Studi Optimalisasi Deteksi Intrusi Jaringan NIDS Menggunakan XGBoost pada Dataset Netflow V2
DOI:
https://doi.org/10.55382/jurnalpustakaai.v6i1.1756Keywords:
NIDS, Cybersecurity, Machine Learning, XGBoost, NetworkingAbstract
This research is motivated by the increasing complexity of cyber attacks on modern networks, necessitating the need for an adaptive and accurate network intrusion detection system (NIDS) through the use of machine learning algorithms, specifically XGBoost. This research uses the NF-UQ-NIDS-v2 dataset with structured pre-processing stages, stratified data partitioning, and the development of an XGBoost-based multi-class classification model with optimized hyperparameter configurations. The test results show that the XGBoost model achieves an overall accuracy of 98.84% with excellent performance in the majority class, but still experiences a decrease in performance in the minority class due to data imbalance. The XGBoost-based NIDS model is proven to be effective and stable in detecting large-scale network attacks, although further strategies are needed to improve detection capabilities for rare types of attacks..
Downloads
References
P. Vanin, T. Newe, L. L. Dhirani, E. O’Connell, D. O’Shea, B. Lee, and M. Rao, “A Study of Network Intrusion Detection Systems Using Artificial Intelligence/Machine Learning,” Applied Sciences, vol. 12, no. 22, pp. 1–30, Nov. 2022.
E. Caville, W. W. Lo, S. Layeghy, and M. Portmann, “Anomal-E: A Self-Supervised Network Intrusion Detection System Based on Graph Neural Networks,” Knowledge-Based Systems, vol. 258, pp. 1–13, Dec. 2022.
E. Alhajjar, P. Maxwell, and N. Bastian, “Adversarial Machine Learning in Network Intrusion Detection Systems,” Expert Systems with Applications, vol. 186, pp. 1–15, Jan. 2021.
R. Ahmad, R. Wazirali, and T. Abu-Ain, “Machine Learning for Wireless Sensor Networks Security: An Overview of Challenges and Issues,” Sensors, vol. 22, no. 13, pp. 1–26, Jun. 2022.
Y. Song, H. Li, P. Xu, and D. Liu, “A Method of Intrusion Detection Based on WOA-XGBoost Algorithm,” Discrete Dynamics in Nature and Society, vol. 2022, pp. 1–12, 2022.
W. Xu and Y. Fan, “Intrusion Detection Systems Based on Logarithmic Autoencoder and XGBoost,” Security and Communication Networks, vol. 2022, pp. 1–11, 2022.
T.-T.-H. Le, Y. E. Oktian, and H. Kim, “XGBoost for Imbalanced Multiclass Classification-Based Industrial Internet of Things Intrusion Detection Systems,” Sustainability, vol. 14, no. 14, pp. 1–18, Jul. 2022.
M. Sarhan, S. Layeghy, and M. Portmann, “Towards a standard feature set for network intrusion detection system datasets,” Mobile Networks and Applications, pp. 1–14, 2022, doi: 10.1007/s11036-021-01843-0.
Purnamasari, E. ., & Asa Verano, D. . (2025). Model Data-Driven untuk Prediksi Digitalisasi UMKM Menggunakan GMM dan XGBoost. Jurnal Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence), 5(2), 204–214. https://doi.org/10.55382/jurnalpustakaai.v5i2.984
Aritonang, M. A. S., Abrar Masril, M. ., Chaniago, D. ., Marshall Al Karim, M. ., Mahani Cunis, V. ., & Surgiwe, S. (2023). Perancangan Sistem Pendeteksi Penyakit Pada Rumput Laut Dengan Menggunakan Metode Convolutional Neural Network. Jurnal Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence), 5(2), 408–417. https://doi.org/10.55382/jurnalpustakaai.v5i2.1160
Google Research, TPU Research Cloud (TRC) – About, https://sites.research.google/trc/about/ (accessed Jan. 8, 2026).
Aritonang, M. A. S., & Jonas Simanullang, M. (2025). Penerapan Network Monitoring System Berbasis SNMP untuk Deteksi Dini Gangguan Jaringan. Jurnal Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence), 5(3), 735–742. https://doi.org/10.55382/jurnalpustakaai.v5i3.1383
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Mhd Adi Setiawan Aritonang, Muhammad Marshall Al Karim, Roland Roland; Jefri Irwan Gultom; muel Enrico Sitompul, Hendri Hendri
This work is licensed under a Creative Commons Attribution 4.0 International License.
