Publication Date
Spring 2015
Degree Type
Master's Project
Degree Name
Master of Science (MS)
Department
Computer Science
First Advisor
Mark Stamp
Second Advisor
Robert Chun
Third Advisor
Fabio Di Troia
Keywords
support vector machines clustering malware detection
Abstract
Previous work has shown that we can effectively cluster certain classes of mal- ware into their respective families. In this research, we extend this previous work to the problem of developing an automated malware detection system. We first compute clusters for a collection of malware families. Then we analyze the effectiveness of clas- sifying new samples based on these existing clusters. We compare results obtained using �-means and Expectation Maximization (EM) clustering to those obtained us- ing Support Vector Machines (SVM). Using clustering, we are able to detect some malware families with an accuracy comparable to that of SVMs. One advantage of the clustering approach is that there is no need to retrain for new malware families.
Recommended Citation
Narra, Usha, "Clustering versus SVM for Malware Detection" (2015). Master's Projects. 405.
DOI: https://doi.org/10.31979/etd.sgwj-a5ab
https://scholarworks.sjsu.edu/etd_projects/405
