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

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 Maritime Networks[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 Maritime Networks[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 Maritime Networks[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 Maritime Networks

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

Received Date: 2025-12-01
Accepted Date: 2026-02-10
Rev Recd Date: 2026-02-10

Available Online: 2026-03-04

Abstract

Abstract

Objective The physical-layer security game is used to reveal the interaction between User Equipment (UE) and attackers, and to provide performance bounds for anti-jamming transmission and physical-layer authentication schemes based on the equilibria. However, existing game models overlook intelligent attackers that transmit jamming or spoofing signals, do not account for maritime wireless channels affected by evaporation ducts and sea wave fluctuations, and do not readily support performance evaluation of Large Language Model (LLM)-based inference tasks such as vessel traffic monitoring. Methods An anti-jamming maritime communication game for LLM inference is formulated. In this game, the jammer first selects the jamming power and channel to reduce the signal-to-interference-plus-noise ratio at the server at lower jamming cost. The UEs then select the transmit power, channel, LLM sparsity ratio, and control center to send sensing data, such as images, temperature, and humidity, so that inference accuracy is improved with lower latency. A 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 reduce authentication accuracy at lower cost. The control center then selects either the fast authentication mode based on channel state or the safe authentication mode based on the received signal strength and packet arrival interval from multiple ambient transmitters, as well as the test threshold, to improve accuracy at lower cost. Results and Discussions Based on the Stackelberg Equilibrium (SE) under an LLM with 7 billion parameters, the performance bounds of the Reinforcement Learning (RL)-based anti-jamming inference scheme are derived to show the effects of evaporation duct height, sea wave height, maximum LLM sparsity ratio, and quantization level on inference accuracy and latency. In addition, the performance bounds of the RL-based maritime spoofing detection scheme are derived from the SE of the physical-layer authentication game to show the effect of the maximum number of spoofing packets on authentication accuracy. Simulations are conducted for five UEs with antenna heights of 3 m, which offload images, temperature, and humidity data using transmit power of 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 uses a Deep Q-Network to select the jamming power, with a maximum transmit power of 200 mW for each 5.8 GHz channel. The spoofing attacker uses a 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 2,500 time slots. In addition, the RL-based authentication scheme converges after 1,000 time slots, with a gap of less than 1.6%. Conclusions In this paper, a maritime physical-layer security game for LLM inference is formulated to address scenarios including anti-jamming sensing data transmission and spoofing detection. The aim is to investigate how UEs determine the transmit power and channel, and how the control center selects authentication modes and test thresholds to improve physical-layer security. The attacker selects attack modes and parameters to reduce inference accuracy, increase latency, and even cause denial of service. Based on the SE and the related conditions, the performance bounds show that inference accuracy increases with the maximum transmit power and decreases linearly with the sparsity ratio. Furthermore, the effect of the maximum number of spoofing packets on inference accuracy is analyzed. Simulation results show that the RL-based maritime physical-layer security schemes converge to the performance bounds, which validates 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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