Reference : On Frame Fingerprinting and Controller Area Networks Security in Connected Vehicles
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/48391
On Frame Fingerprinting and Controller Area Networks Security in Connected Vehicles
English
Buscemi, Alessio mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
Turcanu, Ion [Luxembourg Institute of Science & Technology - LIST > ITIS]
Castignani, German [University of Luxembourg > FSTM]
Engel, Thomas [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS) >]
Jan-2022
IEEE Consumer Communications & Networking Conference, Virtual Conference 8-11 January 2022
Yes
2022 IEEE Consumer Communications & Networking Conference
from 08-01-2022 to 11-01-2022
[en] Connected Vehicles Security ; CAN Bus Reverse Engineering ; Machine Learning ; Frame Identification
[en] Modern connected vehicles are equipped with a large number of sensors, which enable a wide range of services that can improve overall traffic safety and efficiency. However, remote access to connected vehicles also introduces new security issues affecting both inter and intra-vehicle communications. In fact, existing intra-vehicle communication systems, such as Controller Area Network (CAN), lack security features, such as encryption and secure authentication for Electronic Control Units (ECUs). Instead, Original Equipment Manufacturers (OEMs) seek security through obscurity by keeping secret the proprietary format with which they encode the information. Recently, it has been shown that the reuse of CAN frame IDs can be exploited to perform CAN bus reverse engineering without physical access to the vehicle, thus raising further security concerns in a connected environment. This work investigates whether anonymizing the frames of each newly released vehicle is sufficient to prevent CAN bus reverse engineering based on frame ID matching. The results show that, by adopting Machine Learning techniques, anonymized CAN frames can still be fingerprinted and identified in an unknown vehicle with an accuracy of up to 80 %.
http://hdl.handle.net/10993/48391
FnR ; FNR10621687 > Sjouke Mauw > SPsquared > Security And Privacy For System Protection > 01/01/2017 > 30/06/2023 > 2015

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