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.

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}
}