Degree Type
Thesis
Date of Award
2013
Degree Name
Master of Science
Department
Electrical and Computer Engineering
First Advisor
Yong Guan
Abstract
Visualization of complex data, such as a file system or file, allows a forensic analyst or reverse engineer to rapidly locate areas of interest amidst a large quantity of data. While visualization provides a promising form of analysis, is the subject of much skepticism, as human interaction is required in order for this method to be successful. As
a result of this, visualization methods face two major obstacles: tediousness and time.
As our contribution, we propose a unique method of graphing visual information into a measurable format suitable for use with machine learning algorithms. This method will still utilize the visual layout of the data but streamline this form into one that can be
rapidly processed by a machine.
In this work we examine existing methods of file fragment analysis, determine how to apply visualization to this analysis, and transform this visual data into a measurable format for machine leaning algorithms using our tool called VMIFF (Visualization Metrics for the Identification of File Fragments). In its breadth, this work aims to demonstrate that such transformations will still yield meaningful results.
Copyright Owner
Ellen J. Hartstack
Copyright Date
2013
Language
en
File Format
application/pdf
File Size
70 pages
Recommended Citation
Hartstack, Ellen J., "VMIFF - Visualization metrics for the identification of file fragments" (2013). Graduate Theses and Dissertations. 13131.
https://lib.dr.iastate.edu/etd/13131
