Channel Prediction-Based Physical Layer Authentication under Consecutive Spoofing Attacks
arXiv SecurityArchived Mar 23, 2026✓ Full text saved
arXiv:2603.19962v1 Announce Type: new 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 framew
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Computer Science > Cryptography and Security
[Submitted on 20 Mar 2026]
Channel Prediction-Based Physical Layer Authentication under Consecutive Spoofing Attacks
Yijia Guo, Junqing Zhang, Yao-Win Peter Hong
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.
Subjects: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
Cite as: arXiv:2603.19962 [cs.CR]
(or arXiv:2603.19962v1 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2603.19962
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Submission history
From: Yijia Guo [view email]
[v1] Fri, 20 Mar 2026 14:03:46 UTC (253 KB)
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