The aim is to evaluate different deep learning models (e.g., transformers, GNNs, BiLSTMs, LSTMs, MLPs, etc) to detect misbehavior in Intelligent Transportation Systems and their applications. We currently have a large dataset of platooning mobility information that includes network and other attacks. We also have an in-city dataset that includes vehicles, pedestrians, etc. Our goal is to investigate the performance of different machine learning architectures in detecting attacks against our datasets and device solutions for safe, reliable, and secure ITS.