A project under Dr. Felix Muga II, which involves the problem of classifying malware families. One challenging aspect on malware classification using heuristic methods is the fact that malware developers typically employ the use of code polymorphism rendering signature based approach useless. Converting Malware binaries as image files and performing feature extraction allows the use of Machine Learning-based approach (Random Forest in this case) in classifying malwares families with hundreds of variants.
This repository contains both the Python code for data cleaning and R codes as required by the project for performing Machine Learning and 10-fold cross-validation.
Results
