Modern connected vehicles are composed of multiple electronic control units (ECUs) holding sensors, actuators but also wired and wireless connection interfaces, all communicating over shared internal communication buses. The cyber-physical architecture based on this ECU network has been proven vulnerable to multiple types of attacks leveraging remote, direct and indirect physical access. Attacks initiated from these access vectors go through the internal communication buses and spread over the whole network of ECUs. For this reason it is important to detect, and if possible to mitigate, attacks on the internal buses of the vehicle.In this article, a novel intrusion detection system is developed to monitor vehicle state from information collected on internal buses. Based on supervised machine learning techniques, a normal behavior is learned and used as a reference to detect deviations. The principle is to learn how to predict the next state of the vehicle based on information and sensor values sent over communication buses. Experimental validation is conducted using data collected from different drivers. Results show that the approach is able to learn the nominal behavior with high accuracy for a single driver as well as for a set of different drivers. Results also demonstrate its ability to predict attacks with low false negative rate. This motivates the approach to be used for indirect and remote attacks intrusion detection as well as for safety purposes to detect sensor failures, lost connection with the sensor, etc.