A Hybrid Data Mining Approach for Intrusion Detection on Imbalanced NSL-KDD Dataset
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2016, Vol 7, Issue 6
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
Authors and Affiliations
Mohammad Parsaei, Samaneh Rostami, Reza Javidan
Keywords
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- EP ID EP154284
- DOI 10.14569/IJACSA.2016.070603
- Views 106
- Downloads 0
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