Witcha ChimphleeSiriporn Chimphlee2025-03-102025-03-102023International Journal of Computer Networks and Communications975229310.5121/ijcnc.2023.154062-s2.0-85169072179https://repository.dusit.ac.th//handle/123456789/4567The paper proposes a two-phase classification method for detecting anomalies in network traffic, aiming to tackle the challenges of imbalance and feature selection. The study uses Information Gain to select relevant features and evaluates its performance on the CICIDS-2018 dataset with various classifiers. Results indicate that the ensemble classifier achieved the highest accuracy, precision, and recall. The proposed method addresses challenges in intrusion detection and highlights the effectiveness of ensemble classifiers in improving anomaly detection accuracy. Also, the quantity of pertinent characteristics chosen by Information Gain has a considerable impact on the F1-score and detection accuracy. Specifically, the Ensemble Learning achieved the highest accuracy of 98.36% and F1-score of 97.98% using the relevant selected features. © (2023), (Academy and Industry Research Collaboration Center (AIRCC)). All Rights Reserved.All Open Access; Gold Open AccessAnomaly DetectionCICIDS-2018 datasetFeature SelectionImbalance DataIntrusion Detection SystemArticleScopus