AI-driven dataset creation in mobile forensics using LLM-based storyboards

IF 2.2 4区 医学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Dirk Pawlaszczyk , Philipp Engler , Ronny Bodach , Christian Hummert , Margaux Michel , Ralf Zimmermann
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引用次数: 0

Abstract

The generation of datasets is essential for training and validation tasks in digital forensics. Currently, the processes of data generation and provisioning are mainly performed manually. In the field of mobile forensics, there are only a limited number of tools available that aid in populating and injecting data into mobile devices. In this paper, we introduce a novel method for automatic data generation using an AI-driven approach. We present a comprehensive toolchain for dataset creation, focusing on developing a dynamic model (storyboard) with the assistance of large language model (LLM) agents. The generated sequences of activities are then automatically executed on mobile devices. Our proposed approach has been successfully implemented within the data creation and injection framework called AutoPodMobile (APM) as part of a proof-of-concept study. For data generated through AI methods, a validation is presented as well. The paper ends with a brief discussion of the results and the next steps planned.
使用基于法学硕士的故事板在移动取证中创建ai驱动的数据集
数据集的生成对于数字取证的培训和验证任务至关重要。目前,数据的生成和发放主要是手工完成的。在移动取证领域,只有数量有限的工具可以帮助将数据填充和注入移动设备。在本文中,我们介绍了一种使用人工智能驱动方法自动生成数据的新方法。我们提出了一个用于数据集创建的综合工具链,重点是在大型语言模型(LLM)代理的帮助下开发动态模型(故事板)。生成的活动序列然后在移动设备上自动执行。作为概念验证研究的一部分,我们提出的方法已经在名为AutoPodMobile (APM)的数据创建和注入框架中成功实施。对于通过人工智能方法生成的数据,也给出了验证。论文最后简要讨论了研究结果和下一步计划。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.90
自引率
15.00%
发文量
87
审稿时长
76 days
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