A Multi-Strategy Ensemble Learning Framework for Robust and Scalable Malware Detection in Cybersecurity

1.A universal ensemble system with high performance levels was developed according to metric measures and compared to the baseline ML and DL models on the EMBER dataset.
2.Designed an integrated system that uses RandomForest, Extra Trees, XGBoost, LightGBM, Hist-GradientBoosting and unified deep-learning ensembles to control bias-variance in a balanced way.
3. Both traditional and deep learning models incorporate soft-voting, stacking, and meta-learning to ensure adaptability across diverse malware feature spaces.
4. Demonstrated scalability using memory-mapped data handling and standardized preprocessing for large datasets