机器学习在数字取证中的应用

S. Qadir, Basirah Noor
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引用次数: 5

摘要

数字取证(DF)已经成为进行深入调查的一个重要过程。但是由于数字化的发展,潜在的数据量越来越大,因此对它们进行分析变得越来越困难。机器学习(ML)是这方面的灵丹妙药。它不仅简化了分析过程,而且产生了准确的结果。因此,以DF为重点,本文调查了广泛提及基于ML的技术的出版物,这些技术可用于简化DF过程,主要用于恶意软件,网络取证,图像/视频取证和移动/内存取证领域。综述结果表明,机器学习是一种快速、可靠的方法,需要更积极地探索,特别是在DF领域。结果还用于开发基于机器学习的数字取证的一般程序的概念框架。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Applications of Machine Learning in Digital Forensics
Digital forensics (DF) has become a substantial process to perform in depth investigations. But due to the digitalization, the potential Data volumes are increasing and hence it has become difficult to analyze them. Machine Learning (ML) is a panacea in this regard. It not only facilitates the analysis process but also yields accurate results. Therefore, with a focus on DF, this paper surveys a wide range of publications mentioning ML based techniques that can be used to ease the process of DF principally in the field of malware, network forensics, image/video forensics, and mobile/memory forensics. The results of the review show that ML is a fast and reliable procedure and needs to be explored more actively, particularly in DF field. The results are also used to develop a conceptual framework for a general procedure of ML based Digital Forensics.
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