Optimized Network Intrusion Detection Using XGBoost with Hyperparameter Tuning: An Empirical Study on UNSW-NB15 Dataset

This paper presents an enhanced approach to network intrusion detection using XGBoost (eXtreme Gradient Boosting) with optimized hyperparameters through Bayesian optimization. We evaluate our method on the UNSW-NB15 dataset, achieving state-of-the-art performance with an accuracy of 99.67% and an F1-score of 0.9883. Our approach demonstrates superior detection capabilities through comprehensive feature engineering and automated hyperparameter optimization, offering a robust solution for modern network security challenges