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

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.