Publication
Title
Automatic reverse engineering of CAN bus data using machine learning techniques
Author
Abstract
The CAN (Controller Area Network) bus connects different Electronic Control Units (ECU) inside a vehicle. Valuable information about the state of the vehicle is present on this bus and is useful to track driver behaviour, the health of the vehicle, etc. However, the configuration of this bus is not publicly disclosed by the car manufacturers. Therefore, reverse engineering techniques need to be applied. Nevertheless, performing these techniques manually is cumbersome and time consuming. In this paper, we propose an automation of the analysis steps of reverse engineering in order to improve and facilitate the process. Two approaches of automation are discussed, namely an arithmetic approach and machine learning using classification. In conclusion, we show that reverse engineering of CAN bus traffic is at least partially possible by applying machine learning techniques and the performance of the classifiers increases when adding additional features to the analysis.
Language
English
Source (book)
Advances on P2P, Parallel, Grid, Cloud and Internet Computing : proceedings of the 12th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC-2017), November 8-10, 2017, Barcelona, Spain / Xhafa, Fatos [edit.]
Source (series)
Lecture notes on data engineering and communications technologies ; 13
Publication
Cham : Springer international publishing ag , 2018
ISBN
978-3-319-69834-2
978-3-319-69834-2
978-3-319-69835-9
DOI
10.1007/978-3-319-69835-9_71
Volume/pages
13 (2018) , p. 751-761
ISI
000464606800071
Full text (Publisher's DOI)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identifier
Creation 06.11.2017
Last edited 04.03.2024
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