文章基本信息

标题：Improving Intrusion Detection for Imbalanced Network Traffic using Generative Deep Learning
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作者：Amani A. Alqarni ; El-Sayed M. El-Alfy
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2022
卷号：13
期号：4
DOI：10.14569/IJACSA.2022.01304109
语种：English
出版社：Science and Information Society (SAI)
摘要：Network security has become a serious issue since networks are vulnerable and subject to increasing intrusive activities. Therefore, network intrusion detection systems (IDSs) are an essential component to defend against these activities. One of the biggest issues encountered by IDSs is the class imbalance problem which leads to a biased performance by most machine learning models to normal activities (majority class). Several techniques were proposed to overcome the class-imbalance problem such as resampling, cost-sensitive, and en-semble learning techniques. Other issues related to intrusion detection data include mixed data types, and non-Gaussian and multimodal distributions. In this study, we employed a conditional tabular generative adversarial network (CTGAN) model with common machine learning algorithms to construct more effective detection systems while addressing the imbalance issue. CTGAN can generate samples of the minority class during training to make the dataset more balanced. To assess the effectiveness of the proposed IDS, we combined CTGAN with three machine learning algorithms: support vector machine (SVM), K-nearest neighbor (KNN), and decision tree (DT). The imbalanced NSL-KDD dataset was used and several experiments were conducted. The results showed that CTGAN can improve the performance of imbalance learning for intrusion detection with SVM and DT. On the other hand, KNN showed no improvement in the performance since it is less sensitive to the class imbalance problem. Moreover, the results proved that CTGAN can capture the distribution of discrete features better than continuous features.
关键词：Intrusion detection; machine learning; imbalance learning; conditional tabular generative adversarial networks