Publication Date
Spring 2015
Degree Type
Master's Project
Degree Name
Master of Science (MS)
Department
Computer Science
First Advisor
Mark Stamp
Second Advisor
Thomas Austin
Third Advisor
Fabio Di Troia
Keywords
clustering hmm malware detection
Abstract
In this research, we apply clustering techniques to the malware detection problem. Our goal is to classify malware as part of a fully automated detection strategy. We compute clusters using the well-known �-means and EM clustering algorithms, with scores obtained from Hidden Markov Models (HMM). The previous work in this area consists of using HMM and �-means clustering technique to achieve the same. The current effort aims to extend it to use EM clustering technique for detection and also compare this technique with the �-means clustering.
Recommended Citation
Pai, Swathi, "A Comparison of Clustering Techniques for Malware Analysis" (2015). Master's Projects. 404.
DOI: https://doi.org/10.31979/etd.nu7n-2cjh
https://scholarworks.sjsu.edu/etd_projects/404
