Open Access
| Issue |
BIO Web Conf.
Volume 213, 2026
The 1st Papua International Conference on Biodiversity, Natural Sciences, and Technology (PICoBNST 2025)
|
|
|---|---|---|
| Article Number | 03001 | |
| Number of page(s) | 15 | |
| Section | Interdisciplinarity in Sciences and Technology | |
| DOI | https://doi.org/10.1051/bioconf/202621303001 | |
| Published online | 27 January 2026 | |
- M. Rahman, S. Al Shakil, and R. Mustakim, "A survey on intrusion detection system in IoT networks," Cyber Security and Applications, vol. 3, p. 100082, 2025, doi: 10.1016/j.csa.2024.100082. [Google Scholar]
- G. A. Mills, D. K. Acquah, and R. A. Sowah, "Network Intrusion Detection and Prevention System Using Hybrid Machine Learning with Supervised Ensemble Stacking Model," 2024, doi: 10.1155/2024/5775671. [Google Scholar]
- Nour Moustafa and Jill Slay, "UNSW-NB15: A comprehensive dataset for network intrusion detection systems. Australian Centre for Cyber Security (ACCS)." Accessed: Nov. 14, 2025. [Online]. Available: https://research.unsw.edu.au/projects/unsw-nb15-dataset [Google Scholar]
- I. Bibers, O. Arreche, and M. Abdallah, "A Comprehensive Comparative Study of Individual ML Models and Ensemble Strategies for Network Intrusion Detection Systems," Int J Inf Secur, 2024. [Google Scholar]
- S. A. Ajagbe, J. B. Awotunde, and H. Florez, "Intrusion Detection: A Comparison Study of Machine Learning Models Using Unbalanced Dataset," SN Comput Sci, vol. 5, no. 8, p. 1028, Nov. 2024, doi: 10.1007/s42979-024-03369-0. [Google Scholar]
- F. Abdou Vadhil, M. Lemine Salihi, and M. Farouk Nanne, "Machine learning-based intrusion detection system for detecting web attacks," IAES International Journal of Artificial Intelligence (IJ-AI), vol. 13, no. 1, p. 711, Mar. 2024, doi: 10.11591/ijai.v13.i1.pp711-721. [Google Scholar]
- J. Allgaier and R. Pryss, "Cross-Validation Visualized: A Narrative Guide to Advanced Methods," 2024, doi: 10.3390/make6020065. [Google Scholar]
- A. Hasan, K. Janabi, T. Kanakis, M. Johnson, and A. H. Janabi, "A Survey of Intrusion Detection Systems Based Machine Learning Approaches Applied to Software-Defined Networks (SDN): Research Issues and Challenges," 2023, doi: 10.20944/preprints202312.1449.v1. [Google Scholar]
- V. Z. Mohale and I. C. Obagbuwa, "Evaluating machine learning-based intrusion detection systems with explainable AI: enhancing transparency and interpretability," Front Comput Sci, vol. 7, 2025, doi: 10.3389/FCOMP.2025.1520741/FULL. [Google Scholar]
- Z. Yang et al., "A systematic literature review of methods and datasets for anomaly-based network intrusion detection," Comput Secur, vol. 116, p. 102675, 2022, doi: 10.1016/j.cose.2022.102675. [Google Scholar]
- A. Momand, S. U. Jan, and N. Ramzan, "Review Article A Systematic and Comprehensive Survey of Recent Advances in Intrusion Detection Systems Using Machine Learning: Deep Learning, Datasets, and Attack Taxonomy," 2023, doi: 10.1155/2023/6048087. [Google Scholar]
- J. Doménech, O. Leon, M. S. Siddiqui, and J. Pegueroles, "Evaluating and enhancing intrusion detection systems in IoMT: The importance of domain-specific datasets," 2025, doi: 10.1016/j.iot.2025.101631. [Google Scholar]
- University of New Brunswick, "NSL-KDD dataset for network intrusion detection systems." Accessed: Nov. 14, 2025. [Online]. Available: https://www.unb.ca/cic/datasets/nsl.html [Google Scholar]
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