aspg-20-4364-1791307621 20261006172701 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group International Journal of Wireless and Ad Hoc Communication IJWAC 2692-4056 2026 10 2 Zainab Hussein Arif College of Nursing, University of Al-Qadisiyah, Al-Qadisiyah Province, 58002, Iraq Nureize bt Arbaiy Fakulti Sains Komputer dan Teknologi Maklumat, Universiti Tun Hussein Onn Malaysia (UTHM), 86400 Batu Pahat, Johor, Malaysia Normal false false false EN-US X-NONE AR-SA DefSemiHidden="false" DefQFormat="false" DefPriority="99" LatentStyleCount="371"> UnhideWhenUsed="true" QFormat="true" Name="heading 2"/> UnhideWhenUsed="true" QFormat="true" Name="heading 3"/> UnhideWhenUsed="true" QFormat="true" Name="heading 4"/> UnhideWhenUsed="true" QFormat="true" Name="heading 5"/> UnhideWhenUsed="true" QFormat="true" Name="heading 6"/> UnhideWhenUsed="true" QFormat="true" Name="heading 7"/> UnhideWhenUsed="true" QFormat="true" Name="heading 8"/> 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Accent 3"/> Name="Grid Table 7 Colorful Accent 3"/> Name="Grid Table 1 Light Accent 4"/> Name="Grid Table 6 Colorful Accent 4"/> Name="Grid Table 7 Colorful Accent 4"/> Name="Grid Table 1 Light Accent 5"/> Name="Grid Table 6 Colorful Accent 5"/> Name="Grid Table 7 Colorful Accent 5"/> Name="Grid Table 1 Light Accent 6"/> Name="Grid Table 6 Colorful Accent 6"/> Name="Grid Table 7 Colorful Accent 6"/> Name="List Table 1 Light Accent 1"/> Name="List Table 6 Colorful Accent 1"/> Name="List Table 7 Colorful Accent 1"/> Name="List Table 1 Light Accent 2"/> Name="List Table 6 Colorful Accent 2"/> Name="List Table 7 Colorful Accent 2"/> Name="List Table 1 Light Accent 3"/> Name="List Table 6 Colorful Accent 3"/> Name="List Table 7 Colorful Accent 3"/> Name="List Table 1 Light Accent 4"/> Name="List Table 6 Colorful Accent 4"/> Name="List Table 7 Colorful Accent 4"/> Name="List Table 1 Light Accent 5"/> Name="List Table 6 Colorful Accent 5"/> Name="List Table 7 Colorful Accent 5"/> Name="List Table 1 Light Accent 6"/> Name="List Table 6 Colorful Accent 6"/> Name="List Table 7 Colorful Accent 6"/> /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:8.0pt; mso-para-margin-left:0in; line-height:107%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri",sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Arial; mso-bidi-theme-font:minor-bidi;} 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. 2026 43 50 4364 https://creativecommons.org/licenses/by/4.0/ 10.54216/IJWAC.100206 https://americaspg.com/journal/20/article/4364 https://americaspg.com/storage/71779220682.pdf