Deep Learning for In-Vehicle Intrusion Detection System
| Autor: | Jean-Christophe Janodet, Blaise Hanczar, Elies Gherbi, Witold Klaudel |
|---|---|
| Přispěvatelé: | IRT SystemX (IRT SystemX), Informatique, BioInformatique, Systèmes Complexes (IBISC), Université d'Évry-Val-d'Essonne (UEVE)-Université Paris-Saclay, Yang H., Pasupa K., Leung A.CS., Kwok J.T., Chan J.H., King I., IRT SystemX |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Time series
business.industry Computer science Deep learning Intrusion detection system 020206 networking & telecommunications 02 engineering and technology Anomaly detection 010501 environmental sciences Machine learning computer.software_genre 01 natural sciences [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] Asynchronous communication 0202 electrical engineering electronic engineering information engineering In vehicle [INFO]Computer Science [cs] Artificial intelligence business computer In-vehicle security 0105 earth and related environmental sciences |
| Zdroj: | Communications in Computer and Information Science 27th International Conference on Neural Information Processing (ICONIP 2020) 27th International Conference on Neural Information Processing (ICONIP 2020), Nov 2020, Bangkok, Thailand. pp.50--58, ⟨10.1007/978-3-030-63820-7_6⟩ Communications in Computer and Information Science ISBN: 9783030638191 ICONIP (4) |
| DOI: | 10.1007/978-3-030-63820-7_6⟩ |
| Popis: | International audience; 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. |
| Databáze: | OpenAIRE |
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