This study evaluates a lightweight authentication protocol for medical IoT systems, identifying vulnerabilities in encryption and key exchange. It proposes enhancements like ECIES and digital signatures, along with improved resource management and insider threat mitigation measures. These aim to strengthen security and protect medical data. Future research should explore quantum-resistant cryptography and AI-driven adaptive security.

This study evaluates a lightweight authentication protocol for IoMT systems, revealing vulnerabilities like node cloning and insider threats. It proposes enhancements including PUFs, homomorphic encryption, and RBAC/ABAC. Optimized session management and lightweight cryptography are also suggested to improve security and resource use. Future research should explore quantum-resistant cryptography and AI-based adaptive security policies for enhanced resilience against evolving threats.