Automated Reverse Engineering of CAN Protocols
- Car manufacturers define proprietary protocols to be used inside their vehicular networks, which are kept an industrial secret, therefore impeding independent researchers from extracting information from these networks. This article describes a statistical and a neural network approach that allows reverse engineering proprietary controller area network (CAN)-protocols assuming they were designed using the data base CAN (DBC) file format. The proposed algorithms are tested with CAN traces taken from a real car. We show that our approaches can correctly reverse engineer CAN messages in an automated manner.
Author: | Nils Weiss, Enrico Pozzobon, Jürgen MottokORCiDGND, Václav Matoušek |
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DOI: | https://doi.org/10.14311/NNW.2021.31.015 |
ISSN: | 1210-0552 |
Parent Title (English): | Neural Network World |
Document Type: | Article |
Language: | English |
Year of first Publication: | 2021 |
Release Date: | 2022/03/07 |
Volume: | 31 |
Issue: | 4 |
First Page: | 279 |
Last Page: | 295 |
Institutes: | Fakultät Elektro- und Informationstechnik |
Fakultät Elektro- und Informationstechnik / Laboratory for Safe and Secure Systems (LAS3) | |
Begutachtungsstatus: | peer-reviewed |
research focus: | Information und Kommunikation |
Licence (German): | Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG |