Data-Driven Extraction of Vehicle States From CAN Bus Traffic for Cyberprotection and Safety

Abstract

This article develops a data -driven, semisupervised approach to learn physical relationships of controller area network (CAN) signals from only a limited set of CAN packets. These mappings are then used to develop a hidden Markov model (HMM) of the driver's actions upon which transaction analysis is performed to optimize the real-time identification of the states. The proposed approach builds an image from the CAN data, then trains a convolutional neural network (CNN) to give emission probabilities to predicts drivers's actions.