Physical Layer Authentication for Large Language Models in Maritime Communications

CHEN Qiaoxin; XIAO Liang; WANG Pengcheng; LI Jieling; YAO Jinqing; XU Xiaoyu

doi:10.11999/JEIT250804

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CHEN Qiaoxin, XIAO Liang, WANG Pengcheng, LI Jieling, YAO Jinqing, XU Xiaoyu. Physical Layer Authentication for Large Language Models in Maritime Communications[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250804

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CHEN Qiaoxin, XIAO Liang, WANG Pengcheng, LI Jieling, YAO Jinqing, XU Xiaoyu. Physical Layer Authentication for Large Language Models in Maritime Communications[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250804

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Physical Layer Authentication for Large Language Models in Maritime Communications

doi: 10.11999/JEIT250804 cstr: 32379.14.JEIT250804

School of Informatics, Xiamen University, Fujian, Xiamen 361102, China

Funds: The National Natural Science Foundation of China (U21A20444), The Fundamental Research Funds for the Central Universities (20720250036), The National Key Research and Development Program of China (2023YFB3107603)

Received Date: 2025-08-27
Accepted Date: 2025-12-06
Rev Recd Date: 2025-12-05

Available Online: 2025-12-11

Abstract

Abstract

Objective PHYsical (PHY)-layer authentication exploits channel state information to detect spoofing attacks. However, for smart ocean applications supported by Large Language Models (LLMs), authentication accuracy and speed remain limited because of insufficient channel estimation and rapidly time-varying channels in short-packet communications with constrained preamble length. An environment perception-aware PHY-layer authentication scheme is therefore proposed for LLM edge inference in maritime applications. A hypothesis-testing-based multi-mode authentication framework is designed to evaluate channel state information and packet arrival interval. Application types and environmental indicators inferred by the LLM are used in reinforcement learning to optimize the authentication mode and test threshold, thereby improving authentication accuracy and speed. Methods An environment perception-aware PHY-layer authentication scheme is developed for LLM edge inference in maritime wireless networks. Hypothesis-testing-based multi-mode authentication is used to jointly evaluate channel state information and packet arrival interval for spoofing detection. Reinforcement learning is adopted to optimize the authentication mode and test threshold according to application types and environmental indicators inferred by a multimodal LLM fed with images and prompts. A multi-level policy risk function is formulated to quantify miss-detection risk and to reduce exploration probability for unsafe policies. A Benna-Fusi synapse-based continual learning mechanism is proposed to obtain multi-scale optimization experience across multiple maritime scenarios, such as deck and cabin environments, and to replay identical cases to accelerate policy optimization. Results and Discussions Simulations are conducted using four legal devices and a shipborne server with maritime channel data collected in the Xiamen Pearl Harbor area. A spoofing attacker moving at 1.5 m/s transmits false data packets to the server with a maximum power of 100 mW. The results demonstrate clear performance gains over benchmark methods. Compared with RLPA, the proposed scheme achieves an 84.2% reduction in false alarm rate and an 82.3% reduction in miss-detection rate. These gains are attributed to the use of LLM-derived environmental indicators and a safe exploration mechanism that avoids high-risk authentication policies leading to increased miss detection. Conclusions A PHY-layer authentication scheme is proposed for LLM-enabled intelligent maritime wireless networks, in which both the authentication mode and test threshold are optimized to counter spoofing attacks. By jointly using LLM-derived environmental indicators, channel state information, and packet arrival interval, a safe exploration mechanism is applied to improve authentication accuracy and efficiency. Simulation results confirm that the proposed scheme reduces the false alarm rate by 84.2% and the miss-detection rate by 82.3% compared with the benchmark RLPA.
- Physical layer authentication,
- Large language model,
- Short packet communications,
- Maritime communications,
- Reinforcement learning

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References(24)

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