"VeReMiVNDN: Misbehavior Vehicular Named Data Network "

"Vehicular Named Data Networking (VNDN) has emerged as a promising solution to improvecontent lookup, scalability, and reliability in dynamic vehicular environments. However, the name-centriccommunication model and reliance on cooperative forwarding introduce new security challenges, makingVNDN vulnerable to different attacks, such as forwarding-layer attacks that cannot be fully addressedthrough traditional cryptographic mechanisms. In the meantime, there is still a lack of publicly availableVNDN-specific datasets, which significantly hinders the development, evaluation, and reproducibility ofmisbehavior detection in VNDN solutions. To address the limitation, we introduce a novel, first publiclyavailable dataset designed explicitly for misbehavior detection in VNDN. We generate the dataset usingthe Jubail Industrial City SUMO Traffic (JubST) mobility scenario in SUMO, integrated with VEINSand OMNeT++. The dataset includes four forwarding-layer attacks in VNDN: Interest Flooding, InterestAggregation, Routing Information Flood, and Name Prefix Hijacking. Each of these attacks is simulatedunder varying traffic densities, attacker probabilities, and random seeds, with a lightweight trust-basedlabeling mechanism for ground-truth identification. The resulting dataset offers a standardized, extensible,and reproducible foundation for evaluating VNDN. This work provides a critical benchmarking resource forthe research community and paves the way to assess various security systems in VNDN."