Authors - Shayma W. Nourildean, Yousra Abd Mohammed, Nahida Naji Kadhim Abstract - The growth of the Internet of Things (IoT) equipment has changed many industrial and social applications, but it has made the IoT network vulnerable to a wide range of malicious activities by increasing the number of potential attack points. Traditional IDS has a problem with scalability, adaptability and accuracy when facing new and complex cyber threats. In this study, a strong ensemble machine learning framework that integrates Decision Tree (DT), Random Forest (RF), and XGBoost through confidence-based soft voting, had been validated across individual datasets. The proposed system (DTXG-RF) uses different forces and weaknesses of these algorithms to improve the accuracy of the detection and reduce the chances of causing it a false alarm. Two Benchmark IoT data sets, CIC-IoT2023and IoTID20, were used. These data sets showed a wide range of scenarios in the real IoT attack. To reduce overfitting risk and data leakage, strict train–test separation, stratified splitting, and pipeline-based preprocessing were enforced, and additional cross-validation experiments were conducted to verify model stability across folds and datasets. Assessment results showed that DTXG-RF ensemble voting model consistently improves traditional machine learning models such as DT, XGBOOST, KN, logistic regression, Nave Bayes and Catboost. The model accuracy of CIC -IoT-2023 and IOTID20 were 94.03% and 99.99%, respectively, with AUCs of 0.95025 and 0.9994. These results indicated that the ensemble IDS was lightweight and could achieve high detection accuracy with low latency and memory overheads, which is also suitable for low-latency IoT edge deployment.
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