CCE Theses and Dissertations

Date of Award

2022

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

College of Computing and Engineering

Advisor

Sumitra Mukherjee

Committee Member

Michael J. Laszlo

Committee Member

Francisco J. Mitropoulos

Abstract

The widespread use of the digital media in committing crimes, and the steady increase of their storage capacity has created backlogs at digital forensic labs. The problem is exacerbated especially in high profile crimes. In many such cases the judicial proceedings mandate full analysis of the digital media, when doing so is rarely accomplished or practical. Prior studies have proposed different phases for forensic analysis, to lessen the backlog issues. However, these phases are not distinctly differentiated, and some proposed solutions may not be practical. This study utilized several past police forensic analyses. Each case was chosen for having five distinct forensic phases, complete with documented amount of time spent in each phase, along with the number and type of recovered evidence. Data from these cases were empirically analyzed using common descriptive statistical analyses along with linear regression. By using linear regression, we tested the factors that determine the number of recovered evidentiary artifacts.

This study provides models by which future forensic analyses could be assessed. It presents distinctive boundaries for each forensics phase, thus eliminating ambiguity in the examination results, while assisting forensic examiners in determining the necessary depth of analysis.

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