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DOI: 10.14569/IJACSA.2016.070603
PDF

A Hybrid Data Mining Approach for Intrusion Detection on Imbalanced NSL-KDD Dataset

Author 1: Mohammad Reza Parsaei
Author 2: Samaneh Miri Rostami
Author 3: Reza Javidan

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 7 Issue 6, 2016.

  • Abstract and Keywords
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Abstract: Intrusion detection systems aim to detect malicious viruses from computer and network traffic, which is not possible using common firewall. Most intrusion detection systems are developed based on machine learning techniques. Since datasets which used in intrusion detection are imbalanced, in the previous methods, the accuracy of detecting two attack classes, R2L and U2R, is lower than that of the normal and other attack classes. In order to overcome this issue, this study employs a hybrid approach. This hybrid approach is a combination of synthetic minority oversampling technique (SMOTE) and cluster center and nearest neighbor (CANN). Important features are selected using leave one out method (LOO). Moreover, this study employs NSL KDD dataset. Results indicate that the proposed method improves the accuracy of detecting U2R and R2L attacks in comparison to the baseline paper by 94% and 50%, respectively

Keywords: intrusion detection system; feature selection; imbalanced dataset; SMOTE; NSL KDD

Mohammad Reza Parsaei, Samaneh Miri Rostami and Reza Javidan, “A Hybrid Data Mining Approach for Intrusion Detection on Imbalanced NSL-KDD Dataset” International Journal of Advanced Computer Science and Applications(IJACSA), 7(6), 2016. http://dx.doi.org/10.14569/IJACSA.2016.070603

@article{Parsaei2016,
title = {A Hybrid Data Mining Approach for Intrusion Detection on Imbalanced NSL-KDD Dataset},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2016.070603},
url = {http://dx.doi.org/10.14569/IJACSA.2016.070603},
year = {2016},
publisher = {The Science and Information Organization},
volume = {7},
number = {6},
author = {Mohammad Reza Parsaei and Samaneh Miri Rostami and Reza Javidan}
}



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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