Malicious attacks on vehicular networks pose a serious threat to road safety as well as communication reliability. A major source of these threats stems from misbehaving vehicles within the network. To address this challenge, we propose a Large Language Model (LLM)-empowered Misbehavior Detection System (MDS) within an edge-cloud detection framework. Specifically, we fine-tune Mistral-7B, a compact and high-performing LLM, to detect misbehavior based on Basic Safety Messages (BSM) sequences as the edge component for real-time detection, while a larger LLM deployed in the cloud validates and reinforces the edge model's detection through a more comprehensive analysis. By updating only 0.012% of the model parameters, our model, which we named MistralBSM, achieves 98% accuracy in binary classification and 96% in multiclass classification on a selected set of attacks from VeReMi dataset, outperforming LLAMA2-7B and RoBERTa. Our results validate the potential of LLMs in MDS, showing a significant promise in strengthening vehicular network security to better ensure the safety of road users.

翻译：针对车载网络的恶意攻击对道路安全和通信可靠性构成严重威胁，这些威胁主要源于网络中的异常行为车辆。为应对这一挑战，我们提出了一种基于边缘云检测框架的大型语言模型（LLM）驱动的异常行为检测系统（MDS）。具体而言，我们微调了紧凑且高性能的LLM模型Mistral-7B，使其能够基于基本安全消息（BSM）序列进行异常行为检测，作为实时检测的边缘组件；同时，在云端部署的更大规模LLM通过更全面的分析验证并增强边缘模型的检测能力。通过仅更新0.012%的模型参数，我们提出的MistralBSM模型在VeReMi数据集选取的攻击子集上实现了98%的二元分类准确率和96%的多类分类准确率，性能优于LLAMA2-7B和RoBERTa。我们的结果验证了LLM在MDS中的应用潜力，展现了其在强化车载网络安全以更好保障道路使用者安全方面的显著前景。