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Remote Programmatic vCloud Forensics: A Six-Step Collection Process and a Proof of Concept

Authors: Ben Martini; Kim-Kwang Raymond Choo;

Remote Programmatic vCloud Forensics: A Six-Step Collection Process and a Proof of Concept

Abstract

With the increasing popularity of cloud services and their potential to either be the target or the tool in a cybercrime activity, organizational cloud services users need to ensure that they are able to collect evidential data should they be involved in litigation or a criminal investigation. In this paper, we seek to contribute to a better understanding of the technical issues and processes regarding collection of evidential data in the cloud computing environment. Using VMware vCloud as a case study in this paper, we describe the various artefacts available in the cloud environment and identify several forensic preservation considerations for forensics practitioners. We then propose a six-step process for the remote programmatic collection of evidential data to ensure as few changes as possible are made as part of evidence collection and that no potential evidence is missed. The six-step process is implemented in a proof of concept application to demonstrate utility of the process.

Related Organizations
Keywords

remote cloud forensic process, remote evidence preservation, cloud forensics, remote evidence collection, vCloud

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    influence
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Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
26
Average
Top 10%
Top 10%
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