Wireless User Authentication Based on KLT and Gaussian Mixture Model

Physical (PHY)-layer security has received considerable interest as a way to safeguard data confidentiality and achieve security and privacy in wireless networks. Authentication between two devices is a challenging problem. In this paper, a machine learning algorithm is proposed to detect and identify rogue transmitters relying on a low-dimensional channel feature vector that is obtained by the Karhunen-Loeve transform (KLT). Specifically, a Linde-Buzo-Gray algorithm is designed for improving the reliability and robustness of the proposed scheme, where a Gaussian Mixture Model (GMM) is employed to learn and track the changes of physical layer properties. Simulation results demonstrate that the proposed authentication scheme achieves a higher spoofing detection rate compared to other existing methods.