State of the Art and First Synthetic Dataset for Misbehavior Detection with Collective Perception in [C-]V2X Networks

As vehicular networks evolve into fully connected intelligent transportation systems, Collective Perception (CP) enables vehicles and infrastructure to share sensor data for enhanced situational awareness. However, this cooperation introduces new attack surfaces, making misbehavior detection essential for securing [C-]V2X communications. Despite advances in machine learning, no public dataset exists for misbehavior detection in CP scenarios. This work introduces the first synthetic dataset tailored for this purpose and applies a spatio-temporal graph neural network (ST-GNN) model to it. The paper also surveys state-of-the-art approaches to misbehavior detection in CP-enabled V2X and discusses integration into open-source cooperative perception frameworks. By addressing both data and model gaps, this study supports practical and secure deployment of CP in future V2X systems. To the best of our knowledge, this is the first publicly available dataset and evaluation pipeline supporting misbehavior detection over standardized CPMs in [C-]V2X scenarios.