Master of Science in Computer Science (MSCS)
Fabio Di Troia
Malware Evolution, Hidden Markov models, word embeddings
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.
Tupadha, Lolitha Sresta, "Machine Learning to Detect Malware Evolution" (2021). Master's Projects. 1006.
Available for download on Wednesday, May 25, 2022