Publication Date
Spring 2019
Degree Type
Master's Project
Degree Name
Master of Science (MS)
Department
Computer Science
First Advisor
Mark Stamp
Second Advisor
Thomas Austin
Third Advisor
Philip Heller
Keywords
Malware model, CNN, image HMM, SVM
Abstract
Automatically classifying similar malware families is a challenging problem. In this research, we attempt to classify malware families by applying machine learning to machine learning models. Specifically, we train hidden Markov models (HMM) for each malware family in our dataset. The resulting models are then compared in two ways. First, we treat the HMM matrices as images and experiment with convolutional neural networks (CNN) for image classification. Second, we apply support vector machines (SVM) to classify the HMMs. We analyze the results and discuss the relative advantages and disadvantages of each approach.
Recommended Citation
Sethi, Akriti, "Classification of Malware Models" (2019). Master's Projects. 703.
DOI: https://doi.org/10.31979/etd.mrqp-sur9
https://scholarworks.sjsu.edu/etd_projects/703
