Enhancing Physical Layer Authentication in Mobile WiFi Environments Using Sliding Window and Deep Learning

The convenience of wireless communication has led to its widespread adoption across various application scenarios. However, existing Physical Layer Authentication (PLA) schemes still have limitations in terms of adaptability. This paper introduces a novel method based on key-less channel-based authentication framework designed to enhance PLA performance under different mobility patterns. Our method combines the sliding window with a model based on a siamese neural network, enabling a fully connected neural network classifier to effectively distinguish between legitimate transmitters and potential attackers, achieving an accuracy of 97.91%. Experiments conducted in various environments demonstrate the robustness and high performance of the proposed method, making it well-suited for deployment in time-varying environments with high-security demands and resource constraints.