Skip to Main content Skip to Navigation
New interface
Conference papers

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.
Document type :
Conference papers
Complete list of metadata
Contributor : elies gherbi Connect in order to contact the contributor
Submitted on : Tuesday, December 8, 2020 - 3:01:03 PM
Last modification on : Wednesday, October 19, 2022 - 3:57:11 AM



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⟩



Record views