Denial of Service attacks have become a disrupting issue for modern applications. In the context of autonomous vehicles, this problem can affect the users in many ways, ranging from security data breaches to a crash in the car’s system, which prevents the broad availability of these services to society. In recent years, a vast majority of studies have proposed the use of contemporary Machine Learning techniques to assist in the detection of these anomalies. Nevertheless, they still cannot cope with the fast-paced nature of this adversarial attack. In this work, we propose to study a trendy method called Graph Neural Networks to evaluate its effectiveness over these challenges in the vehicular network context, specifically regarding the security message transmission sys- tem, using a publicly available dataset. The measured metrics achieved great performance compared to other traditional classifiers, which emphasizes the robustness of this model and paves the way for future works looking to assess stronger variables and sophisticated scenarios.