Volume 10 • Issue 2 • PP: 43–50 • 2026
WS-STACK: A Weighted Stacking Ensemble with Multi-Criteria Feature Selection for Multi-Class Traffic Classification and Anomaly Detection in Heterogeneous Wireless Sensor Networks
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© 2026 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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Heterogeneous Internet-of-Things deployments expose wireless sensor networks to a diverse and continuously evolving threat landscape encompassing distributed denial-of-service flooding, network reconnaissance scanning, and brute-force credential attacks. Existing intrusion detection approaches predominantly adopt single-classifier architectures and binary labelling, which are ill-suited to the multi-class, class-imbalanced traffic characteristic of real-world IoT sensor deployments. This paper proposes WS-STACK, a Weighted Stacking ensemble that combines five heterogeneous base learners—Random Forest, XGBoost, Support Vector Machine, K-Nearest Neighbours, and Gradient Boosting—under an ℓ2-regularised Logistic Regression meta-learner trained on cross validationgenerated probability features. A three-stage feature engineering pipeline comprising mutual information filtering, variance inflation factor pruning, and correlation-based elimination reduces the 83 dimensional RT-IoT2022 feature space to 20 informative features, and the Synthetic Minority Over-Sampling Technique corrects the six-fold class imbalance prior to training. Evaluated on 83,000 labelled network flow records from the publicly available RTIoT2022 benchmark spanning four benign traffic patterns and seven attack categories, WS-STACK achieves 99.61% classification accuracy, a weighted F1-score of 0.9960, and an AUC-ROC of 0.9978, outperforming every individual base classifier and five recently published state-of-the-art baselines. The false positive rate is reduced to 0.0006, and ten-fold cross-validation confirms μacc = 0.9959 (σ = 0.0004). Ablation experiments identify SMOTE as the single most critical preprocessing component, and noise robustness tests confirm 98.81% accuracy under 20% Gaussian feature perturbation. The framework is grounded through a formal variance-reduction proof and a channel-energy anomaly model that establishes the physical motivation for packet-rate features as the dominant intrusion detection signal in constrained wireless sensor networks.
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