ARCHIVES
Original Article
Cyber security Risk Assessment in Medical IoT (MIoT) Networks
Balakshaj B1
Balaji Sai Y2
Dr. J. Refonaa3
1 2 3 Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology Chennai, Tamil Nadu, India.
Published Online: March-April 2026
Pages: 109-115
Cite this article
↗ https://www.doi.org/10.59256/ijire.20260702015
References
1. T. E. Ali, F. I. Ali, P. Dakić, and A. D. Zoltan, “Trends, prospects, challenges, and security in the healthcare internet of things,”
Computing, vol. 107, no. 1, 2025.
2. M. Babar, M. U. Tariq, Z. Ullah, F. Arif, Z. Khan, and B. Qureshi, “An efficient and hybrid deep learning-driven model to enhance
security and performance of healthcare internet of things,” IEEE Access, 2025.
3. A. U. Rehman et al., “Internet of Things in healthcare research: Trends, innovations, security considerations, challenges and future
strategy,” International Journal of Intelligent Systems, 2025.
4. S. Szymoniak, J. Piątkowski, and M. Kurkowski, “Defense and security mechanisms in the Internet of Things: A review,” Applied
Sciences, vol. 15, no. 2, 2025.
5. H. Sebestyen, D. E. Popescu, and R. D. Zmaranda, “A literature review on security in the Internet of Things,” Computers, vol. 14,
no. 2, 2025.
6. A. A. Khan et al., “BDLT-IoMT: A novel architecture for secure data processing in Internet of Medical Things,” Journal of
Supercomputing, vol. 81, no. 1, 2025.7. R. P. Pinto, B. M. Silva, and P. R. Inácio, “Federated learning for anomaly detection on Internet of Medical Things: A survey,” Internet
of Things, 2025.
8. M. Hizem et al., “Reliable ECG anomaly detection on edge devices for IoMT applications,” Sensors, vol. 25, no. 8, 2025.
9. H. Goumidi and S. Pierre, “Real time anomaly detection in IoMT networks,” IEEE Access, 2025.
10. P. Chandekar, M. Mehta, and S. Chandan, “Enhanced anomaly detection in IoMT networks using ensemble AI models,” arXiv
preprint, 2025.
11. D. Abshari and M. Sridhar, “A survey of anomaly detection in cyber physical systems,” arXiv preprint, 2025.
12. A. Mehmmod et al., “ERBM: A machine learning-driven rule- based model for intrusion detection in IoT environments,” Computers,
Materials & Continua, 2025.
13. K. Wang et al., “ATHENA: An intrusion detection framework for in-vehicle systems,” arXiv preprint, 2025.
14. A. Almalawi et al., “Hybrid cybersecurity for asymmetric threats,” Symmetry, vol. 17, no. 4, 2025.
15. S. Srivastav et al., “HYRIDE: Hybrid and robust intrusion detection approach,” Internet of Things, vol. 30, 2025.
Computing, vol. 107, no. 1, 2025.
2. M. Babar, M. U. Tariq, Z. Ullah, F. Arif, Z. Khan, and B. Qureshi, “An efficient and hybrid deep learning-driven model to enhance
security and performance of healthcare internet of things,” IEEE Access, 2025.
3. A. U. Rehman et al., “Internet of Things in healthcare research: Trends, innovations, security considerations, challenges and future
strategy,” International Journal of Intelligent Systems, 2025.
4. S. Szymoniak, J. Piątkowski, and M. Kurkowski, “Defense and security mechanisms in the Internet of Things: A review,” Applied
Sciences, vol. 15, no. 2, 2025.
5. H. Sebestyen, D. E. Popescu, and R. D. Zmaranda, “A literature review on security in the Internet of Things,” Computers, vol. 14,
no. 2, 2025.
6. A. A. Khan et al., “BDLT-IoMT: A novel architecture for secure data processing in Internet of Medical Things,” Journal of
Supercomputing, vol. 81, no. 1, 2025.7. R. P. Pinto, B. M. Silva, and P. R. Inácio, “Federated learning for anomaly detection on Internet of Medical Things: A survey,” Internet
of Things, 2025.
8. M. Hizem et al., “Reliable ECG anomaly detection on edge devices for IoMT applications,” Sensors, vol. 25, no. 8, 2025.
9. H. Goumidi and S. Pierre, “Real time anomaly detection in IoMT networks,” IEEE Access, 2025.
10. P. Chandekar, M. Mehta, and S. Chandan, “Enhanced anomaly detection in IoMT networks using ensemble AI models,” arXiv
preprint, 2025.
11. D. Abshari and M. Sridhar, “A survey of anomaly detection in cyber physical systems,” arXiv preprint, 2025.
12. A. Mehmmod et al., “ERBM: A machine learning-driven rule- based model for intrusion detection in IoT environments,” Computers,
Materials & Continua, 2025.
13. K. Wang et al., “ATHENA: An intrusion detection framework for in-vehicle systems,” arXiv preprint, 2025.
14. A. Almalawi et al., “Hybrid cybersecurity for asymmetric threats,” Symmetry, vol. 17, no. 4, 2025.
15. S. Srivastav et al., “HYRIDE: Hybrid and robust intrusion detection approach,” Internet of Things, vol. 30, 2025.
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