arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1906.02492cs.CRcs.LGeess.SP

CANet: An Unsupervised Intrusion Detection System for High Dimensional CAN Bus Data

ETAS GmbH, Bosch Group(博世集团ETAS有限公司);Robert Bosch GmbH(罗伯特·博世有限公司)

Markus Hanselmann, Thilo Strauss, Katharina Dormann, Holger Ulmer

英文摘要:

We propose a novel neural network architecture for detecting intrusions on the CAN bus. The Controller Area Network (CAN) 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 detect 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 takes individual CAN messages with different IDs and evaluates them in the moment they are received. This is a significant advancement because messages with different IDs are typically sent at different times and with different frequencies. Our method is evaluated on real and synthetic CAN data. For reproducibility of the method, our synthetic data is publicly available. A comparison with previous machine learning based methods shows that CANet outperforms them by a significant margin.

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