Publication Date
Spring 2015
Degree Type
Master's Project
Degree Name
Master of Science (MS)
Department
Computer Science
First Advisor
Mark Stamp
Second Advisor
Jon Pearce
Third Advisor
Fabio Di Troia
Keywords
support vector machines metamorphic malware detection
Abstract
Metamorphic malware changes its internal structure with each infection, which makes it challenging to detect. In this research, we test several scor- ing techniques that have shown promise in metamorphic detection. We then perform a careful robustness analysis by employing morphing strategies that cause each score to fail. Finally, we show that combining scores using a Sup- port Vector Machine (SVM) yields results that are significantly more robust than we obtained using any of the individual scores.
Recommended Citation
Singh, Tanuvir, "Support Vector Machines and Metamorphic Malware Detection" (2015). Master's Projects. 409.
DOI: https://doi.org/10.31979/etd.43jb-raq4
https://scholarworks.sjsu.edu/etd_projects/409
