Machine Learning for Malware Evolution Detection

Authors

Lolitha Sresta Tupadha, San Jose State University
Mark Stamp, San Jose State UniversityFollow

Publication Date

1-1-2022

Document Type

Contribution to a Book

Publication Title

Advances in Information Security

Volume

54

DOI

10.1007/978-3-030-97087-1_8

First Page

183

Last Page

213

Abstract

Malware evolves over time and antivirus must adapt to such evolution. Hence, it is critical to detect those points in time where malware has evolved so that appropriate countermeasures can be undertaken. In this research, we perform a variety of experiments on a significant number of malware families 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 (HMM) and the word embedding techniques HMM2Vec and Word2Vec.

Department

Computer Science

Recommended Citation

Lolitha Sresta Tupadha and Mark Stamp. "Machine Learning for Malware Evolution Detection" Advances in Information Security (2022): 183-213. https://doi.org/10.1007/978-3-030-97087-1_8