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Comparative Analysis of Oversampling Techniques and Feature Selection for Intrusion Detection
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Publication Year
2024-11
Journal
한국통신학회논문지
Publisher
한국통신학회
Citation
한국통신학회논문지, Vol.49 No.11, pp.1510-1524
Keyword
Intrusion DetectionClass Imbalanced DataOversamplingFeature Selection
Abstract
To accurately detect and defend against ever-evolving cyber-attacks, network security technologies using artificial intelligence are continually advancing. This study analyzed the effective network intrusion detection methods based on the CICIDS2017 dataset, which contains various types of network attacks and has a highly imbalanced class distribution. To enhance detection performance for the minority classes of attacks, five oversampling techniques, including SMOTE, Borderline-SMOTE, ADASYN, GAN, and BiGAN, were applied to the underrepresented Bot and Infiltration classes. Additionally, the impact of feature selection on classification performance was evaluated by selecting features based on the feature importance scores from each machine learning model: Random Forest and XGBoost. The experimental results demonstrated that oversampling with SMOTE and ADASYN improved the recall scores of minority classes. Furthermore, applying feature selection reduced the model's complexity while maintaining or even improving its accuracy.
ISSN
1226-4717
Language
Eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36085
DOI
https://doi.org/10.7840/kics.2024.49.11.1510
Type
Article
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Kim, Minkyung Image
Kim, Minkyung김민경
Dasan University College
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