Master of Science in Computer Science (MSCS)
Fabio Di Troia
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.
Singh, Shamli, "Hidden Markov Model-based Clustering for Malware Classification" (2021). Master's Projects. 1008.
Available for download on Wednesday, May 25, 2022