Abstract:This research focuses on studying the classification performance of a Machine Learning-based Intrusion Detection System (IDS) using the UNSW-NB15 dataset. The effectiveness of three classifiers - Decision Tree, Multilayer Perceptron (MLP), and XGBoost - was analyzed to determine their accuracy in identifying attacks and normal network traffic. The experimental results revealed that Decision Tree achieved an accuracy of 85%, MLP achieved an accuracy of 89.83%, and XGBoost achieved an accuracy of 89.9%. Addition… Show more
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