A Detailed Analysis of the Multi-Class Classification Problem in Network Intrusion Detection using Resampling Techniques
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Machine learning algorithms are known to help identify cyberattacks such as network intrusion. However, common network intrusion datasets are often imbalanced. We conduct a detailed analysis on the impact of the different resampling techniques over different machine learning classifiers. We include more advanced resampling techniques, such as CGAN oversampling, in our study and compare its performance against other oversampling techniques. To further investigate CGAN-based oversampling potential, we examine the effect of CGAN with other standard machine learning classifiers on two different datasets. We do not recommend using CGAN in a dataset with extremely low samples in its minority classes based on our experimental results. Consequently, we also investigate the choice of minority class(es) to be oversampled in a dataset with low minority samples. Finally, the impact of the number of synthetic samples to be generated on the detection rate is evaluated on two different network intrusion datasets.
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Copyright © 2021 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
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- 2021
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ahsan-adetailedanalysisofthemulticlassclassification.pdf | 2023-05-05 | Public | Download |