Evaluating the Performance of Machine Learning Classifiers for Network Intrusion Detection: A Comparative Study Using the UNSW-NB15 Dataset - Iwan Handoyo Putro (Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya, 2025)
Network security has become a critical concern in digital data transmission. It is because of their growing adoption and complexity of cyber-attacks. Therefore, protecting network infrastructures and identifying malicious behavior becomes a necessity. This paper gives a comparative performance analysis of multiple machine learning (ML) classifiers for intrusion detection systems (IDS) by using the UNSW-NB15 dataset. To gain better insight into the IDS performances, several ML classifiers are being assessed. This includes the Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Naïve Bayes (NB), and Gradient Boosting (XGB). The performance matrix in the analysis comprises the training score, accuracy, precision, recall, F1-score, training time, and AUC-ROC. The results reveal that DT and RF scored the highest training marks of 99.77%. Regarding accuracy, the RF model achieves the highest percentage at 95.05%. In terms of the computational time, k-NN displayed the lowest training time at 0.01 seconds. These analysis results provide guidance to the selection of appropriate ML-based cyberattack classification. It also provides insights for further research in ML-based cybersecurity systems to support the development of intelligent and efficient IDS solutions.