A hybrid blockchain and AI-based approach for attack protection to secure internet of medical things networks
Hadeel Alsolai, Fahd N. Al‐Wesabi, Ali Mohammed Al-Sharafi, Asma Alshuhail, Abdulbasit A. Darem, Bandar M. Alghamdi, Fouad Shoie Alallah, Abdulrahman Alzahrani · Alexandria Engineering Journal · 2025
The Internet of Medical Things (IoMT) has revolutionized healthcare by enabling real-time monitoring, remote diagnosis, and seamless data exchange among medical devices and providers. However, the interconnected and distributed nature of IoMT systems exposes them to significant cybersecurity threats, including data breaches and unauthorized access. This study aims to develop a secure and scalable framework to safeguard IoMT environments using blockchain technology, federated learning, and dynamic consensus algorithms. The proposed approach integrates artificial intelligence to detect threats and employs federated learning to maintain patient privacy while training models collaboratively. Experimental evaluation of the framework shows improved data security, reduced response times, and enhanced system trustworthiness. The results suggest that the integration of blockchain and AI-driven mechanisms offers a robust solution to IoMT cybersecurity challenges, paving the way for more reliable and efficient healthcare systems.