Random Forest for Malware Classification
This addresses malware detection for cybersecurity, but it is incremental as it applies an existing method to a new data representation.
The study tackled malware classification by converting malware binaries into images and using Random Forest, achieving an accuracy of 0.9562.
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