Research Article
Use the ensemble methods when detecting DoS attacks in Network Intrusion Detection Systems
@ARTICLE{10.4108/eai.29-11-2019.163484, author={Hoang Ngoc Thanh and Tran Van Lang}, title={Use the ensemble methods when detecting DoS attacks in Network Intrusion Detection Systems}, journal={EAI Endorsed Transactions on Context-aware Systems and Applications}, volume={6}, number={19}, publisher={EAI}, journal_a={CASA}, year={2019}, month={11}, keywords={Machine Learning, Ensemble Classifier, Stacking, DoS, UNSW-NB15 dataset}, doi={10.4108/eai.29-11-2019.163484} }- Hoang Ngoc Thanh
Tran Van Lang
Year: 2019
Use the ensemble methods when detecting DoS attacks in Network Intrusion Detection Systems
CASA
EAI
DOI: 10.4108/eai.29-11-2019.163484
Abstract
Building a good IDS model from a certain dataset is one of the main tasks in machine learning. Training multiple classifiers at the same time to solve the same problem and then combining their outputs to improve classification quality, called ensemble method. This paper analyzes and evaluates the performance of using known ensemble techniques such as Bagging, AdaBoost, Stacking, Decorate, Random Forest and Voting to detect DoS attacks on UNSW-NB15 dataset, created by the Australian Cyber Security Center 2015. The experimental results show that the Stacking technique with heterogeneous classifiers for the best classification quality with F − Measure is 99.28% compared to 98.61%, which is the best result are obtained by using single classifiers and 99.02% by using the Random Forest technique.
Copyright © 2019 Hoang Ngoc Thanh et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
