End-to-end PE malware detection with XGBoost and MalConv2. Adversarial robustness evaluation via GAMMA attack, SHAP interpretability, and multi-model Pareto comparison.
python machine-learning deep-learning static-analysis scikit-learn pytorch cybersecurity xgboost feature-engineering malware-detection adversarial-machine-learning security-evaluation shap explainability pe-analysis black-box-attack ember-dataset graduate-research malconv2 gamma-attack
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Updated
Jul 20, 2026 - Python