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 Forensic Science International-Digital Investigation Pub Date : 2024-10-30 DOI:10.1016/j.fsidi.2024.301842
Michael C. Todd, Gilbert L. Peterson
{"title":"时态元数据分析:学习分类系统方法","authors":"Michael C. Todd,&nbsp;Gilbert L. Peterson","doi":"10.1016/j.fsidi.2024.301842","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48481,"journal":{"name":"Forensic Science International-Digital Investigation","volume":"51 ","pages":"Article 301842"},"PeriodicalIF":2.0000,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Temporal metadata analysis: A learning classifier system approach\",\"authors\":\"Michael C. Todd,&nbsp;Gilbert L. Peterson\",\"doi\":\"10.1016/j.fsidi.2024.301842\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":48481,\"journal\":{\"name\":\"Forensic Science International-Digital Investigation\",\"volume\":\"51 \",\"pages\":\"Article 301842\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2024-10-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Forensic Science International-Digital Investigation\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2666281724001690\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Forensic Science International-Digital Investigation","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666281724001690","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
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。

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
5.90
自引率
15.00%
发文量
87
审稿时长
76 days
期刊最新文献
Commentary:- Can I use that tool? Temporal metadata analysis: A learning classifier system approach Uncertainty and error in location traces Competence in digital forensics “What you say in the lab, stays in the lab”: A reflexive thematic analysis of current challenges and future directions of digital forensic investigations in the UK
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1