Suppr超能文献

一种适用于无人机取证领域的新型取证准备框架。

A Novel Forensic Readiness Framework Applicable to the Drone Forensics Field.

机构信息

Department Information System, Faculty of Computing and Information Technology (FCIT), King Abdulaziz University, Jeddah, Saudi Arabia.

School of Computing, Faculty of Engineering, Universiti Teknologi, Skudai, Malaysia.

出版信息

Comput Intell Neurosci. 2022 Feb 28;2022:8002963. doi: 10.1155/2022/8002963. eCollection 2022.

Abstract

The Drone Forensics (DRFs) field is a branch of digital forensics, which involves the identification, capture, preservation, reconstruction, analysis, and documentation of drone incidents. Several models have been proposed in the literature for the DRF field, which generally discusses DRF from a reactive forensic perspective; however, the proactive forensic perspective is missing. Therefore, this paper proposes a novel forensic readiness framework called Drone Forensics Readiness Framework (DRFRF) using the design science method. It consists of two stages: (i) proactive forensic stage and (ii) reactive forensic stage. It considers centralized logging of all events of all the applicants within the drone device in preparation for an examination. It will speed up gathering data when an investigation is needed, permitting the forensic investigators to handle the examination and analysis directly. Additionally, digital forensics analysts can increase the possible use of digital evidence while decreasing the charge of performing forensic readiness. Thus, both the time and cost required to perform forensic readiness could be saved. The completeness, logicalness, and usefulness of DRFRF were compared to those of other models already existing in the DRF domain. The results showed the novelty and efficiency of DRFRF and its applicability to the situations before and after drone incidents.

摘要

无人机取证(DRF)领域是数字取证的一个分支,涉及到对无人机事件的识别、捕获、保存、重建、分析和记录。文献中已经提出了几种用于 DRF 领域的模型,这些模型通常从反应式取证的角度讨论 DRF;然而,缺少主动式取证的视角。因此,本文提出了一种名为无人机取证准备框架(DRFRF)的新取证准备框架,该框架使用设计科学方法。它由两个阶段组成:(i)主动取证阶段和(ii)反应取证阶段。它考虑了在无人机设备中集中记录所有申请人的所有事件,为检查做准备。当需要进行调查时,它将加快数据收集的速度,允许取证调查人员直接进行检查和分析。此外,数字取证分析师可以在减少执行取证准备工作所需的费用的同时,增加数字证据的可能用途。因此,可以节省执行取证准备工作所需的时间和成本。DRFRF 的完整性、逻辑性和有用性与 DRF 领域中已经存在的其他模型进行了比较。结果表明了 DRFRF 的新颖性和效率,以及它在无人机事件前后的情况中的适用性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a67/8901304/547fc743b078/CIN2022-8002963.001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验