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A Data-Driven Minimal Approach for CAN Bus Reverse Engineering
Buscemi, Alessio; Castignani, German; Engel, Thomas et al.
2020In 3rd IEEE Connected and Automated Vehicles Symposium, Victoria, Canada, 4-5 October 2020
Peer reviewed
 

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Keywords :
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.
Disciplines :
Computer science
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)
External co-authors :
no
Language :
English
Title :
A Data-Driven Minimal Approach for CAN Bus Reverse Engineering
Publication date :
2020
Event name :
3rd IEEE Connected and Automated Vehicles Symposium
Event date :
from 04-10-2020 to 05-10-2020
Audience :
International
Main work title :
3rd IEEE Connected and Automated Vehicles Symposium, Victoria, Canada, 4-5 October 2020
Peer reviewed :
Peer reviewed
Funders :
FNR - Fonds National de la Recherche [LU]
Available on ORBilu :
since 06 September 2020

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