Publication Date
Spring 2021
Degree Type
Master's Project
Degree Name
Master of Science in Computer Science (MSCS)
Department
Computer Science
First Advisor
Mark Stamp
Second Advisor
Mike Wu
Third Advisor
Fabio Di Troia
Keywords
Malware Evolution, Hidden Markov models, word embeddings
Abstract
Malware evolves over time and anti-virus must adapt to such evolution. Hence, it is critical to detect those points in time where malware has evolved so that appro-priate countermeasures can be undertaken. In this research, we perform a variety of experiments to determine when malware evolution is likely to have occurred. All of the evolution detection techniques that we consider are based on machine learning and can be fully automated—in particular, no reverse engineering or other labor-intensive manual analysis is required. Specifically, we consider analysis based on hidden Markov models and various word embedding techniques, among other machine learning based approaches.
Recommended Citation
Tupadha, Lolitha Sresta, "Machine Learning to Detect Malware Evolution" (2021). Master's Projects. 1006.
DOI: https://doi.org/10.31979/etd.4vv2-panr
https://scholarworks.sjsu.edu/etd_projects/1006
