Deep Learning for In-Vehicle Intrusion Detection System

Modern and future vehicles are complex cyber-physical systems. The connection to their outside environment raises many security problems that impact our safety directly. In this work, we propose a Deep CAN intrusion detection system framework. We introduce a multivariate time series representation for asynchronous CAN data which enhances the temporal modelling of deep learning architectures for anomaly detection. We study different deep learning tasks (supervised/unsupervised) and compare several architectures, in order to design an in-vehicle intrusion detection system that fits in-vehicle computational constraints. We conduct experiments with many types of attacks on an in-vehicle CAN using SynCAn Dataset.