DAdAE: Domain Adversarial Autoencoder Based In-Vehicle CAN Anomaly Detection

Abstract

Modern vehicles have multiple electronic control units (ECUs) that are connected as part of a complex cyber-physical system (CPS). The controller area network (CAN) is a well-known communication protocol that connects these ECUs because of its reliability and efficiency. However, adversaries can easily inject abnormal messages into the CAN bus remotely to affect vehicle driving safety. Existing anomaly detection methods only focus on specific vehicle models and have a limited range of applications across different vehicles. To address this challenge, this paper proposes a Domain Adversarial training-based AutoEncoder (DAdAE) for unsupervised CAN anomaly detection. The advantages of our approach are: 1) detect variant attack scenarios on different car models 2) does not require labeled data 3) works well even with a limited dataset. The effectiveness of the proposed model is evaluated on the survival dataset, and the experiment results show that the DAdAE model improves the overall f1 score significantly, compared to other unsupervised models.