CANet: An Unsupervised Intrusion Detection System for High Dimensional CAN Bus Data
Markus Hanselmann, Thilo Strauss, Katharina Dormann, Holger Ulmer
Abstract
We propose a neural network architecture for detecting intrusions on the controller area network (CAN). The latter is the standard communication method between the electronic control units (ECUs) of automobiles. However, CAN lacks security mechanisms and it has recently been shown that it can be attacked remotely. Hence, it is desirable to monitor CAN traffic to detect intrusions. In order to find both, known and unknown intrusion scenarios, we consider a novel unsupervised learning approach which we call CANet. To our knowledge, this is the first deep learning based intrusion detection system (IDS) that naturally handles the data structure of the high dimensional CAN bus, where different message types are sent at different times. Our method is evaluated on real and synthetic CAN data. A comparison with previous machine learning based methods shows that CANet outperforms them by a significant margin. For reproducibility of the method, our synthetic data is publicly available.