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Article

AutoPoD-Mobile—Semi-Automated Data Population Using Case-like Scenarios for Training and Validation in Mobile Forensics

by
Margaux Michel
1,
Dirk Pawlaszczyk
2,* and
Ralf Zimmermann
1
1
Central Office for Information Technology in the Security Sector (ZITiS), Zamdorfer Str. 88, 81677 Munich, Germany
2
Faculty of Computer Sciences, Hochschule Mittweida—University of Applied Sciences, Technikumplatz 17, 09648 Mittweida, Germany
*
Author to whom correspondence should be addressed.
Forensic Sci. 2022, 2(2), 302-320; https://doi.org/10.3390/forensicsci2020023
Submission received: 30 January 2022 / Revised: 4 March 2022 / Accepted: 22 March 2022 / Published: 25 March 2022
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Abstract

The complexity and constant changes in mobile forensics require special training of investigators with datasets that are as realistic as possible. Even today, the generation of training data is almost exclusively done manually. This paper presents a novel open-source framework called AutoPoD-Mobile. The framework supports the creation of case-based scenarios. Even more, the semi-automated provision of datasets for mobile forensics is enabled. Thus, the behavior of suspects interacting with each other can be simulated. The result combines mobile device data from normal device usage and case-related information. This way helps validate mobile forensic tools, test new techniques, and create realistic training datasets in the mobile forensics domain. The results of a proof-of-concept trial in a realistic deployment environment will also be presented. The paper concludes with a discussion of the results and identifies options for future improvements.
Keywords: mobile device digital forensics; digital forensics analysis; digital evidence; device population; training; forensics dataset; behavioral simulation mobile device digital forensics; digital forensics analysis; digital evidence; device population; training; forensics dataset; behavioral simulation

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MDPI and ACS Style

Michel, M.; Pawlaszczyk, D.; Zimmermann, R. AutoPoD-Mobile—Semi-Automated Data Population Using Case-like Scenarios for Training and Validation in Mobile Forensics. Forensic Sci. 2022, 2, 302-320. https://doi.org/10.3390/forensicsci2020023

AMA Style

Michel M, Pawlaszczyk D, Zimmermann R. AutoPoD-Mobile—Semi-Automated Data Population Using Case-like Scenarios for Training and Validation in Mobile Forensics. Forensic Sciences. 2022; 2(2):302-320. https://doi.org/10.3390/forensicsci2020023

Chicago/Turabian Style

Michel, Margaux, Dirk Pawlaszczyk, and Ralf Zimmermann. 2022. "AutoPoD-Mobile—Semi-Automated Data Population Using Case-like Scenarios for Training and Validation in Mobile Forensics" Forensic Sciences 2, no. 2: 302-320. https://doi.org/10.3390/forensicsci2020023

APA Style

Michel, M., Pawlaszczyk, D., & Zimmermann, R. (2022). AutoPoD-Mobile—Semi-Automated Data Population Using Case-like Scenarios for Training and Validation in Mobile Forensics. Forensic Sciences, 2(2), 302-320. https://doi.org/10.3390/forensicsci2020023

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