Connected Vehicles Security; CAN Bus Reverse Engineering; Fingerprinting; Morphing; Machine Learning
Résumé :
[en] The continuous increase of connectivity in commercial vehicles is leading to a higher number of remote access points to the Controller Area Network (CAN) – the most popular in-vehicle network system. This factor, coupled with the absence of encryption in the communication protocol, poses serious threats to the security of the CAN bus. Recently, it has been demonstrated that CAN data can be reverse engineered via frame fingerprinting, i.e., identification of frames based on statistical traffic analysis. Such a methodology allows fully remote decoding of in-vehicle data and paves the way for remote pre-compiled vehicle-agnostic attacks. In this work, we propose a first solution against CAN frame fingerprinting based on mutating the traffic without applying modifications to the CAN protocol. The results show that the proposed methodology halves the accuracy of CAN frame fingerprinting.
Disciplines :
Sciences informatiques
Auteur, co-auteur :
BUSCEMI, Alessio ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)
Turcanu, Ion; Luxembourg Institute of Science & Technology - LIST
CASTIGNANI, German ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
ENGEL, Thomas ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)
Co-auteurs externes :
no
Langue du document :
Anglais
Titre :
Preventing Frame Fingerprinting in Controller Area Network Through Traffic Mutation
Date de publication/diffusion :
mai 2022
Nom de la manifestation :
IEEE ICC 2022 Workshop - DDINS
Date de la manifestation :
from 16-05-2022 to 20-05-2022
Titre de l'ouvrage principal :
IEEE ICC 2022 Workshop - DDINS, Seoul 16-20 May 2022
Peer reviewed :
Peer reviewed
Projet FnR :
FNR10621687 - Security And Privacy For System Protection, 2015 (01/01/2017-30/06/2023) - Sjouke Mauw
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