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