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

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

Available Online: 2025-12-11

Abstract

Abstract

Objective Physical (PHY)-layer authentication exploits channel state information of radio transmitters to detect spoofing attacks, but the accuracy and speed have to be further improved for LLM based smart ocean applications, due to insufficient channel estimation and fast time-varying channels in short-packet communication with limited preamble length. In this paper, we propose an environment perception-aware PHY-layer authentication scheme for LLM edge inference in smart ocean application, which designs a hypothesis testing based multi-mode authentication to evaluate channel state information and packet arrival interval. The application type and the environmental indicators inferred by LLM are exploited in reinforcement learning to optimize the authentication mode and test threshold, enhancing authentication accuracy and speed. Methods The environment perception-aware PHY-layer authentication scheme is proposed for LLM edge inference in maritime wireless networks, which designs a hypothesis testing based multi-mode authentication to evaluate channel state information and packet arrival interval to detect spoofing. Reinforcement learning is utilized to jointly optimize authentication mode and test threshold based on application types and the environmental indicators inferred by the multimodal LLM fed both images and prompts, thus enhancing authentication accuracy. The multi-level policy risk function is formulated to quantify the potential of miss detection, reducing the exploration probability for unsafe policies. A Benna-Fusi synapse model based continual learning mechanism is proposed to obtain multi-scale optimization experiences across multiple maritime scenarios, such as deck and cabin, and replays in the same cases to enhance policy optimization speed. Results and Discussions Simulations are performed based on a legal device and a shipborne server using the maritime channel data collected in the Xiamen Pearl Harbor area against a spoofing attacker at 1.5 m/s that sends false data packets to the server with up to 100 mW. Simulation results show the performance gain over benchmarks, e.g., providing 82.3% lower false alarm rate and 84.2% lower miss detection rate compared with RLPA, due to the use of environmental indicator derived from LLM and the safe exploration mechanism to avoid choosing the risk authentication policy that leads to a decline in miss detection. Conclusions This paper proposes a PHY-layer authentication scheme for LLM-enabled intelligent maritime wireless networks to optimize both the authentication mode and test threshold against spoofing attacks. Based on the environmental indicator derived from LLM, the channel state information, and the packet arrival interval, the safe exploration mechanism in the policy distribution is used to improve authentication accuracy and efficiency. Simulation results show that our proposed scheme reduces 82.3% false alarm rate and 84.2% miss rate 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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