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What is Imbalanced Network Attack Classification Techniques about?

This research addresses the challenges of multiclass imbalanced classification in network attack detection by proposing two novel machine learning approaches. The Multi-Selective Synthetic Sample Integration (MS3I) method, which combines SMOTE with XGBoost, significantly improves detection accuracy for minority classes, achieving a macro-average F1-score of 0.62 and a weighted F1-score of 0.83. Additionally, the framework utilizing hyperparameter tuning and ensemble learning techniques resulted in a top-per

Author

Malik Nauman

Language

EN