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http://dx.doi.org/10.6109/jkiice.2022.26.12.1872

A Data Sampling Technique for Secure Dataset Using Weight VAE Oversampling(W-VAE)  

Kang, Hanbada (Department of Convergence Security, Chung-Ang University)
Lee, Jaewoo (Department of Industrial Security, Chung-Ang University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.26, no.12, 2022 , pp. 1872-1879 More about this Journal
Abstract
Recently, with the development of artificial intelligence technology, research to use artificial intelligence to detect hacking attacks is being actively conducted. However, the fact that security data is a representative imbalanced data is recognized as a major obstacle in composing the learning data, which is the key to the development of artificial intelligence models. Therefore, in this paper, we propose a W-VAE oversampling technique that applies VAE, a deep learning generation model, to data extraction for oversampling, and sets the number of oversampling for each class through weight calculation using K-NN for sampling. In this paper, a total of five oversampling techniques such as ROS, SMOTE, and ADASYN were applied through NSL-KDD, an open network security dataset. The oversampling method proposed in this paper proved to be the most effective sampling method compared to the existing oversampling method through the F1-Score evaluation index.
Keywords
AI; Information Security; Over Sampling; VAE;
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Times Cited By KSCI : 3  (Citation Analysis)
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