# Machine Learning for Malware Evolution Detection

**Authors:** Lolitha Sresta Tupadha, Mark Stamp  
**arXiv:** [2107.01627](https://arxiv.org/abs/2107.01627)  
**Date:** 2021-07-04  
**Summary page:** https://www.summarizepaper.com/en/paper/2107.01627v1/

## Key points

- Malware evolution is a significant challenge for antivirus software
- Traditional signature-based detection methods can be defeated by advanced forms of malware
- Machine learning and deep learning techniques are increasingly popular for detecting and analyzing malware
- Limited research on detecting malware evolution exists
- The authors explore various machine learning techniques (HMM, HMM2Vec, Word2Vec) for detecting when malware has evolved and requires new countermeasures
- Experiments are based on mnemonic opcodes extracted from the malware samples
- HMM-based techniques and Word2Vec provide powerful tools for automatically detecting evolutionary changes in malware
- Future work may consider other features that are less costly to extract or dynamic features that provide more information about evolutionary trends.
- Detecting when a malware family has evolved significantly is important so that appropriate countermeasures can be taken.
- Machine learning techniques offer an automated approach to identifying these changes without requiring labor-intensive manual analysis or reverse engineering.

## Summary

The evolution of malware poses a significant challenge to antivirus software, which must adapt to keep up with the changing threat landscape. In this research, the authors explore various machine learning techniques for detecting when malware has evolved and requires new countermeasures. The study involves experiments on a large number of malware families, using hidden Markov models (HMM) and word embedding techniques such as HMM2Vec and Word2Vec. The paper begins by providing background information on different types of malware, including computer worms, viruses, trojans, and backdoors. While traditional signature-based detection methods are effective against known threats, they can be defeated by obfuscation and morphing techniques used by more advanced forms of malware. Machine learning and deep learning techniques have become increasingly popular for detecting and analyzing malware; however, there is limited research on detecting malware evolution. The authors extend previous work in this area by exploring additional learning techniques for automatically detecting evolutionary changes in malware. They find that HMM-based techniques and Word2Vec provide powerful tools for this purpose. The experiments are based on mnemonic opcodes extracted from the malware samples; however, future work may consider other features that are less costly to extract or dynamic features that provide more information about evolutionary trends. In conclusion, the study demonstrates the importance of detecting when a malware family has evolved significantly so that appropriate countermeasures can be taken. Machine learning techniques offer an automated approach to identifying these changes without requiring labor-intensive manual analysis or reverse engineering. The authors suggest several potential avenues for future research in this area.