# Recent Advances in the Internet of Medical Things (IoMT) Systems Security

arXiv: https://arxiv.org/abs/2302.04439 · HTML: https://mlsanity.com/paper/2302.04439/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2023.

## Built on by (most cited first, 4 of 4)

- [Swarm Learning: A Survey of Concepts, Applications, and Trends](https://mlsanity.com/paper/2405.00556/) (2024)
- [A Robust Cross-Domain IDS using BiGRU-LSTM-Attention for Medical and Industrial IoT Security](https://mlsanity.com/paper/2508.12470/) (2025)
- [IoT Federated Blockchain Learning at the Edge](https://mlsanity.com/paper/2304.03006/) (2023)
- [PP-Net: A Hybrid Physical-Prior Neural Network for Scattered Light Removal in Biomedical Images on Embedded Devices](https://mlsanity.com/paper/2609.26474/) (2026)

## Abstract (arXiv)

The rapid evolutions in micro-computing, mini-hardware manufacturing, and machine to machine (M2M) communications have enabled novel Internet of Things (IoT) solutions to reshape many networking applications. Healthcare systems are among these applications that have been revolutionized with IoT, introducing an IoT branch known as the Internet of Medical Things (IoMT) systems. IoMT systems allow remote monitoring of patients with chronic diseases. Thus, it can provide timely patients' diagnostic that can save their life in case of emergencies. However, security in these critical systems is a major challenge facing their wide utilization. In this paper, we present state-of-the-art techniques to secure IoMT systems' data during collection, transmission, and storage. We comprehensively overview IoMT systems' potential attacks, including physical and network attacks. Our findings reveal that most security techniques do not consider various types of attacks. Hence, we propose a security framework that combines several security techniques. The framework covers IoMT security requirements and can mitigate most of its known attacks.