Malware category classification using machine learning algorithms (Random Forest, XGBoost, k-NN, Naive Bayes and MLP).

Python 3.6.8 was used during this project.

pip install -r requirements.txt

The n-grams implementation used some code from the following repository: https://github.com/kaanege/malware

The dataset used for this project can be found via: https://github.com/naisofly/Static-Malware-Analysis

• Set input data directories for both feature extraction methods (common.py for n-grams and feature_extraction.py for header analysis)

• If a different dataset is being used than the one above, then any new subdirectories must be specified in feature_extraction.py.

• Run main.sh (byte_features_extractor.py requires the following format)

python3 src/n-grams/byte_features_extractor.py [ngram e.g. 2gram] [number of cores on your machine]