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Tutorial on Likelihood Ratios with Applications in Digital Forensics

Primary Author: Rachel Longjohn
Secondary Authors: Dr. Padhraic Smyth
Type: Webinar

This CSAFE webinar was held on September 15, 2022.

Presenters:
Rachel Longjohn
PhD Student – Department of Statistics, University of California, Irvine

Dr. Padhraic Smyth
Chancellor’s Professor – Departments of Statistics and Computer Sciences, University of California, Irvine

Presentation Description:

To date, digital forensics research has largely focused on extracting and reconstructing information from devices and the cloud. In comparison, there has been relatively little work on statistical methodologies that can be used to analyze such data after this step. In this webinar, we discuss statistical analyses in digital forensics, with a particular focus on likelihood ratios and ideas from Bayesian statistics. There are three parts to the webinar.

First, we begin with a general introduction to the concept of likelihood ratios. We show how they can be constructed mathematically, how they can be interpreted, and how they have been broadly applied in forensics. We discuss how strategies from Bayesian statistics can be incorporated into the statistical models used to construct the likelihood ratio and walk through simple motivating examples step-by-step.

Second, we discuss the development of likelihood ratios in the context of digital forensics. We consider the types of evidence available in digital forensics, the types of questions investigators may ask about this data, and how likelihood ratios can be used to address these questions. Building upon the first part of the webinar, we present a likelihood ratio-based method for analyzing digital evidence data which uses a Bayesian approach.

Lastly, we present results from applying these methods to real-world datasets related to digital evidence. We discuss these results, limitations of the method, and how future research can improve upon this approach.

 

Webinar Recording:

 

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