期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2019
卷号：10
期号：12
DOI：10.14569/IJACSA.2019.0101286
出版社：Science and Information Society (SAI)
摘要：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.
