A Stacking Ensemble Model with Enhanced Feature Selection for Distributed Denial-of-Service Detection in Software-Defined Networks DOI Open Access
Tariq Emad Ali, Yung-Wey Chong,

Selvakumar Manickam

и другие.

Engineering Technology & Applied Science Research, Год журнала: 2025, Номер 15(1), С. 19232 - 19245

Опубликована: Фев. 2, 2025

The proliferation of Distributed Denial Service (DDoS) attacks poses a significant threat to network accessibility and performance. Traditional feature selection methods struggle with the complexity traffic data, leading poor detection To address this issue, Genetic Algorithm Wrapper Feature Selection (GAWFS) is proposed, integrating Chi-squared (GA) approaches correlation method select most correlated features. GAWFS effectively reduces dimensions, eliminates redundancy, identifies crucial features for classification. Detection accuracy further improved by employing stacking ensemble model, combining Multi-Layer Perceptron (MLP) Support Vector Machine (SVM) as base models, Random Forest (RF) metamodel. proposed classifier achieves impressive accuracies 99.86% training data 98.89% test representing improvements approximately 5% 40%, respectively, over previous studies. time was also reduced 2,593 s, substantial improvement 29.92%. Validation on various benchmark datasets confirmed efficacy approach, underscoring importance enhanced model against DDoS attacks.

Язык: Английский

A Stacking Ensemble Model with Enhanced Feature Selection for Distributed Denial-of-Service Detection in Software-Defined Networks DOI Open Access
Tariq Emad Ali, Yung-Wey Chong,

Selvakumar Manickam

и другие.

Engineering Technology & Applied Science Research, Год журнала: 2025, Номер 15(1), С. 19232 - 19245

Опубликована: Фев. 2, 2025

The proliferation of Distributed Denial Service (DDoS) attacks poses a significant threat to network accessibility and performance. Traditional feature selection methods struggle with the complexity traffic data, leading poor detection To address this issue, Genetic Algorithm Wrapper Feature Selection (GAWFS) is proposed, integrating Chi-squared (GA) approaches correlation method select most correlated features. GAWFS effectively reduces dimensions, eliminates redundancy, identifies crucial features for classification. Detection accuracy further improved by employing stacking ensemble model, combining Multi-Layer Perceptron (MLP) Support Vector Machine (SVM) as base models, Random Forest (RF) metamodel. proposed classifier achieves impressive accuracies 99.86% training data 98.89% test representing improvements approximately 5% 40%, respectively, over previous studies. time was also reduced 2,593 s, substantial improvement 29.92%. Validation on various benchmark datasets confirmed efficacy approach, underscoring importance enhanced model against DDoS attacks.

Язык: Английский

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