications, Academic Press, Cambridge, pp. 109–130.
- Sallab, A.E., Abdou, M., Perot, E. and Yogamani, S. (2017). Deep reinforcement learning framework for autonomous driving, arXiv: 1704.02532.
- Seiger, R., Huber, S. and Schlegel, T. (2015). Proteus: An integrated system for process execution in cyber-physical systems, International Workshop on Business Process Modeling, Development and Support, Stockholm, Sweden, pp. 265–280, DOI: 10.1007/978-3-319-19237-6_17.
- Seiger, R., Huber, S. and Schlegel, T. (2018). Toward an execution system for self-healing workflows in cyber-physical systems, Software & Systems Modeling 17(2): 551–572, DOI: 10.1007/s10270-016-0551-z.
- Sutton, R. and Barto, A. (2018). Reinforcement Learning: An Introduction, MIT Press, Cambridge.
- Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A., Kaiser, L. and Polosukhin, I. (2017). Attention is all you need, 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, USA.
- Wang, G., Liu, X., Ying, Z., Yang, G., Chen, Z., Liu, Z., Zhang, M., Yan, H., Lu, Y., Gao, Y., Xue, K., Li, X. and Chen, Y. (2023). Optimized glycemic control of type 2 diabetes with reinforcement learning: A proof-of-concept trial, Nature Medicine 29(10): 2633–2642, DOI: 10.1038/s41591-023-02552-9.
- Yu, K.-H., Beam, A.L. and Kohane, I.S. (2018). Artificial intelligence in healthcare, Nature Biomedical Engineering 2(10): 719–731.
- Zabihi, Z., Eftekhari Moghadam, A.M. and Rezvani, M.H. (2023). Reinforcement learning methods for computation offloading: A systematic review, ACM Computing Surveys 56(1): 1–41.
Language: English
Page range: 519 - 533
Submitted on: Jan 14, 2025
Accepted on: Jun 30, 2025
Published on: Sep 8, 2025
Published by: University of Zielona Góra
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
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© 2025 Ana S. Carreon-Rascon, Huayu Li, Jerzy W. Rozenblit, Wojciech Rafajłowicz, published by University of Zielona Góra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.