AUTOMATED DATA POPULATION FOR IOS DEVICES WITH AUTOPODMOBILE

Training investigators with realistic datasets is crucial for mobile forensics. However, until now, most training data has been generated manually, with very few automated solutions available. Particularly for iOS devices, there is currently no automated method for transferring data to phones. This article addresses this issue by introducing an approach that enables macros to be executed on the target device using simple onboard tools. Additionally, it presents the latest version of AutoPodMobile, a tool designed for automatic data population and injection. The paper discusses the results of a proof-of-concept implementation and concludes with an analysis of the findings, along with options for future enhancements.

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Publication Details

Journal
Journal of Digital Security and Forensics
Published
2025-05-23
DOI
https://doi.org/10.29121/digisecforensics.v2.i1.2025.46
Citations
2
Primary Topic
Digital and Cyber Forensics
Type
article
Field-Weighted Citation Impact
6.49
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article

AUTOMATED DATA POPULATION FOR IOS DEVICES WITH AUTOPODMOBILE

Ralf Zimmermann, Philipp Engler, Dirk Pawlaszczyk, Margaux Michel et al.
2 citations
Journal of Digital Security and Forensics
Digital and Cyber Forensics
6.49
article

AUTOMATED DATA POPULATION FOR IOS DEVICES WITH AUTOPODMOBILE

Ralf Zimmermann, Philipp Engler, Dirk Pawlaszczyk, Margaux Michel, Ronny Bodach
article en
2 citations

Abstract

Training investigators with realistic datasets is crucial for mobile forensics. However, until now, most training data has been generated manually, with very few automated solutions available. Particularly for iOS devices, there is currently no automated method for transferring data to phones. This article addresses this issue by introducing an approach that enables macros to be executed on the target device using simple onboard tools. Additionally, it presents the latest version of AutoPodMobile, a tool designed for automatic data population and injection. The paper discusses the results of a proof-of-concept implementation and concludes with an analysis of the findings, along with options for future enhancements.

Journal of Digital Security and ForensicsVol. 2(1)
Hochschule Mittweida (DE), Central Statistical Office (ZM)
Openalex Percentile: Top 4%
Digital and Cyber Forensics
6.49
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AUTOMATED DATA POPULATION FOR IOS DEVICES WITH AUTOPODMOBILE — Ralf Zimmermann, Philipp Engler, et al. · Journal of Digital Security and Forensics (2025) | TGRS Research Map | TGRS