LLM-Enhanced Maritime UAV Routing Algorithm Against Gray-Hole Attacks

LI Jie-ling; XIAO Liang; WANG Peng-cheng; LEI Yan; CHEN Qiao-xin; WANG Cheng-yao

doi:10.12263/DZXB.20250878

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LLM-Enhanced Maritime UAV Routing Algorithm Against Gray-Hole Attacks

PAPERS | 更新时间：2026-04-24

- LLM-Enhanced Maritime UAV Routing Algorithm Against Gray-Hole Attacks
- ACTA ELECTRONICA SINICA Vol. 53, Issue 12, Pages: 4474-4484(2025)
- 作者机构：
  
  厦门大学信息学院，福建厦门 361102
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(U21A20444);Fundamental Research Funds for the Central Universities(20720250036);National Key Research and Development Program of China(2023YFB3107603)
- DOI：10.12263/DZXB.20250878
  CLC： TP393;
- Received：25 November 2025，
  
  Accepted：18 December 2025，
  
  Published：25 December 2025
- 稿件说明：
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李杰铃, 肖亮, 王鹏程, 等. 大语言模型增强的抗灰洞攻击海域无人机路由算法[J]. 电子学报, 2025, 53(12): 4474-4484.

LI Jie-ling, XIAO Liang, WANG Peng-cheng, et al. LLM-Enhanced Maritime UAV Routing Algorithm Against Gray-Hole Attacks[J]. Acta Electronica Sinica, 2025, 53(12): 4474-4484.
李杰铃, 肖亮, 王鹏程, 等. 大语言模型增强的抗灰洞攻击海域无人机路由算法[J]. 电子学报, 2025, 53(12): 4474-4484. DOI：10.12263/DZXB.20250878

LI Jie-ling, XIAO Liang, WANG Peng-cheng, et al. LLM-Enhanced Maritime UAV Routing Algorithm Against Gray-Hole Attacks[J]. Acta Electronica Sinica, 2025, 53(12): 4474-4484. DOI：10.12263/DZXB.20250878

摘要

无人机路由实现图像、音频和位置等多模态数据至部署大语言模型的船载目的节点的高效传输，支撑目标搜索等推断任务，适用于环境监测和搜索救援等海域业务.然而，在恶劣海域信道条件下，无人机网络拓扑快速变化，路由稳定性显著下降.同时，灰洞攻击选择性丢弃数据包，导致感知数据传输的丢包概率和时延大幅增加，甚至引发推断失败.为此，本文提出大语言模型增强的抗灰洞攻击海域无人机路由算法，根据大语言模型所推断出的环境特征以及相邻无人机成功转发的数据包数，建立路由信任体系，采用强化学习优化下一跳无人机和发射功率.面向海域业务服务质量需求，结合相邻无人机信任度，设计路由策略分布函数，适配无人机群的网络拓扑和信道变化，快速恢复中断路由.针对海上节点分布稀疏及信道快速变化导致的反馈丢失问题，在路由经验回放中引入反馈恢复机制，提升路由稳定性.搭建海域无人机路由系统，在船载目的节点部署参数量为70亿的大模型LLaVA-1.5，以图像和相邻无人机位置等信息为输入，识别环境特征并将结果反馈给无人机，增强路由策略优化.基于厦门欧厝海域实测信道数据，构建30架无人机防御具有不同丢包概率灰洞攻击的结果表明，所提算法可提升72.8%的数据包到达率，降低75.1%的端到端时延和64.7%的能耗，支撑海域大语言模型任务.

Abstract

Unmanned aerial vehicle (UAV) routing enables the efficient transmission of multimodal data such as images

audio and location to the shipborne destination node equipped with a large language model (LLM) to support inference tasks including target search

which are applicable to maritime applications such as environmental monitoring and search and rescue. However

UAV network topologies change rapidly under harsh maritime channel conditions

resulting in significant degradation of routing stability. Meanwhile

gray-hole attacks selectively discard packets

leading to substantial increases in packet loss rate and transmission latency

and even causing inference failures. To address these challenges

this paper proposes an LLM-enhanced maritime UAV routing algorithm against gray-hole attacks that exploits the environment feature inferred by the LLM and the number of packets successfully forwarded by neighboring UAVs to construct a routing trust framework and applies reinforcement learning to jointly optimize the next hop UAV and the transmit power. The routing policy distribution function is formulated based on the quality-of-service requirements and the trust levels of the neighboring UAV

enabling rapid self-healing in response to dynamic network topologies and channel variations. To address feedback loss caused by sparse node distribution and rapidly varying channels in maritime environments

a feedback recovery mechanism is incorporated into routing experience replay to enhance routing stability. We develop a maritime UAV routing system

with the shipborne as the destination hosting a 7-billion-parameter LLaVA-1.5 model. Taking the captured images and one-hop neighbor information such as location as input

this model infers environment features and feeds the results back to UAVs to enhance the routing policy optimization. Based on measured channel data from the Oucuo sea area in Xiamen

a simulation scenario is constructed with 30 UAVs under gray-hole attacks with different packet loss probabilities. The results show that the proposed algorithm improves 72.8% packet delivery ratio

reduces 75.1% end-to-end latency and 64.7% energy consumption

and effectively supports LLM-driven maritime applications.

关键词

Keywords

references

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