<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

Forensic driver identification considering an unknown suspect

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Forensic driver identification considering an unknown suspect"}

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,...

Forensic driver identification considering an unknown suspect

Publié en ligne: 30 déc. 2021

Pages: 587 - 599

Reçu: 09 avr. 2021

Accepté: 19 oct. 2021

DOI: https://doi.org/10.34768/amcs-2021-0040

Mots clés
natural driving behavior, digital biometry, OCC, CAN-BUS data, validation

© 2021 Klara Dološ et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Forensic driver identification considering an unknown suspect

Publié en ligne: 30 déc. 2021

Pages: 587 - 599

Reçu: 09 avr. 2021

Accepté: 19 oct. 2021

DOI: https://doi.org/10.34768/amcs-2021-0040

Mots clésnatural driving behavior, digital biometry, OCC, CAN-BUS data, validation

© 2021 Klara Dološ et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
natural driving behavior, digital biometry, OCC, CAN-BUS data, validation