MALPRED
Predictive Modeling for Malware Detection in Windows Systems using Ensemble Learning

Abstract

Malware infections are a pervasive issue for computers running the Windows operating system. In this study, we present a machine-learning based approach to predict the likelihood of malware infection in Windows machines. Our methodology involves conducting data pre-processing, feature engineering, and selection on the Microsoft Malware Prediction dataset. We then perform extensive experimentation using various machine learning algorithms and identify XGBoost, LightGBM and CatBoost as the 3 best-performing algorithms. Through hyperparameter tuning via the Tree-Structured Parzen Estimator and using a Meta Learner on top of our top 3 best-performing algorithms, our optimal novel model achieves an AUC score of 73.24% across Stratified 5-fold cross-validation, demonstrating the efficacy of our approach. Additionally, we develop a web-based interface enabling users to input their Windows machine specifications and obtain predictions regarding the probability of malware infection.