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  5. A Lightweight Software Write-blocker for Virtual Machine Forensics
 
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A Lightweight Software Write-blocker for Virtual Machine Forensics

Author(s)
Tobin, Patrick  
Le-Khac, Nhien-An  
Kechadi, Tahar  
Uri
http://hdl.handle.net/10197/8150
Date Issued
2016-08-26
Date Available
2016-11-25T13:02:13Z
Abstract
The integrity of any original evidence is fundamental to a forensic examination. Preserving the integrity of digital evidence is vitally important as changing just one bit among perhaps gigabits of data, will irrevocably alter that data and cast doubt on any evidence extracted. In traditional digital forensics write-blockers are used to preserve the integrity of that evidence and prevent changes from occurring, but virtual machine forensics presents more difficult challenges to address. Access to the digital storage device will probably not be possible, typically the only accessible storage will be a virtual hard disk drive. This will have the same integrity issues as those of a real device, but with the added complication that it is not possible to use a hardware write-blocker to prevent changes to those data. For this reason it is important to explore how to implement write-blocking mechanisms on a virtual device. In this paper we present an implementation of a software write-blocker and show how we can use it to be compliant with the 2nd ACPO principle on digital evidence.
Type of Material
Conference Publication
Publisher
IEEE
Subjects

Machine learning

Statistics

Digital forensics

Digital evidence

Write blocker

Virtual machine

DOI
10.1109/INTECH.2016.7845141
Language
English
Status of Item
Peer reviewed
Journal
2016 Sixth International Conference on Innovative Computing Technology (INTECH)
Conference Details
The Sixth International Conference on Innovative Computing Technology (INTECH 2016), Irish Computer Society, Dublin, Ireland, 24-26 August 2016
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
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insight_publication.pdf

Size

280.66 KB

Format

Adobe PDF

Checksum (MD5)

a06028fc17f686dad888120668e73442

Owning collection
Insight Research Collection
Mapped collections
Computer Science Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

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