Vehicular networks are exposed to various threats resulting from malicious attacks. These threats compromise the security and reliability of communications among road users, thereby jeopardizing road and traffic safety. One of the main vectors of these attacks within vehicular networks is misbehaving vehicles. To address this challenge, we propose deploying a pretrained Large Language Model (LLM)-empowered Misbehavior Detection System (MDS) within an edge-cloud detection framework. Specifically, we fine-tune Mistral-7B, a state-of-the-art LLM, as the edge component to enable real-time detection, whereas a larger LLM deployed in the cloud can conduct a more comprehensive analysis. Our experiments conducted on the extended VeReMi dataset demonstrate Mistral-7B's superior performance, achieving 98\% accuracy compared to other LLMs such as LLAMA2-7B and RoBERTa. Additionally, we investigate the impact of window size on computational costs to optimize deployment efficiency. Leveraging LLMs in MDS shows interesting results in improving the detection of vehicle misbehavior, consequently strengthening vehicular network security to ensure the safety of road users.
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