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DOI: 10.14569/IJACSA.2019.0101286
PDF

Performance Analysis of Network Intrusion Detection System using Machine Learning

Author 1: Abdullah Alsaeedi
Author 2: Mohammad Zubair Khan

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 10 Issue 12, 2019.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: With the coming of the Internet and the increasing number of Internet users in recent years, the number of attacks has also increased. Protecting computers and networks is a hard task. An intrusion detection system is used to detect attacks and to protect computers and network systems from these attacks. This paper aimed to compare the performance of Random Forests, Decision Tree, Gaussian Na¨ıve Bayes, and Support Vector Machines in detecting network attacks. An up-to-date dataset was chosen to compare the performance of these classifiers. The results of the conducted experiments demonstrate that both Random Forests and Decision Tree performed effectively in detecting attacks.

Keywords: Intrusion Detection System (IDS); classifiers; AI; machine learning; KDD99; CICIDS2017; DoS; U2R; R2L

Abdullah Alsaeedi and Mohammad Zubair Khan, “Performance Analysis of Network Intrusion Detection System using Machine Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 10(12), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0101286

@article{Alsaeedi2019,
title = {Performance Analysis of Network Intrusion Detection System using Machine Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0101286},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0101286},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {12},
author = {Abdullah Alsaeedi and Mohammad Zubair Khan}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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