Volume 15, Number 4

Intrusion Detection System(IDS) Development Using Tree-Based Machine Learning Algorithms

  Authors

Witcha Chimphlee and Siriporn Chimphlee, Suan Dusit University, Thailand

  Abstract

The 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.

  Keywords

Intrusion Detection System, Anomaly Detection,Imbalance Data, Feature Selection, CICIDS-2018 dataset