The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
CANet: An Unsupervised Intrusion Detection System for High Dimensional CAN Bus Data
Open accessAbstract
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.
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Cites
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“With the advances in deep learning in the recent years [4]–[6] new tools are becoming available that have the potential of detecting unknown attacks.”
BackgroundFast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
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Convergence Results for Neural Networks via Electrodynamics
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Ensemble Adversarial Training: Attacks and Defenses
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Hybrid speech recognition with Deep Bidirectional LSTM
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Metadata
- Authors
- Hanselmann, M., Strauss, T., Dormann, K., & Ulmer, H.
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 58194-58205
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2948384692
- Cited by
- 268
- References
- 30
- FWCI
- 15.68
- Type
- journal article
- Language
- en
- Open access
- gold
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