The increase in the negativities experienced in traffic mobility is one of the recent problems of crowded population. With the acceleration of developments in wireless communication in recent years, it is seen that some efforts have been made to prevent traffic jams and accidents in big cities. Vehicular Ad Hoc Networks (VANET) are networks developed for purposes, such as regulating traffic flow and saving lives. HCRL-VANET, which was developed to detect and prevent security measures and threats in Inter-Vehicle Networks, monitors the behavior of vehicles and infrastructure devices and is evaluated accordingly. In this study, intrusion detection performance was evaluated using conventional machine learning and deep learning methods on the dataset obtained from data traffic circulating in HCRL-VANET networks. In the analysis, the DNN method has reached the highest intrusion detection rate of 94.31% for accuracy.