Reference : A Data-Driven Minimal Approach for CAN Bus Reverse Engineering

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/10993/44180

Title :	A Data-Driven Minimal Approach for CAN Bus Reverse Engineering
Language :	English
Author, co-author :	Buscemi, Alessio [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
	Castignani, German [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)]
	Engel, Thomas [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
	Turcanu, Ion [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Publication date :	2020
Main document title :	3rd IEEE Connected and Automated Vehicles Symposium, Victoria, Canada, 4-5 October 2020
Peer reviewed :	Yes
On invitation :	No
Audience :	International
Event name :	3rd IEEE Connected and Automated Vehicles Symposium
Event date :	from 04-10-2020 to 05-10-2020
Keywords :	[en] CAN Bus ; Automated Reverse Engineering ; In-Vehicle Networks ; Signal Identification ; Machine Learning
Abstract :	[en] Current in-vehicle communication systems lack security features, such as encryption and secure authentication. The approach most commonly used by car manufacturers is to achieve security through obscurity – keep the proprietary format used to encode the information secret. However, it is still possible to decode this information via reverse engineering. Existing reverse engineering methods typically require physical access to the vehicle and are time consuming. In this paper, we present a Machine Learning-based method that performs automated Controller Area Network (CAN) bus reverse engineering while requiring minimal time, hardware equipment, and potentially no physical access to the vehicle. Our results demonstrate high accuracy in identifying critical vehicle functions just from analysing raw traces of CAN data.
Funders :	Fonds National de la Recherche - FnR
Permalink :	http://hdl.handle.net/10993/44180
DOI :	10.1109/CAVS51000.2020.9334650

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