Authors:
Szilvia Lestyán
1
;
Gergely Acs
1
;
Gergely Biczók
1
and
Zsolt Szalay
2
Affiliations:
1
CrySyS Lab, Dept. of Networked Systems and Services, Budapest Univ. of Technology and Economics and Hungary
;
2
Dept. of Automotive Technology, Budapest Univ. of Technology and Economics and Hungary
Keyword(s):
Driver Re-identification, CAN Bus, Sensor Signals, Privacy, Reverse Engineering, Machine Learning, Time Series Data.
Related
Ontology
Subjects/Areas/Topics:
Information and Systems Security
;
Information Assurance
;
Information Hiding
Abstract:
Data is the new oil for the car industry. Cars generate data about how they are used and who’s behind the wheel which gives rise to a novel way of profiling individuals. Several prior works have successfully demonstrated the feasibility of driver re-identification using the in-vehicle network data captured on the vehicle’s CAN (Controller Area Network) bus. However, all of them used signals (e.g., velocity, brake pedal or accelerator position) that have already been extracted from the CAN log which is itself not a straightforward process. Indeed, car manufacturers intentionally do not reveal the exact signal location within CAN logs. Nevertheless, we show that signals can be efficiently extracted from CAN logs using machine learning techniques. We exploit that signals have several distinguishing statistical features which can be learnt and effectively used to identify them across different vehicles, that is, to quasi ”reverse-engineer” the CAN protocol. We also demonstrate that the e
xtracted signals can be successfully used to re-identify individuals in a dataset of 33 drivers. Therefore, not revealing signal locations in CAN logs per se does not prevent them to be regarded as personal data of drivers.
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