As Vehicular Ad Hoc Networks (VANETs) and the volume of vehicles on the streets continue to rise, so does the security threats to devices within these networks. A VANET is a system that is comprised of moving or stationary vehicles connected by a wireless network. They are crucial in enhancing safety and comfort for drivers in vehicular settings. To function optimally, vehicles within VANETs must interact and communicate transmitting crucial and vital information. Ensuring the legitimacy and safety of these messages, and preventing malicious activity, becomes essential. This can be achieved by monitoring the messages in real time in order to detect any malicious activity. In this study, we propose a monitoring system that harnesses the power of machine learning to identify any malicious activity emanating from vehicles in VANETs.