Digital Forensic Science

Roussev, Vassil

doi:10.1007/978-3-031-02351-4

Synthesis Lectures on Information Security, Privacy, and Trust

2017

DOI: 10.1007/978-3-031-02351-4

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Digital Forensic Science

Vassil Roussev

Abstract: He received his Master's degree at KMC, Manipal, and joined as faculty at KMC, Mangalore, where he has been teaching since 2005. He is presently appointed as the Honorary State Medico-Legal Consultant for three districts [Dakshina Kannada, Udupi and Coorg], Government of Karnataka. He has received his certificate in "Analytical Toxicology & Forensic DNA Typing" awarded by the

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Likelihood ratios for categorical count data with applications in digital forensics

Longjohn

Smyth

2022

Law, Probability and Risk

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We consider the forensic context in which the goal is to assess whether two sets of observed data came from the same source or from different sources. In particular, we focus on the situation in which the evidence consists of two sets of categorical count data: a set of event counts from an unknown source tied to a crime and a set of event counts generated by a known source. Using a same-source versus different-source hypothesis framework, we develop an approach to calculating a likelihood ratio. Under our proposed model, the likelihood ratio can be calculated in closed form, and we use this to theoretically analyse how the likelihood ratio is affected by how much data is observed, the number of event types being considered, and the prior used in the Bayesian model. Our work is motivated in particular by user-generated event data in digital forensics, a context in which relatively few statistical methodologies have yet been developed to support quantitative analysis of event data after it is extracted from a device. We evaluate our proposed method through experiments using three real-world event datasets, representing a variety of event types that may arise in digital forensics. The results of the theoretical analyses and experiments with real-world datasets demonstrate that while this model is a useful starting point for the statistical forensic analysis of user-generated event data, more work is needed before it can be applied for practical use.

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Likelihood ratios for categorical count data with applications in digital forensics

Longjohn

Smyth

2022

Law, Probability and Risk

View full text Add to dashboard Cite

show abstract

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Digital Forensic Science

Cited by 1 publication

References 6 publications

Likelihood ratios for categorical count data with applications in digital forensics

Likelihood ratios for categorical count data with applications in digital forensics

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