Channel Prediction-Based Physical Layer Authentication under Consecutive Spoofing Attacks
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866917354797006848 |
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| author | Guo, Yijia Zhang, Junqing Hong, Yao-Win Peter |
| author_facet | Guo, Yijia Zhang, Junqing Hong, Yao-Win Peter |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_19962 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Channel Prediction-Based Physical Layer Authentication under Consecutive Spoofing Attacks Guo, Yijia Zhang, Junqing Hong, Yao-Win Peter Cryptography and Security Machine Learning 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. |
| title | Channel Prediction-Based Physical Layer Authentication under Consecutive Spoofing Attacks |
| topic | Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2603.19962 |