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Channel Prediction-Based Physical Layer Authentication under Consecutive Spoofing Attacks

Yijia Guo, Junqing Zhang, Yao-Win Peter Hong

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.

Channel Prediction-Based Physical Layer Authentication under Consecutive Spoofing Attacks

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.
Paper Structure (24 sections, 31 equations, 5 figures, 1 algorithm)

This paper contains 24 sections, 31 equations, 5 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model and Problem Statement
  3. System Model
  4. Problem Statement
  5. Proposed Method
  6. Overall Framework
  7. Training Stage
  8. Transformer
  9. Position Selection
  10. Test Stage
  11. Channel Prediction
  12. Correlation-based Authentication
  13. Input Sequence Update
  14. Performance Metrics and Benchmark
  15. Performance Metrics
  16. ...and 9 more sections

Figures (5)

  • Figure 1: The system model under consecutive spoofing attacks.
  • Figure 2: The channel prediction-based PLA system.
  • Figure 3: The structure of encoder-decoder Transformer.
  • Figure 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.
  • Figure 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.