Hybrid Machine Learning for Zero-Day Malware Detection: An Adaptive Static-Dynamic Analysis Approach
Date of Award
5-2026
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Electrical Engineering and Computer Science
First Advisor
Abdullah Aydeger
Second Advisor
Jignya Patel
Third Advisor
Michael C. King
Fourth Advisor
Brian A. Lail
Abstract
In an era of swiftly evolving cyber threats, zero-day malware continues to be one of the most challenging classes of attacks to detect and mitigate. Traditional signature-based methods often fail to detect novel malicious code, leaving institutions vulnerable to unknown exploits. This thesis proposes a machine learning (ML)-based framework that is designed to detect unknown malware variants. By combining both static and dynamic techniques, such as file structure exam ination and sandbox-based runtime analysis, this approach aims to successfully capture malicious characteristics. The proposed custom pipeline addresses the computational overhead that is associ ated with deep inspections, outlining a staged detection mechanism that aims to balance efficiency and in-depth analysis. The staged detection will first perform a quick static check and if it fails or recognizes anomalies, it will then move to a more resource-intensive dynamic analysis.
Recommended Citation
Farmer, Garrett, "Hybrid Machine Learning for Zero-Day Malware Detection: An Adaptive Static-Dynamic Analysis Approach" (2026). Theses and Dissertations. 1650.
https://repository.fit.edu/etd/1650