Comparative Analysis of Classification Algorithms on KDD'99 Data Set
Автор: Iknoor Singh Arora, Gurpriya Kaur Bhatia, Amrit Pal Singh
Журнал: International Journal of Computer Network and Information Security(IJCNIS) @ijcnis
Статья в выпуске: 9 vol.8, 2016 года.
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Due to the enormous growth of network based services and the need for secure communications over the network there is an increasing emphasis on improving intrusion detection systems so as to detect the growing network attacks. A lot of data mining techniques have been proposed to detect intrusions in the network. In this paper study of two different classification algorithms has been carried out: Naïve Bayes and J48. Results obtained after applying these algorithms on 10% of the KDD'99 dataset and on 10% of the filtered KDD'99 dataset are compared and analyzed based on several performance metrics. Comparison between these two algorithms is also done on the basis of the percentage of correctly classified instances of different attack categories present in both the datasets as well as the time they take to build their classification models.Overall J48 is a better classifier compared to Naïve Bayes on both the datasets but it is slow in building the classification model.
Intrusion detection system, Naïve Bayes, J48, DD`99(Knowledge Discovery and Data Mining)
Короткий адрес: https://sciup.org/15011575
IDR: 15011575
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