Cited By
View all- Akremi A(2022)A forensic-driven data model for automatic vehicles events analysisPeerJ Computer Science10.7717/peerj-cs.8418(e841)Online publication date: 5-Jan-2022
An Adaptative and Compliant Forensics Admissibility Metrics Generation Methodology
Pages 495 - 503
Abstract
The paper aims to solve the lack or absence of effective metrics that reflect the sized system’s forensics readiness, providing intrinsic weights to the gathered evidence. This paper proposes a new adaptive methodology to elicit forensics metrics that measure the evidence admissibility attributes. It considers the legislation difference between countries and required procedures. It also helps in measuring the computing system forensics soundness to generate admissible evidence. The article presents an application of the methodology within Cloud computing services as a demonstration example.
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Published In
November 2021
658 pages
ISBN:9781450395564
DOI:10.1145/3487664
Copyright © 2021 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 30 December 2021
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- Research-article
- Research
- Refereed limited
Funding Sources
- Deanship of scientific research at Umm Al-Qura University
Conference
iiWAS2021
iiWAS2021: The 23rd International Conference on Information Integration and Web Intelligence
November 29 - December 1, 2021
Linz, Austria
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Cited By
View all- Akremi A(2022)A forensic-driven data model for automatic vehicles events analysisPeerJ Computer Science10.7717/peerj-cs.8418(e841)Online publication date: 5-Jan-2022
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