时态元数据分析:学习分类系统方法

IF 2 4区 医学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Michael C. Todd, Gilbert L. Peterson
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引用次数: 0

摘要

数字取证是一个复杂的领域,需要专家知识(EK)和专业工具来收集、分析和报告数字证据。时间元数据分析尤其具有挑战性,需要专家知识来理解和解释底层痕迹,并将它们与来源联系起来。本文介绍了数字痕迹检查器(DTI),这是一种基于学习分类系统(LCS)的决策支持工具,用于时态元数据分析。DTI 利用二进制密歇根式 LCS 来定位和分组目标用户活动的时间数字痕迹。规则由专家创建,并使用结构化 EK 规则框架中定义的模式编码为特征向量。该系统在 Windows 10 工作站上的 10 个典型用户行为场景中进行了评估。结果表明,所有模型都达到了完美的召回率,平均 F1 得分为 0.98,并且几乎不需要训练数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Temporal metadata analysis: A learning classifier system approach

Temporal metadata analysis: A learning classifier system approach
Digital forensics is a complex field that requires expert knowledge (EK) and specialized tools to collect, analyze, and report on digital evidence. Temporal metadata analysis is particularly challenging, requiring expert knowledge to understand and interpret underlying traces and associate them with their source. This paper introduces Digital Trace Inspector (DTI), a Learning Classifier System (LCS)-based decision support tool for temporal metadata analysis. DTI leverages a binary Michigan-style LCS to locate and group corroborating temporal digital traces of targeted user activity. Rules are built from expert-created atomics encoded as feature vectors using patterns defined in a structured EK rule framework. The system is evaluated on 10 scenarios of typical user behavior on a Windows 10 workstation. Results show that all models achieved perfect recall, had an average F1 score of 0.98, and required little training data.
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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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