A Data-Adaptive Ensemble Machine Learning Framework for Accurate Malware Detection and Predictive Threat Analysis in Modern Computing Systems

Abstract

With the increasing volume and sophistication of cyber threats, detecting and classifying malware has become a critical challenge in cybersecurity. Traditional detection methods often rely on signature-based systems, which fail to identify newly emerging or obfuscated malware. The proposed system leverages advanced Machine Learning (ML) and Ensemble Learning techniques to accurately classify malicious and non-malicious applications. The process begins with detailed data analysis, feature extraction, and preprocessing to ensure reliable input for model training. Multiple ensemble algorithms such as Random Forest, XGBoost, LightGBM, and Gradient Boosting are compared to evaluate their effectiveness. A comprehensive comparison report and performance evaluation are generated using metrics like accuracy, precision, recall, and F1-score. This approach provides a robust and scalable framework for malware detection, improving overall system security and resilience against evolving cyber threats.