Use the ensemble methods when detecting DoS attacks in Network Intrusion Detection Systems

Authors

DOI:

https://doi.org/10.4108/eai.29-11-2019.163484

Keywords:

Machine Learning, Ensemble Classifier, Stacking, DoS, UNSW-NB15 dataset

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.

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Published

29-11-2019

How to Cite

1.
Ngoc Thanh H, Van Lang T. Use the ensemble methods when detecting DoS attacks in Network Intrusion Detection Systems. EAI Endorsed Trans Context Aware Syst App [Internet]. 2019 Nov. 29 [cited 2024 May 26];6(19):e5. Available from: https://publications.eai.eu/index.php/casa/article/view/1893