PHY-Layer Authentication With Multiple Landmarks With Reduced Overhead

Physical (PHY)-layer authentication systems can exploit channel state information of radio transmitters to detect spoofing attacks in wireless networks. The use of multiple landmarks each with multiple antennas enhances the spatial resolution of radio transmitters, and thus improves the spoofing detection accuracy of PHY-layer authentication. Unlike most existing PHY-layer authentication schemes that apply hypothesis tests and rely on the known radio channel model, we propose a logistic regression-based authentication to remove the assumption on the known channel model, and thus be applicable to more generic wireless networks. The Frank-Wolfe algorithm is used to estimate the parameters of the logistic regression model, in which the convex problem under a ℓ 1 -norm constraint is solved for weight sparsity to avoid over-fitting in the learning process. We design a distributed Frank-Wolfe-based PHY-layer authentication to further reduce the communication overhead between the landmarks and the security agent. Then, we construct an incremental aggregated gradient-based scheme to provide online authentication with a higher accuracy and lower computation overhead. Simulation and experimental results validate the accuracy of the proposed authentication schemes, and show the reduced communication and computation overheads.