Volume 10 , Issue 2 , PP: 43–50, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Zainab Hussein Arif 1 , Nureize bt Arbaiy 2 *
Doi: https://doi.org/10.54216/IJWAC.100206
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.
Wireless sensor networks , IoT security , Ensemble learning , Stacking classifier , RT-IoT2022 dataset , Multi-class intrusion detection , Feature selection , SMOTE , Anomaly detection
[1] A. S. Balobaid, S. B. Ahamed, S. Shamsudheen, and S. Balamurugan, “Neural network clustering and swarm intelligence-based routing protocol for wireless sensor networks,” Wireless Communications and Mobile Computing, vol. 2023, p. 4758852, 2023, doi: 10.1155/2023/4758852.
[2] O. A. Khashan, N. M. Khafajah, W. Alomoush, and M. Alshinwan, “Innovative energy-efficient proxy reencryption for secure data exchange in wireless sensor networks,” IEEE Access, vol. 12, pp. 23 290–23 304, 2024, doi: 10.1109/ACCESS.2024.3360488.
[3] B. S. Sharmila and R. Nagapadma, “Quantized autoencoder (QAE) intrusion detection system for anomaly detection in resource-constrained IoT devices using RTIoT2022 dataset,” Cybersecurity, vol. 6, no. 1, p. 41, 2023, doi: 10.1186/s42400-023-00178-5.
[4] T. M. Nguyen, H. H.-P. Vo, and M. Yoo, “Enhancing intrusion detection in wireless sensor networks using a GSWO-CatBoost approach,” Sensors, vol. 24, no. 11, p. 3339, 2024, doi: 10.3390/s24113339.
[5] V. K. Pandey, S. Prakash, T. K. Gupta, P. Sinha, T. Yang, R. S. Rathore, L. Wang, S. Tahir, and S. T. Bakhsh, “Enhancing intrusion detection in wireless sensor networks using a Tabu search based optimized random forest,” Scientific Reports, vol. 15, pp. 1–19, 2025, doi: 10.1038/s41598-025-03498-3.
[6] J. B. Awotunde, F. E. Ayo, R. Panigrahi, A. Garg, A. K. Bhoi, and P. Barsocchi, “A multi-level random forest model-based intrusion detection using fuzzy inference system for Internet of Things networks,” International Journal of Computational Intelligence Systems, vol. 16, no. 1, p. 31, 2023, doi: 10.1007/s44196-023-00205-w.
[7] G. Liu, Z. Zhang, B. Jing, M. Zhang, and H. Li, “An enhanced intrusion detection model based on improved kNN in wireless sensor networks,” Sensors, vol. 22, no. 4, p. 1407, 2022, doi: 10.3390/s22041407.
[8] B. S. Sharmila and R. Nagapadma, “RT-IoT2022,” 2023, doi: 10.24432/C5QW3H.
[9] X. Zhong, Y. Liang, and Y. Li, “Energy-efficient and robust QoS control for wireless sensor networks using the extended Gur game,” Sensors, vol. 25, no. 3, p. 730, 2025, doi: 10.3390/s25030730.
[10] N. S. Albalawi, Y. Alzahrani, N. Alsalmi, Y. Patidar, and M. Tolani, “Energy-efficient priority encoding strategies using machine learning based hybrid MAC protocol for wireless sensor networks,” Scientific Reports, vol. 15, p. 45054, 2025, doi: 10.1038/s41598-025-31752-1.
[11] D. Godfrey, B. Suh, B. H. Lim, K. C. Lee, and K.-I. Kim, “An energy-efficient routing protocol with reinforcement learning in software-defined wireless sensor networks,” Sensors, vol. 23, no. 20, p. 8435, 2023, doi: 10.3390/s23208435.
[12] U. Srilakshmi, S. A. Alghamdi, V. A. Vuyyuru, N. Veeraiah, and Y. Alotaibi, “A secure optimization routing algorithm for mobile ad hoc networks,” IEEE Access, vol. 10, pp. 14 260–14 269, 2022, doi: 10.1109/ACCESS. 2022.3144679.
[13] M. Aljawarneh, R. Hamdaoui, A. Zouinkhi, S. Alangari, and M. N. Abdelkrim, “Energy optimization for wireless sensor network using minimum redundancy maximum relevance feature selection and classification techniques,” PeerJ Computer Science, vol. 10, p. e1997, 2024, doi: 10.7717/peerj-cs.1997.
[14] K. Rashid, Y. Saeed, A. Ali, F. Jamil, R. Alkanhel, and A. Muthanna, “An adaptive real-time malicious node detection framework using machine learning in vehicular ad-hoc networks (VANETs),” Sensors, vol. 23, no. 5, p. 2594, 2023, doi: 10.3390/s23052594.
[15] N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: Synthetic minority oversampling technique,” in Journal of Artificial Intelligence Research, vol. 16, 2002, pp. 321–357, doi: 10.1613/jair.953.
[16] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R.Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
