Machine Learning for Malware Evolution Detection

2021/07/04 by Lolitha Sresta Tupadha, Tupadha, Lolitha Sresta, Mark Stamp +1
Computer Science · #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Machine Learning (cs.LG) #cs.CR #cs.LG

paper · pdf · doi:10.48550/arxiv.2107.01627

arxiv created 2021/07/04 · arxiv updated 2021/07/06

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.

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