发表机构
Clemson University; University of Alabama（克莱姆森大学; 阿拉巴马大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出基于小型语言模型（SLM）的GNSS欺骗检测框架，通过对比多源驾驶状态的结构化叙事检测攻击，性能接近大模型且资源消耗更低，适用于车载平台。
AI 中文摘要
自动驾驶车辆（AVs）依赖可靠的全球导航卫星系统（GNSS）定位，但欺骗性GNSS信号可诱导出看似合理却错误的车辆状态。本研究开发了一种基于小型语言模型（SLM）的框架，通过对比从GNSS及其他传感源独立推导的车辆行为，实现对GNSS欺骗攻击的检测与分类。该框架将来自GNSS和其他传感源的独立驾驶状态转换为结构化语义叙事，输入SLM以完成欺骗检测与攻击分类。将该基于SLM的框架与在相同训练数据上微调的大型语言模型（LLMs）进行性能对比，在同一测试集上评估，评估涵盖五类情况：无攻击、过冲攻击、停车攻击、逐段转弯攻击、错转弯攻击。还使用在美国南卡罗来纳州克莱姆森收集的地理未见过的现场数据对该框架进行评估。实验结果表明，所评估的SLMs取得与LLMs相近的性能，平均准确率达96.99%，精确率为99.05%，召回率为95.59%，F1值为97.18%。在计算效率与资源利用方面，SLMs相比LLMs具有优势，在微调与推理阶段均需要更低的推理延迟和更少的GPU内存。在地理不同位置收集的现场数据的评估进一步证明了其有效性。所提出的框架可实时检测和分类GNSS欺骗攻击，同时所需计算与内存资源相对较低，因此适用于资源受限的车载计算平台部署。
英文摘要
Autonomous vehicles (AVs) depend on reliable Global Navigation Satellite System (GNSS) positioning. However, spoofed GNSS signals can induce plausible but incorrect vehicle states. This study develops a small language model (SLM)-based framework for detecting and classifying GNSS spoofing attacks by comparing vehicle behaviors independently derived from GNSS and other sensing sources. The framework converts independent driving states from GNSS and other sensing sources into structured semantic narratives that are provided to an SLM for spoofing detection and attack classification. The performance of the SLM-based framework is compared with large language models (LLMs) fine-tuned on identical training data and evaluated on the same test set. The evaluation considers five classes: no attack, overshoot attack, stopped attack, turn-by-turn attack, and wrong-turn attack. The framework is also evaluated with geographically unseen field data collected in Clemson, South Carolina, United States. Experimental results indicate that the evaluated SLMs achieve performance similar to the LLMs, achieving an average accuracy of 96.99%, precision of 99.05%, recall of 95.59%, and F1-score of 97.18%. In terms of computational efficiency and resource utilization, the SLMs demonstrate advantages over the LLMs by requiring lower inference latency and less GPU memory during both fine-tuning and inference. Evaluation using field data collected in a geographically distinct location further demonstrated its efficacy. The presented framework can detect and classify GNSS spoofing attacks in real-time while requiring relatively low computational and memory resources, and is therefore suitable for deployment on resource-constrained vehicular computing platforms.
CommentsThis work has been submitted to the Transportation Research Record: Journal of the Transportation Research Board for possible publication
