Abstract
Modern and future vehicles are complex cyber-physical systems. The connection to their outside environment raises many security problems that impact our safety directly. In this work, we propose a Deep CAN intrusion detection system framework. We introduce a multivariate time series representation for asynchronous CAN data which enhances the temporal modelling of deep learning architectures for anomaly detection. We study different deep learning tasks (supervised/unsupervised) and compare several architectures, in order to design an in-vehicle intrusion detection system that fits in-vehicle computational constraints. We conduct experiments with many types of attacks on an in-vehicle CAN using SynCAn Dataset.
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Notes
- 1.
See https://github.com/anonymeEG/Deep-Learning-4-IDS for implementation.
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Gherbi, E., Hanczar, B., Janodet, JC., Klaudel, W. (2020). Deep Learning for In-Vehicle Intrusion Detection System. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_6
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DOI: https://doi.org/10.1007/978-3-030-63820-7_6
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