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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...

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Detalles Bibliográficos
Autores principales: Do, Quang, Martini, Ben, Choo, Kim-Kwang Raymond
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
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.
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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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