Machine Learning for Malware Evolution Detection

• URL: http://arxiv.org/abs/2107.01627v1
• Date: Sun, 4 Jul 2021 13:47:06 GMT
• Title: Machine Learning for Malware Evolution Detection
• Authors: Lolitha Sresta Tupadha and Mark Stamp
• Abstract summary: We perform experiments on a significant number of malware families to determine when malware evolution is likely to have occurred. We consider analysis based on hidden Markov models (HMM) and the word embedding techniques HMM2Vec and Word2Vec.
• Score: 4.111899441919165
• License: http://creativecommons.org/licenses/by/4.0/
• 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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