Random Forest for Malware Classification
Cryptography and Security
2016-09-27 v1 Machine Learning
Abstract
The challenge in engaging malware activities involves the correct identification and classification of different malware variants. Various malwares incorporate code obfuscation methods that alters their code signatures effectively countering antimalware detection techniques utilizing static methods and signature database. In this study, we utilized an approach of converting a malware binary into an image and use Random Forest to classify various malware families. The resulting accuracy of 0.9562 exhibits the effectivess of the method in detecting malware
Cite
@article{arxiv.1609.07770,
title = {Random Forest for Malware Classification},
author = {Felan Carlo C. Garcia and Felix P. Muga},
journal= {arXiv preprint arXiv:1609.07770},
year = {2016}
}