aspg-20-4330-1791307640 20261006172720 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group International Journal of Wireless and Ad Hoc Communication IJWAC 2692-4056 2026 10 1 Ahmed Aziz Dean of the Engineering School, Central Asian University, Uzbekistan Mahmoud Abdel-Salam Faculty of Computer and Information Sciences, Mansoura University, Egypt 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"/> UnhideWhenUsed="true" QFormat="true" Name="heading 9"/> Name="index 1"/> Name="index 2"/> 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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;} 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. 2026 15 20 4330 https://creativecommons.org/licenses/by/4.0/ 10.54216/IJWAC.100103 https://americaspg.com/journal/20/article/4330 https://americaspg.com/storage/41779152272.pdf