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

Thanh, Hoang Ngoc and Lang, Tran Van (2019) Use the ensemble methods when detecting DoS attacks in Network Intrusion Detection Systems. EAI Endorsed Transactions on Context-aware Systems and Applications, 6 (19): e5. ISSN 2409-0026

[img]
Preview
Text
eai.29-11-2019.163484.pdf - Published Version
Available under License Creative Commons Attribution No Derivatives.

Download (1MB) | Preview

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.

Item Type: Article
Uncontrolled Keywords: Machine Learning, Ensemble Classifier, Stacking, DoS, UNSW-NB15 dataset
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
Depositing User: EAI Editor II.
Date Deposited: 14 Sep 2020 11:20
Last Modified: 14 Sep 2020 11:20
URI: https://eprints.eudl.eu/id/eprint/283

Actions (login required)

View Item View Item