Date of Award
2026
Type
Thesis
Major
Master of Science
Degree Type
Applied Computer Science
Department
TSYS School of Computer Science
First Advisor
Dr. Lydia Ray
Second Advisor
Dr. Yesem Kurt-Peker
Third Advisor
Dr. Rahmatullah Roche
Abstract
The analysis of binary files is a critical component of antivirus software and is one of the most important tools for incident response teams across the industry. In the field, malware is often obfuscated, a practice in which the compilation process is transformed with different techniques to hinder decompilation and reverse engineering. Artificial Intelligence and Machine Learning techniques can assist, but models need to be trained on well constructed datasets first. This paper outlines a pipeline for creating such a dataset and builds a proof-of-concept machine learning classification model. All associated data and code are supplied in the project GitHub repository at https://github.com/Papaya-Messiah/GOOBER.
Recommended Citation
Wilmink, Luka, "A PIPELINE FOR CREATING OBFUSCATED BINARY SAMPLES TO TRAIN AI-POWERED DETECTION MODELS" (2026). Theses and Dissertations. 803.
https://csuepress.columbusstate.edu/theses_dissertations/803
