A suspect-oriented intelligent and automated computer forensic analysis
- Submitting institution
-
University of Portsmouth
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 14800702
- Type
- D - Journal article
- DOI
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10.1016/j.diin.2016.08.001
- Title of journal
- Digital Investigation
- Article number
- -
- First page
- 65
- Volume
- 18
- Issue
- -
- ISSN
- 1742-2876
- Open access status
- Compliant
- Month of publication
- August
- Year of publication
- 2016
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
-
C - Cyber Security
- Citation count
- 10
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This is the first unsupervised machine learning method for grouping forensics artefacts with the aim to help forensic investigators in finding evidence. Traditionally, the burden to find and understand inter-relationships between artefacts is on the investigators. This method shows the promising potential of unsupervised machine learning to support digital forensic investigators in their work.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -