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无人机取证领域的全面采集与分析模型。

A Comprehensive Collection and Analysis Model for the Drone Forensics Field.

机构信息

Faculty of Computing and Information Technology (FCIT), King Abdulaziz University, Jeddah 22254, Saudi Arabia.

Faculty of Engineering, School of Computing, Universiti Teknologi Malaysia, Skudai 81310, Malaysia.

出版信息

Sensors (Basel). 2022 Aug 29;22(17):6486. doi: 10.3390/s22176486.

Abstract

Unmanned aerial vehicles (UAVs) are adaptable and rapid mobile boards that can be applied to several purposes, especially in smart cities. These involve traffic observation, environmental monitoring, and public safety. The need to realize effective drone forensic processes has mainly been reinforced by drone-based evidence. Drone-based evidence collection and preservation entails accumulating and collecting digital evidence from the drone of the victim for subsequent analysis and presentation. Digital evidence must, however, be collected and analyzed in a forensically sound manner using the appropriate collection and analysis methodologies and tools to preserve the integrity of the evidence. For this purpose, various collection and analysis models have been proposed for drone forensics based on the existing literature; several models are inclined towards specific scenarios and drone systems. As a result, the literature lacks a suitable and standardized drone-based collection and analysis model devoid of commonalities, which can solve future problems that may arise in the drone forensics field. Therefore, this paper has three contributions: (a) studies the machine learning existing in the literature in the context of handling drone data to discover criminal actions, (b) highlights the existing forensic models proposed for drone forensics, and (c) proposes a novel comprehensive collection and analysis forensic model (CCAFM) applicable to the drone forensics field using the design science research approach. The proposed CCAFM consists of three main processes: (1) acquisition and preservation, (2) reconstruction and analysis, and (3) post-investigation process. CCAFM contextually leverages the initially proposed models herein incorporated in this study. CCAFM allows digital forensic investigators to collect, protect, rebuild, and examine volatile and nonvolatile items from the suspected drone based on scientific forensic techniques. Therefore, it enables sharing of knowledge on drone forensic investigation among practitioners working in the forensics domain.

摘要

无人飞行器 (UAV) 是一种适应性强、移动速度快的平台,可应用于多个领域,尤其是在智慧城市中。这些应用包括交通观察、环境监测和公共安全。实现有效的无人机取证过程的需求主要是由基于无人机的证据推动的。基于无人机的证据收集和保存需要从受害者的无人机中积累和收集数字证据,以便后续进行分析和呈现。然而,为了保持证据的完整性,必须以合理的方式使用适当的收集和分析方法和工具来收集和分析数字证据。为此,基于现有文献,已经提出了各种基于无人机的取证收集和分析模型;一些模型倾向于特定的场景和无人机系统。因此,文献中缺乏一个合适的、标准化的、没有共同点的基于无人机的收集和分析模型,无法解决无人机取证领域可能出现的未来问题。因此,本文有三个贡献:(a)在处理无人机数据以发现犯罪行为的背景下,研究文献中的机器学习;(b)强调为无人机取证提出的现有取证模型;(c)使用设计科学研究方法,提出一种适用于无人机取证领域的新的综合取证模型(CCAFM)。拟议的 CCAFM 由三个主要流程组成:(1)获取和保存;(2)重建和分析;(3)调查后处理。CCAFM 上下文利用了本文中最初提出的模型。CCAFM 允许数字取证调查员根据科学取证技术,从可疑无人机中收集、保护、重建和检查易失性和非易失性项目。因此,它使在取证领域工作的从业者能够共享有关无人机取证调查的知识。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e05b/9460793/a443cc2cd424/sensors-22-06486-g001.jpg

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