Online Two-Stage Channel-Based Lightweight Authentication Method for Time-Varying Scenarios

Abstract

Physical Layer Authentication (PLA) emerges as a promising security solution, offering efficient identity verification for the Internet of Things (IoT). The advent of 5G/6G technologies has ushered in an era of extensive device connectivity, diverse networks, and complex application scenarios within IoT ecosystems. These advancements necessitate PLA systems that are highly secure, robust, capable of online processing, and adaptable to unknown channel conditions. In this paper, we introduce a novel two-stage PLA framework that synergizes channel prediction with power-delay attributes, ensuring superior performance in mobile and time-varying channel environments. Specifically, our approach employs Sparse Variational Gaussian Processes (SVGP) to accurately model and track real-time channel variations, leveraging historical data for online predictions without incurring significant computational or storage overhead. The second stage of our framework enhances the robustness of the authentication process by incorporating power-delay features, which are inherently resistant to temporal fluctuations, thereby eliminating the need for additional feature extraction in noisy settings. Moreover, our authentication scheme is designed to be distribution-agnostic, utilizing Kernel Density Estimation (KDE) for non-parametric threshold determination in hypothesis testing. Theoretical analysis underpins the generalization capabilities of our proposed method. Simulation results in mobile scenarios reveal that our two-stage PLA framework reduces complexity and significantly improves identity authentication performance, particularly in scenarios with low signal-to-noise ratios.