Abstract & Overview
The proliferation of Distributed Denial-of-Service (DDoS) attacks poses critical threats to network infrastructure, while conventional intrusion detection systems struggle to adapt to evolving attack patterns. Although ensemble learning methods achieve high accuracy on benchmark datasets, their opaque decision-making processes hinder deployment in Security Operations Centers (SOCs). To address this interpretability-performance gap, we propose an explainable machine learning framework integrating comparative benchmarking with quantitative interpretability analysis using the CIC-DDoS2019 dataset. Six supervised algorithms Decision Tree, Random Forest, XGBoost, LightGBM, Multilayer Perceptron, and Naïve Bayes were evaluated under standardized preprocessing protocols including random undersampling (50:50 class ratio), correlation-based feature selection (r > 0.9 threshold), and three-tier validation combining hold-out testing, train-validation splits, and 5-fold stratified cross-validation. LightGBM achieved optimal performance with 99.96% accuracy and F1-score of 0.9996, outperforming simple baselines by 0.35 percentage points while demonstrating superior computational efficiency. Beyond conventional performance metrics, we introduce the Feature Stability Score (FSS), a novel quantitative measure of SHAP-based feature importance consistency across validation folds. Spearman correlation analysis reveals a strong positive relationship between FSS and model robustness measured by cross-validation variance (ρ = 0.857, p = 0.014), establishing that stable feature attributions predict superior generalization. SHAP analysis identifies Flow Duration, Bwd Packet Length Mean, Fwd Packet Length Max, and Flow IAT Mean as dominant attack indicators. This integrated framework demonstrates that combining explainable AI with ensemble learning enables accurate, robust, and interpretable DDoS detection suitable for operational cybersecurity deployments.