Publication: Enhanced Feature Selection With Stacking Method For Ddos Attack Detection In Software Defined Networking Environment
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Date
2025-05
Authors
Alasfour, Tareq I. A. Alasfour
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Abstract
Software-defined networking (sdn) is a networking approach that separates the control plane from the data plane. However, the dynamic and programmable nature of sdns introduces new security challenges, particularly in detecting distributed denial of service (ddos) attacks. The proliferation of ddos attacks significantly threatens network accessibility and performance. Traditional feature selection methods struggle with the complexity of network traffic data, resulting in poor detection performance. To address this, we propose a genetic algorithm wrapper feature selection (gawfs) method. This approach integrates chi-squared (chi2) and genetic algorithm (ga) techniques with the kendall rank correlation method to select the most relevant features. Gawfs effectively reduces feature dimensions, eliminates redundancy, and identifies crucial correlated features for classification. To further enhance detection accuracy, we employ a stacking ensemble model. This model combines multi-layer perceptron (mlp) and support vector machine (svm) as base classifiers, with a random forest (rf) as the meta-classifier. Our proposed classifier achieves impressive accuracy rates of 99.86% for seen data and 98.89% for unseen data, representing improvements of approximately 5% and 40%, respectively, over previous studies. Additionally, the training time is reduced to 2,593 seconds, an improvement of approximately 29.92%.
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Keywords
Enhanced Feature Selection Stacking Method Attack