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A Forensically Sound Adversary Model for Mobile Devices
In this paper, we propose an adversary model to facilitate forensic investigations of mobile devices (e.g. Android, iOS and Windows smartphones) that can be readily adapted to the latest mobile device technologies. This is essential given the ongoing and rapidly changing nature of mobile device tech...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4579090/ https://www.ncbi.nlm.nih.gov/pubmed/26393812 http://dx.doi.org/10.1371/journal.pone.0138449 |
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author | Do, Quang Martini, Ben Choo, Kim-Kwang Raymond |
author_facet | Do, Quang Martini, Ben Choo, Kim-Kwang Raymond |
author_sort | Do, Quang |
collection | PubMed |
description | In this paper, we propose an adversary model to facilitate forensic investigations of mobile devices (e.g. Android, iOS and Windows smartphones) that can be readily adapted to the latest mobile device technologies. This is essential given the ongoing and rapidly changing nature of mobile device technologies. An integral principle and significant constraint upon forensic practitioners is that of forensic soundness. Our adversary model specifically considers and integrates the constraints of forensic soundness on the adversary, in our case, a forensic practitioner. One construction of the adversary model is an evidence collection and analysis methodology for Android devices. Using the methodology with six popular cloud apps, we were successful in extracting various information of forensic interest in both the external and internal storage of the mobile device. |
format | Online Article Text |
id | pubmed-4579090 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-45790902015-10-01 A Forensically Sound Adversary Model for Mobile Devices Do, Quang Martini, Ben Choo, Kim-Kwang Raymond PLoS One Research Article In this paper, we propose an adversary model to facilitate forensic investigations of mobile devices (e.g. Android, iOS and Windows smartphones) that can be readily adapted to the latest mobile device technologies. This is essential given the ongoing and rapidly changing nature of mobile device technologies. An integral principle and significant constraint upon forensic practitioners is that of forensic soundness. Our adversary model specifically considers and integrates the constraints of forensic soundness on the adversary, in our case, a forensic practitioner. One construction of the adversary model is an evidence collection and analysis methodology for Android devices. Using the methodology with six popular cloud apps, we were successful in extracting various information of forensic interest in both the external and internal storage of the mobile device. Public Library of Science 2015-09-22 /pmc/articles/PMC4579090/ /pubmed/26393812 http://dx.doi.org/10.1371/journal.pone.0138449 Text en © 2015 Do et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Do, Quang Martini, Ben Choo, Kim-Kwang Raymond A Forensically Sound Adversary Model for Mobile Devices |
title | A Forensically Sound Adversary Model for Mobile Devices |
title_full | A Forensically Sound Adversary Model for Mobile Devices |
title_fullStr | A Forensically Sound Adversary Model for Mobile Devices |
title_full_unstemmed | A Forensically Sound Adversary Model for Mobile Devices |
title_short | A Forensically Sound Adversary Model for Mobile Devices |
title_sort | forensically sound adversary model for mobile devices |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4579090/ https://www.ncbi.nlm.nih.gov/pubmed/26393812 http://dx.doi.org/10.1371/journal.pone.0138449 |
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