  Data generated by cars is growing at an unprecedented scale. As cars
gradually become part of the Internet of Things (IoT) ecosystem, several
stakeholders discover the value of in-vehicle network logs containing the
measurements of the multitude of sensors deployed within the car. This wealth
of data is also expected to be exploitable by third parties for the purpose of
profiling drivers in order to provide personalized, valueadded services.
Although 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, they inferred the identity of the
driver only from known sensor signals (such as the vehicle's speed, brake pedal
position, steering wheel angle, etc.) extracted from the CAN messages. However,
car manufacturers intentionally do not reveal exact signal location and
semantics within CAN logs. We show that the inference of driver identity is
possible even with off-the-shelf machine learning techniques without
reverse-engineering the CAN protocol. We demonstrate our approach on a dataset
of 33 drivers and show that a driver can be re-identified and distinguished
from other drivers with an accuracy of 75-85%.
