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  5. Event Reconstruction: A state of the art
 
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Event Reconstruction: A state of the art

Author(s)
Chabot, Yoan  
Bertaux, AurĂ©lie  
Nicolle, Christophe  
Kechadi, Tahar  
Uri
http://hdl.handle.net/10197/8520
Date Issued
2015-07
Date Available
2017-05-18T14:09:26Z
Abstract
Event reconstruction is one of the most important step in digital forensic investigations. It allows investigators to have a clear view of the events that have occurred over time. Event reconstruction is a complex task which requires exploration of a large amount of events due to the pervasiveness of new technologies nowadays. Any evidence produced at the end of the investigative process must also meet the requirements of the courts, such as reproducibility, verifiability, validation, etc. After defining the most important concepts of event reconstruction, a survey of the challenges of this field and solutions proposed so far is given in this chapter.
Sponsorship
Irish Research Council
Science Foundation Ireland
Other Sponsorship
National Natural Science Foundation of China
Science Foundation of Education Commission of Beijing
CAEP THz Science and Technology Foundation
Young Talent Project of Beijing University of Technology
Special Matching Project of Local Supported by Centre
Graduate Science and Technology Fund of Beijing University of Technology
Iraqi Ministry of Higher Education and Scientific Research
National Development Plan (NDP)
Type of Material
Book Chapter
Publisher
IGI Global
Subjects

Machine learning

Statistics

Terahertz imaging

Digital holography

Sparsity

DOI
10.4018/978-1-4666-6324-4.ch015
Language
English
Status of Item
Peer reviewed
Journal
Cruz-Cunha, M.M. and Portela, I.M. (eds.). Handbook of Research on Digital Crime, Cyberspace Security, and Information Assurance
ISBN
9781466663244
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
File(s)
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insight_publication.pdf

Size

385.52 KB

Format

Adobe PDF

Checksum (MD5)

4e683fdeb0c3a6adf221c822bc202955

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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