Dimensions overall score 4.0

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• Lack of feature compatibility in public datasets limits generalization and transferability of Machine Learning (ML) detection approaches for malware.limitation 95%
• The study evaluates the suitability of different data preprocessing approaches for the detection of Portable Executable (PE) files with ML models.method 95%
• The preprocessing pipeline unifies EMBERv2 features datasets and trains paired models under two training setups: EMBER + BODMAS and EMBER + BODMAS + ERMDS.method 95%
• Models trained with EMBER + BODMAS and EMBER + BODMAS + ERMDS are tested against TRITIUM, INFERNO, and SOREL-20M datasets.method 95%
• ERMDS is also used for testing for the EMBER + BODMAS setup.method 95%
• Compact boosting static detectors are applicable for on-host use but require careful analysis of PE obfuscation effects on feature distributions.result 90%
• Principal Component Analysis (PCA) or XGBoost Feature Selection (XGBFS) are applied to unified datasets to produce feature vectors of 128/256/384 dimensions.technical 95%
• FLAML is used for hyperparameter tuning of LightGBM, XGBoost, Extra Trees, and Random Forest models trained on reduced vector dimensions.method 95%

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