Applying Deep Learning Techniques to CAN Bus Attacks for Supporting Identification and Analysis Tasks

Alfredo Cuzzocrea, Fabio Martinelli, Francesco Mercaldo

2018

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.

Paper Citation

in Harvard Style

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 1: SECRYPT, ISBN 978-989-758-319-3, pages 313-321. DOI: 10.5220/0006835603130321

in Bibtex Style

@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 1: SECRYPT,},
year={2018},
pages={313-321},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006835603130321},
isbn={978-989-758-319-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on e-Business and Telecommunications - Volume 1: 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 - 10.5220/0006835603130321