Connected Vehicles Security; CAN Bus Reverse Engineering; Fingerprinting; Morphing; Machine Learning
Abstract :
[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 :
Computer science
Author, co-author :
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)
External co-authors :
no
Language :
English
Title :
Preventing Frame Fingerprinting in Controller Area Network Through Traffic Mutation
Publication date :
May 2022
Event name :
IEEE ICC 2022 Workshop - DDINS
Event date :
from 16-05-2022 to 20-05-2022
Main work title :
IEEE ICC 2022 Workshop - DDINS, Seoul 16-20 May 2022
Peer reviewed :
Peer reviewed
FnR Project :
FNR10621687 > Sjouke Mauw > SPsquared > Security And Privacy For System Protection > 01/01/2017 > 30/06/2023 > 2015
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