# Random Forest for Malware Classification

arXiv: https://arxiv.org/abs/1609.07770 · HTML: https://mlsanity.com/paper/1609.07770/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2016.

## Built on by (most cited first, 4 of 4)

- [Generative Adversarial Networks and Image-Based Malware Classification](https://mlsanity.com/paper/2207.00421/) (2022)
- [A Natural Language Processing Approach to Malware Classification](https://mlsanity.com/paper/2307.11032/) (2023)
- [From Malware Samples to Fractal Images: A New Paradigm for Classification. (Version 2.0, Previous version paper name: Have you ever seen malware?)](https://mlsanity.com/paper/2212.02341/) (2022)
- [Malware Classification using a Hybrid Hidden Markov Model-Convolutional Neural Network](https://mlsanity.com/paper/2412.18932/) (2024)

## Abstract (arXiv)

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