Enhancing cyber security by predicting malwares using supervised machine learning models

Abstract

Malware poses a severe threat to computer systems and networks. Quick and accurate detection of malware is crucial to mitigating its detrimental impacts. This study aimed to develop a machine learning model to accurately classify whether a Portable Executable (P.E.) file is malware or benign. Supervised classification algorithms like Random Forest, K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), Decision Tree, Multinomial Naïve Bayes, and Logistic Regression were trained on a dataset of 10,868 PE files. Each file had extracted static features like file headers, entropy, string literals, metadata, etc. The algorithms were evaluated using accuracy, precision, recall, and F1 scores. Random Forest performed the best with 99% accuracy, 0.99 precision, 1.00 recall, and a 0.99 F1 score. The features were ranked by importance, with the top ones providing the most discriminatory power. The finalized Random Forest model was saved for operationalization to classify unknown P.E. files automatically.In conclusion, machine learning, especially ensemble tree-based methods, proves highly efficacious for malware detection with the proper feature engineering of file content and characteristics. The model has promising capabilities as an anti-malware system to identify and nullify malware attacks proactively. Further research can focus on generalizability testing across different file types and integration with antivirus solutions.