Adaptive Security in Mobile Wireless Networks: Machine Learning-Enhanced Continuous Physical Layer Authentication for Dynamic Environments

The widespread adoption of the Internet of Things (IoT) in various industries highlights the urgent need for strong security measures to protect data transmission integrity. This article stresses the importance of continuous physical layer authentication (PLA) in wireless networks with mobile nodes, using machine learning (ML) to enhance security. By analyzing unique characteristics in wireless communication channels, recipient nodes can accurately verify sender node authenticity, bolstering overall communication system security. The proposed method addresses challenges in adapting PLA to dynamic mobile scenarios, particularly dealing with channel variations caused by transmitter mobility. The article evaluates the authentication accuracy a ML-based technique. Overall, this research contributes to advancing wireless network security by integrating ML-based continuous PLA to ensure IoT communication reliability and integrity in dynamic mobile environments.