Physical Layer Security Game for Large Language Model-Based Inference in the Maritime Network

CHEN Haoyu; XIAO Liang; XU Xiaoyu; LI Jieling; WANG Zicheng; LIU Huanhuan; CHEN Hongyi

doi:10.11999/JEIT251269

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CHEN Haoyu, XIAO Liang, XU Xiaoyu, LI Jieling, WANG Zicheng, LIU Huanhuan, CHEN Hongyi. Physical Layer Security Game for Large Language Model-Based Inference in the Maritime Network[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251269

Citation:

CHEN Haoyu, XIAO Liang, XU Xiaoyu, LI Jieling, WANG Zicheng, LIU Huanhuan, CHEN Hongyi. Physical Layer Security Game for Large Language Model-Based Inference in the Maritime Network[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251269

Citation:

CHEN Haoyu, XIAO Liang, XU Xiaoyu, LI Jieling, WANG Zicheng, LIU Huanhuan, CHEN Hongyi. Physical Layer Security Game for Large Language Model-Based Inference in the Maritime Network[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251269

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Physical Layer Security Game for Large Language Model-Based Inference in the Maritime Network

doi: 10.11999/JEIT251269 cstr: 32379.14.JEIT251269

1.
Institute of Artificial Intelligence, Xiamen University, Xiamen 361105, China
2.
School of Informatics, Xiamen University, Xiamen 361105, China

Funds: National Natural Science Foundation of China (U25A20388), Fundamental Research Funds for the Central Universities (20720250036), National Key Research and Development Program of China (2023YFB3107603)

Accepted Date: 2026-02-10
Rev Recd Date: 2026-02-10

Available Online: 2026-03-04

Abstract

Abstract

Objective The physical-layer security game reveals the interaction between user equipment (UE) and attackers, and provides performance bounds of anti-jamming transmission and physical-layer authentication schemes based on the equilibriums. However, existing game models overlook smart attackers that send jamming or spoofing signals, fail to account for the maritime wireless channels affected by evaporation ducts and sea wave fluctuations, and are difficult to evaluate the performance of large language models (LLMs)-based inference, such as the vessel traffic monitoring. Methods The anti-jamming maritime communication game for LLM inference is formulated, where the jammer first selects the jamming power and channel to reduce the signal-to-interference-plus-noise ratio at the server with less jamming cost, and the UEs then choose transmit power, channel, LLM sparsity ratio and control center to send sensing data (e.g., images, temperature, and humidity) to enhance the inference accuracy with less latency. The physical-layer authentication game for maritime wireless networks with LLM inference is further formulated. The spoofing attacker first selects the number of spoofing packets to degrade authentication accuracy with less cost. The control center then selects the fast authentication mode based on channel state or the safe authentication mode based on the received signal strength and the arrival interval of the packet from multiple ambient transmitters, and the test threshold to increase accuracy with less cost. Results and Discussions Based on the Stackelberg equilibrium (SE) under the LLM with 7 billion parameters, the performance bounds of the reinforcement learning (RL)-based anti-jamming inference scheme are provided to reveal the impact of evaporation duct height, wave height, maximum sparsity ratio of LLM and the quantization level on inference accuracy and latency. In addition, the performance bounds of the RL-based maritime spoofing detection scheme are provided based on the SE of the physical-layer authentication game to show the impact of the maximum number of spoofing packets on the authentication accuracy. Simulations are carried out based on the five UEs with the antenna height of 3 meters offloading the image, temperature and humidity using the transmit power up to 200 mW at 5.8 GHz with a bandwidth of 20 MHz to five control centers with antenna heights of 6 m. The jammer applies Deep Q-Network to choose the jamming power with a maximum transmit power of 200 mW for each 5.8 GHz channel, and the spoofing attacker applies the Deep Q-Network to select the number of spoofing packets up to 100. The results show that the inference accuracy and latency of the RL-based anti-jamming maritime communication scheme for LLM inference converge to the performance bounds with gaps of less than 0.6% after 2500 time slots. In addition, the RL-based authentication scheme converges after 1000 time slots with the gap of less than 1.6%. Conclusions In this paper, we have formulated the maritime physical-layer security game for LLM inference, addressing scenarios such as anti-jamming sensing data transmission and spoofing detection, aiming at investigating how UEs determine transmit power and channel, and how the control center selects authentication modes and test thresholds to enhance the physical-layer security mechanisms. The attacker chooses attack modes and parameters to degrade the inference accuracy, increase latency, and even cause denial-of-service. Based on the SE and the conditions, the performance bounds of the inference accuracy increase with the maximum transmit power and linearly decrease with the sparsity ratio. Furthermore, the impact of the maximum number of spoofing packets on the inference accuracy is provided. Simulation results show that the RL-based maritime physical-layer security schemes converge to the performance bounds, thereby validating the accuracy and effectiveness of the game model.
- Game theory,
- Anti-jamming,
- Physical-layer authentication,
- Maritime communication,
- Large language model

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References(23)

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