Research Article
A hybrid classification model in improving the classification quality of network intrusion detection systems
@ARTICLE{10.4108/eetcasa.6735, author={Thanh Ho\'{a}ng Ngọc}, title={A hybrid classification model in improving the classification quality of network intrusion detection systems}, journal={EAI Endorsed Transactions on Contex-aware Systems and Applications}, volume={10}, number={1}, publisher={EAI}, journal_a={CASA}, year={2025}, month={6}, keywords={Machine Learning, NIDS, Ensemble, Feature Selection, Resampling, UNSW-NB15}, doi={10.4108/eetcasa.6735} }- Thanh Hoàng Ngọc
Year: 2025
A hybrid classification model in improving the classification quality of network intrusion detection systems
CASA
EAI
DOI: 10.4108/eetcasa.6735
Abstract
Stream-based anomaly detection is an issue that continues to be researched in the cybersecurity environment. Much previous research has applied machine learning as a method to improve anomaly detection in network intrusion detection systems. Recent research shows that network intrusion detection systems still face challenges in improving accuracy, reducing false alarm rates, and detecting new attacks. The article proposes a hybrid classification model that combines improved data preprocessing techniques with ensemble techniques. Experimental results on the UNSW-NB15 dataset show that the proposed solutions have helped improve the classification quality of network intrusion detection systems compared to some other research.
Copyright © 2025 Hoang Ngoc Thanh et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
