Using Bayesian Networks to Quantify Digital Forensic Evidence and Hypotheses
Duration: 43 mins 38 secs
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About this item
| Description: |
Overill, R
Tuesday 27th September 2016 - 15:30 to 16:15 |
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| Created: | 2016-10-05 11:50 |
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| Collection: | Probability and Statistics in Forensic Science |
| Publisher: | Isaac Newton Institute |
| Copyright: | Overill, R |
| Language: | eng (English) |
| Distribution: |
World
(downloadable)
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| Explicit content: | No |
| Aspect Ratio: | 16:9 |
| Screencast: | No |
| Bumper: | UCS Default |
| Trailer: | UCS Default |
| Abstract: | In what appears to be an increasingly litigious age, courts, legal officials and law enforcement officers in a number of adversarial legal jurisdictions are starting to look for quantitative indications of (i) the probative value (or weight) of individual items of digital evidence connected with a case; and (ii) the relative plausibility of competing hypotheses (or narratives) purporting to explain how the recovered items of digital evidence (traces) were created.
In this presentation, we review the contributions that Bayesian Networks are capable of making to the understanding, analysis and evaluation of crimes whose primary items of evidence are digital in nature, and show how as a consequence they may fulfill both of the two above desiderata. |
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