Publication Date

Spring 5-25-2021

Degree Type

Master's Project

Degree Name

Master of Science in Computer Science (MSCS)

Department

Computer Science

First Advisor

Mark Stamp

Second Advisor

William Andreopoulos

Third Advisor

Fabio Di Troia

Abstract

Automated techniques to classify malware samples into their respective families are critical in cybersecurity. Previously research applied 𝑘-means clustering to scores generated by hidden Markov models (HMM) as a means of dealing with the malware classification problem. In this research, we follow a somewhat similar approach, but instead of using HMMs to generate scores, we directly cluster the HMMs themselves. We obtain good results on a challenging malware dataset.

Available for download on Wednesday, May 25, 2022

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