Random Forest for Malware Classification

TL;DR

This paper presents a novel malware classification method that converts malware binaries into images and uses Random Forest to achieve high accuracy, effectively countering obfuscation techniques.

Contribution

It introduces a new approach of malware classification using image conversion and Random Forest, improving detection accuracy over traditional static methods.

Findings

Achieved 95.62% accuracy in malware classification

Effective against code obfuscation techniques

Demonstrates the viability of image-based malware analysis

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