Machine Learning Based Intrusion Detection Systems for Class Imbalanced Datasets

Vol. 27, No. 6, pp. 1385-1395, Dec. 2017
10.13089/JKIISC.2017.27.6., Full Text:
Keywords: Intrusion Detection System, Machine Learning, Imbalanced Dataset
Abstract

This paper aims to develop an IDS (Intrusion Detection System) that takes into account class imbalanced datasets. For this, we first built a set of training data sets from the Kyoto 2006+ dataset in which the amounts of normal data and abnormal (intrusion) data are not balanced. Then, we have run a number of tests to evaluate the effectiveness of machine learning techniques for detecting intrusions. Our evaluation results demonstrated that the Random Forest algorithm achieved the best performances.

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Cite this article
[IEEE Style]
Y. Cheong, K. Park, H. Kim, J. Kim, S. Hyun, "Machine Learning Based Intrusion Detection Systems for Class Imbalanced Datasets," Journal of The Korea Institute of Information Security and Cryptology, vol. 27, no. 6, pp. 1385-1395, 2017. DOI: 10.13089/JKIISC.2017.27.6..

[ACM Style]
Yun-Gyung Cheong, Kinam Park, Hyunjoo Kim, Jonghyun Kim, and Sangwon Hyun. 2017. Machine Learning Based Intrusion Detection Systems for Class Imbalanced Datasets. Journal of The Korea Institute of Information Security and Cryptology, 27, 6, (2017), 1385-1395. DOI: 10.13089/JKIISC.2017.27.6..