A Quantitative Approach to Triaging in Mobile Forensics

F. Marturana, G. Me, R. Berte, S. Tacconi
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引用次数: 57

Abstract

Forensic study of mobile devices is a relatively new field, dating from the early 2000s. The proliferation of phones (particularly smart phones) on the consumer market has caused a growing demand for forensic examination of the devices, which could not be met by existing Computer Forensics techniques. As a matter of fact, Law enforcement are much more likely to encounter a suspect with a mobile device in his possession than a PC or laptop and so the growth of demand for analysis of mobiles has increased exponentially in the last decade. Early investigations, moreover, consisted of live analysis of mobile devices by examining phone contents directly via the screen and photographing it with the risk of modifying the device content, as well as leaving many parts of the proprietary operating system inaccessible. The recent development of Mobile Forensics, a branch of Digital Forensics, is the answer to the demand of forensically sound examination procedures of gathering, retrieving, identifying, storing and documenting evidence of any digital device that has both internal memory and communication ability [1]. Over time commercial tools appeared which allowed analysts to recover phone content with minimal interference and examine it separately. By means of such toolkits, moreover, it is now possible to think of a new approach to Mobile Forensics which takes also advantage of "Data Mining" and "Machine Learning" theory. This paper is the result of study concerning cell phones classification in a real case of pedophilia. Based on Mobile Forensics "Triaging" concept and the adoption of self-knowledge algorithms for classifying mobile devices, we focused our attention on a viable way to predict phone usage's classifications. Based on a set of real sized phones, the research has been extensively discussed with Italian law enforcement cyber crime specialists in order to find a viable methodology to determine the likelihood that a mobile phone has been used to commit the specific crime of pedophilia, which could be very relevant during a forensic investigation.
移动取证中分诊的定量方法
移动设备的法医研究是一个相对较新的领域,可以追溯到21世纪初。手机(尤其是智能手机)在消费市场上的激增,导致对设备的法医检查需求不断增长,而现有的计算机取证技术无法满足这一需求。事实上,执法部门更有可能遇到拥有移动设备的嫌疑人,而不是个人电脑或笔记本电脑,因此,在过去十年中,对移动设备分析的需求呈指数级增长。此外,早期的调查包括对移动设备的实时分析,方法是直接通过屏幕检查手机内容,并对其拍照,冒着修改设备内容的风险,同时也使专有操作系统的许多部分无法访问。移动取证(Mobile Forensics)是数字取证(Digital Forensics)的一个分支,是为了满足对任何具有内存和通信能力的数字设备的证据收集、检索、识别、存储和记录的法医健全的检验程序的需求而发展起来的[1]。随着时间的推移,商业工具的出现使分析人员能够以最小的干扰恢复手机内容并单独检查。此外,借助这些工具包,现在有可能想到一种新的移动取证方法,这种方法也利用了“数据挖掘”和“机器学习”理论。本文是对一个真实的恋童癖案例进行手机分类研究的结果。基于移动取证“Triaging”的概念,采用自我认知算法对移动设备进行分类,研究了一种可行的手机使用分类预测方法。这项研究基于一组真实大小的手机,与意大利执法网络犯罪专家进行了广泛的讨论,以找到一种可行的方法来确定手机被用来犯下恋童癖这一特定罪行的可能性,这在法医调查中可能非常相关。
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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