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Conference Papers Year : 2020

Deep Learning for In-Vehicle Intrusion Detection System


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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hal-03046673 , version 1 (08-12-2020)



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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