Publication Date
Fall 2020
Degree Type
Master's Project
Degree Name
Master of Science (MS)
Department
Computer Science
First Advisor
Mark Stamp
Second Advisor
Teng Moh
Third Advisor
Samanvitha Basole
Keywords
Malware, Gaussian mixture model-HMMs, opcode sequences, entropy-based sequence
Abstract
Discrete hidden Markov models (HMM) are often applied to the malware detection and classification problems. However, the continuous analog of discrete HMMs, that is, Gaussian mixture model-HMMs (GMM-HMM), are rarely considered in the field of cybersecurity. In this study, we apply GMM-HMMs to the malware classification problem and we compare our results to those obtained using discrete HMMs. As features, we consider opcode sequences and entropy-based sequences. For our opcode features, GMM-HMMs produce results that are comparable to those obtained using discrete HMMs, whereas for our entropy-based features, GMM-HMMs generally improve on the classification results that we can attain with discrete HMMs.
Recommended Citation
Zhao, Jing, "Malware Classification with Gaussian Mixture Model-Hidden Markov Models" (2020). Master's Projects. 967.
DOI: https://doi.org/10.31979/etd.8sxr-8wj6
https://scholarworks.sjsu.edu/etd_projects/967
