Abstract
Recently, as the practical application of deep learning has become possible, research on the problems pertaining to intrusion detection has increased.
However, it is difficult to detect a small number of attack traffic when the real network is connected to produce an imbalance between the attack traffic class data and the normal traffic data necessary for learning. In this study, we propose a method to improve the accuracy of attack traffic data detection by creating similar attack traffic, using a Generative Adversarial Network (GAN) algorithm of deep learning. The proposed method generates similar attack traffic for NSL–KDD, ISCX 2012, and USTC_TFC 2016 datasets, which are well-known intrusion detection learning data sets. Experiments have shown that the data imbalance in each data set can improve classification accuracy by 10–12%, owing to the degradation problem.
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Lee, W.H., Lim, C.S., Noh, B.N. (2020). Generation of Similar Traffic Using GAN for Resolving Data Imbalance. In: Park, J., Park, DS., Jeong, YS., Pan, Y. (eds) Advances in Computer Science and Ubiquitous Computing. CUTE CSA 2018 2018. Lecture Notes in Electrical Engineering, vol 536. Springer, Singapore. https://doi.org/10.1007/978-981-13-9341-9_1
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DOI: https://doi.org/10.1007/978-981-13-9341-9_1
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