Abstract—The increasing application of machine learning to network intrusion detection has shown great successes in the recent years. To reduce the high dimension of captured network records for classifiers to learn efficiently, various feature reduction methods have been proposed. Using the Information Bottleneck framework, a concise and discriminative feature representation can be derived by optimizing an intuition and theoretically grounded objective function. In this paper, we propose a novel Variational Information Bottleneck method for Network Intrusion Detection that can produce high-quality low-dimensional representation to increase the classification accuracy of conventional machine learning classifier. Moreover, the proposed model is shown to achieve promising accuracy scores in comparison to several state-of-the-art anomaly-based IDS, on both the 2-class classification and 5-class classification intrusion detection problem.
 

Index Terms—Intrusion detection, information bottleneck, deep learning, variational inference, NSL-KDD

Cite: Vu Van Thieu, Nguyen The Anh, and Tran Hoang Hai, "A Variational Information Bottleneck Method for Network Intrusion Detection," Journal of Communications vol. 17, no. 11, pp. 933-940, November 2022. Doi: 10.12720/jcm.17.11.933-940