Harnessing Machine Learning to Enhance Malware Classification: A Focus on Ensemble Techniques

In the ever-evolving domain of cybersecurity, mal-ware detection and classification are critical for safeguarding digital infrastructure. This study explores the application of various machine learning algorithms, including K-Nearest Neighbors (KNN), Random Forest, Logistic Regression, and XGBoost, to classify malware using the Microsoft Malware Classification Challenge (BIG 2015) dataset. The dataset consists of diverse malware families, each exhibiting unique characteristics. A comprehensive preprocessing framework was applied, including data cleaning, feature extraction from binary and assembly files, and the creation of new features to improve model accuracy. Our experimental results indicate that Random Forest and XGBoost significantly outperform other models, achieving accuracies as high as 98%. Logistic Regression provided strong interpretability, making it suitable for applications where understanding model decisions is crucial. This study demonstrates the effectiveness of ensemble learning models in malware classification and highlights their potential for integration into real-world cybersecurity solutions, ensuring timely detection and prevention of cyber threats.