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Deep Learning for In-Vehicle Intrusion Detection System

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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Conference papers
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https://hal.archives-ouvertes.fr/hal-03046673
Contributor : Elies Gherbi <>
Submitted on : Tuesday, December 8, 2020 - 3:01:03 PM
Last modification on : Thursday, December 10, 2020 - 3:41:26 AM

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Elies Gherbi, Blaise Hanczar, Jean-Christophe Janodet, Witold Klaudel. Deep Learning for In-Vehicle Intrusion Detection System. 27th International Conference on Neural Information Processing (ICONIP 2020), Nov 2020, Bangkok, Thailand. pp.50--58, ⟨10.1007/978-3-030-63820-7_6⟩. ⟨hal-03046673⟩

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