Random Forest for Malware Classification

Understand

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