Volume 10 , Issue 1 , PP: 28–41, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Ahmed Aziz 1 * , Mahmoud Abdel-Salam 2
Doi: https://doi.org/10.54216/IJWAC.100103
As wireless sensor networks (WSNs) and mobile ad hoc networks (MANETs) are (DoS) attacks has become a critical security concern in mission-critical wireless (DoS) attacks has become a critical security issue. This paper proposes ADML-IDS, an Adaptive Machine Learning Intrusion Detection System that integrates ensemble of Random Forest, XGBoost and Gradient Boosting classifiers using a Flooding, and Scheduling—as well as normal traffic. Flooding, Scheduling and normal traffic. Experiments are conducted on the open-source WSN-DS dataset, which contains 166,000 network observations using the LEACH hierarchical routing protocol with 23 features obtained from NS-2 simulation. The data preprocessing steps include Min- Max normalisation and Synthetic Minority Over-Sampling Technique (SMOTE) to balance classes, and importance-based feature selection to retain 19 features. A rigorous ten-fold crossvalidation strategy is followed. ADML-IDS achieves an overall accuracy of 99.57%, weighted F1-score of 0.9956 and AUC-ROC of 0.9985. AUC-ROC of 0.9985, outperforming each of the sub-classifiers and five state-of-the-art methods. Scalability experiments demonstrate that the accuracy of detection remains above network size reaches 200 nodes, and with a reasonable computational cost. A formal presentation of the energy-aware network model and ensemble decision rule is tables are also included along with a full description of the algorithm tables.
Wireless sensor networks , Ad hoc networks , Intrusion detection system , Ensemble machine learning , DoS attacks , LEACH protocol , WSN-DS dataset , Random Forest , XGBoost , Soft-voting classifier
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