Year of Award
2023
Document Type
Thesis
Degree Type
Master of Science (MS)
Degree Name
Computer Science
Department or School/College
Computer Science
Committee Chair
David Opitz
Commitee Members
David Opitz, Andrew Ware, Patricia Duce
Keywords
malware, vulnerability, transfer learning, machine learning, cyber security, binary
Publisher
University of Montana
Subject Categories
Data Science | Statistical Models
Abstract
Malware detection and vulnerability detection are important cybersecurity tasks. Previous research has successfully applied a variety of machine learning methods to both. However, despite their potential synergies, previous research has yet to unite these two tasks. Given the recent success of transfer learning in many domains, such as language modeling and image recognition, this thesis investigated the use of transfer learning to improve vulnerability detection. Specifically, we pre-trained a series of models to detect malicious binaries and used the weights from those models to kickstart the detection of vulnerable binaries. In our study, we also investigated five different data representations of portable executable binaries, all but one of which showed positive transfer in at least one experiment. The single-channel image and tf-idf assembly instruction count embedding were particularly successful, increasing the accuracy of a non- transfer randomly initialized model from 77.2% to 95.8%.
Recommended Citation
McNulty, Sean Patrick, "APPLICATIONS OF TRANSFER LEARNING FROM MALICIOUS TO VULNERABLE BINARIES" (2023). Graduate Student Theses, Dissertations, & Professional Papers. 12187.
https://scholarworks.umt.edu/etd/12187
Included in
© Copyright 2023 Sean Patrick McNulty