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Paper details
Number 4 - December 2021
Volume 31 - 2021
Forensic driver identification considering an unknown suspect
Klara Dološ, Conrad Meyer, Andreas Attenberger, Jessica Steinberger
Abstract
One major focus in forensics is the identification of individuals based on different kinds of evidence found at a crime
scene and in the digital domain. Here, we assess the potential of using in-vehicle digital data to capture the natural driving
behavior of individuals in order to identify them. We formulate a forensic scenario of a hit-and-run car accident with a
known and an unknown suspect being the actual driver during the accident. Specific aims of this study are (i) to further
develop a workflow for driver identification in digital forensics considering a scenario with an unknown suspect, and (ii) to
assess the potential of one-class compared to multi-class classification for this task. The developed workflow demonstrates
that in the application of machine learning in digital forensics it is important to decide on the statistical application, data
mining or hypothesis testing in advance. Further, multi-class classification is superior to one-class classification in terms
of statistical model quality. Using multi-class classification it is possible to contribute to the identification of the driver in
the hit-and-run accident in both types of application, data mining and hypothesis testing. Model quality is in the range of
already employed methods for forensic identification of individuals.
Keywords
natural driving behavior, digital biometry, OCC, CAN-BUS data, validation