Channel Prediction-Based Physical Layer Authentication under Consecutive Spoofing Attacks

Yijia Guo; Junqing Zhang; Yao-Win Peter Hong

cs.CRMar 20, 2026

Channel Prediction-Based Physical Layer Authentication under Consecutive Spoofing Attacks

Authors: Yijia Guo, Junqing Zhang, Yao-Win Peter Hong

Organizations: School of Computer Science and Informatics, University of Liverpool, Liverpool, L69 3DR, United Kingdom · Institute of Communications Engineering, National Tsing Hua University, Hsinchu, Taiwan 300044

Abstract

Wireless networks are highly vulnerable to spoofing attacks, especially when attackers transmit consecutive spoofing packets. Conventional physical layer authentication (PLA) methods have mostly focused on single-packet spoofing attack. However, under consecutive spoofing attacks, they become ineffective due to channel evolution caused by device mobility and channel fading. To address this challenge, we propose a channel prediction-based PLA framework. Specifically, a Transformer-based channel prediction module is employed to predict legitimate CSI measurements during spoofing interval, and the input of channel prediction module is adaptively updated with predicted or observed CSI measurements based on the authentication decision to ensure robustness against sustained spoofing. Simulation results under Rayleigh fading channels demonstrate that the proposed approach achieves low prediction error and significantly higher authentication accuracy than conventional benchmark, maintaining robustness even under extended spoofing attacks.

Figures & tables

Fig. 1: The system model under consecutive spoofing attacks.

Fig. 2: The channel prediction-based PLA system.

Fig. 3: The structure of encoder-decoder Transformer.

Fig. 4: The NMSE of Transformer-based channel prediction module with

N_{\rm head}=4

and

N_{p}=20

evaluated on the simulation test dataset under

T_{\rm rms}=50

ns,

\text{SNR}=20

dB,

v_{0}=0.5

m/s, and

\Delta t=3

ms.

Fig. 5: The authentication accuracy versus

N_{a}

on the simulation test dataset with

T_{\rm rms}=50

ns,

\text{SNR}=20

dB,

v_{0}=1

m/s,

\Delta t=3

ms, and

d_{\rm bm}=24

cm.

Channel State Prediction

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Mar 20, 2026J/K move · Enter open · S save

Model-Driven Learning-Based Physical Layer Authentication for Mobile Wi-Fi Devices

Yijia Guo, Junqing Zhang, Yao-Win Peter Hong, Stefano Tomasin

School of Computer Science and Informatics, University of Liverpool, Liverpool, L69 3DR, United Kingdom · Institute of Communications Engineering, National Tsing Hua University, Hsinchu, Taiwan 300044 · Department of Information Engineering, University of Padova, Padova, Italy

Wireless Communications

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The rise of wireless technologies has made the Internet of Things (IoT) ubiquitous, but the broadcast nature of wireless communications exposes IoT to authentication risks. Physical layer authentication (PLA) offers a promising solution by leveraging unique characteristics of wireless channels. As a common approach in PLA, hypothesis testing yields a theoretically optimal Neyman-Pearson (NP) detector, but its reliance on channel statistics limits its practicality in real-world scenarios. In contrast, deep learning-based PLA approaches are practical but tend to be not optimal. To address these challenges, we proposed a learning-based PLA scheme driven by hypothesis testing and conducted extensive simulations and experimental evaluations using Wi-Fi. Specifically, we incorporated conditional statistical models into the hypothesis testing framework to derive a theoretically optimal NP detector. Building on this, we developed LiteNP-Net, a lightweight neural network driven by the NP detector. Simulation results demonstrated that LiteNP-Net could approach the performance of the NP detector even without prior knowledge of the channel statistics. To further assess its effectiveness in practical environments, we deployed an experimental testbed using Wi-Fi IoT development kits in various real-world scenarios. Experimental results demonstrated that the LiteNP-Net outperformed the conventional correlation-based method as well as state-of-the-art Siamese-based methods.

Abstract

Channel State Prediction

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