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Authors: Alfredo Cuzzocrea 1 ; Fabio Martinelli 2 and Francesco Mercaldo 2

Affiliations: 1 University of Trieste & ICAR-CNR, Trieste and Italy ; 2 Institute for Informatics and Telematics, National Research Council of Italy (CNR), Pisa and Italy

ISBN: 978-989-758-319-3

Keyword(s): Automotive, CAN, OBD, Deep Learning, Security, Testing.

Related Ontology Subjects/Areas/Topics: Critical Infrastructure Protection ; Information and Systems Security ; Software Security

Abstract: Cars are no longer only mechanical vehicles. As a matter of fact, they contain an ecosystem of several electronic units able to exchange data using the serial communication provided by the CAN bus. CAN packets are broadcasted to all components and it is in charge of the single component to decide whether it is the receiver of the packets, in addition the protocol does not provide source identification of authentication: these are the reasons why the CAN bus is exposed to attacks. In this paper we design a method to identify CAN bus targeting attacks. The proposed method takes into account deep learning algorithms i.e., the Neural Network and the MultiLayer Perception. We evaluated our method using CAN messages gathered from a real vehicle injecting four different attacks (i.e. dos, fuzzy, gear and rpm), obtaining encouraging results in attacks identification.

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Paper citation in several formats:
Cuzzocrea, A.; Martinelli, F. and Mercaldo, F. (2018). Applying Deep Learning Techniques to CAN Bus Attacks for Supporting Identification and Analysis Tasks.In Proceedings of the 15th International Joint Conference on e-Business and Telecommunications - Volume 2 SECRYPT: SECRYPT, ISBN 978-989-758-319-3, pages 313-321. DOI: 10.5220/0006835604790487

@conference{secrypt18,
author={Alfredo Cuzzocrea. and Fabio Martinelli. and Francesco Mercaldo.},
title={Applying Deep Learning Techniques to CAN Bus Attacks for Supporting Identification and Analysis Tasks},
booktitle={Proceedings of the 15th International Joint Conference on e-Business and Telecommunications - Volume 2 SECRYPT: SECRYPT,},
year={2018},
pages={313-321},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006835604790487},
isbn={978-989-758-319-3},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on e-Business and Telecommunications - Volume 2 SECRYPT: SECRYPT,
TI - Applying Deep Learning Techniques to CAN Bus Attacks for Supporting Identification and Analysis Tasks
SN - 978-989-758-319-3
AU - Cuzzocrea, A.
AU - Martinelli, F.
AU - Mercaldo, F.
PY - 2018
SP - 313
EP - 321
DO - 10.5220/0006835604790487

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