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@article{193014,
        author = {JOHN OJO AJAYI and Adesina Simon Sodiya and Mustapha Aminu Bagiwa and Ismaili Idris Sinan},
        title = {Comparative Performance Analysis of Both Traditional and Ensemble Machine Learning for Intrusion Detection System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {3783-3791},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=193014},
        abstract = {The increasing complexity of cyberattacks makes it more challenging to use traditional machine learning (ML) techniques to reliably identify intrusions. Failure to accurately detect intrusions could lead to a decline in the confidence placed in security services. This paper presents both ensemble machine learning methods, including Random Forest (RF), Light Gradient Boosting Machine (LGBM), and Extreme Gradient Boosting (XGBoost), as well as traditional machine learning techniques, including K-Nearest Neighbors (KNN), Naive Bayes (NB), and Decision Tree (DT), using the UNSW-NB15 dataset from the Australian Centre for Cyber Security's Cyber Range Lab to classify data as either intrusion or normal. The findings of this study show that the accuracy of NB is 51.38%, KNN is 89.75%, DT is 89.57%, RF is 91.51%, LGBM is 91.34%, and XGBoost is 91.57%. These results indicate that the XGBoost classifier performed the best in detecting intrusions within the scope of this study, outperforming the other five models considered. This research demonstrates that the XGBoost ensemble strategy exhibited exceptional performance, surpassing both traditional machine learning algorithms and other ensemble methods.},
        keywords = {decision tree, ids, k-nearest neighbour, lgbm, machine learning, naïve bayes, random forest, and XGBoost.},
        month = {February},
        }