A Pre-processing Study to Solve the Problem of Rare Class Classification of Network Traffic Data


KIPS Transactions on Software and Data Engineering, Vol. 9, No. 12, pp. 411-418, Dec. 2020
https://doi.org/10.3745/KTSDE.2020.9.12.411,   PDF Download:
Keywords: Machine Learning, Rare Class, Semi Rare Class, pre-processing, Feature selection
Abstract

In the field of information security, IDS(Intrusion Detection System) is normally classified in two different categories: signature-based IDS and anomaly-based IDS. Many studies in anomaly-based IDS have been conducted that analyze network traffic data generated in cyberspace by machine learning algorithms. In this paper, we studied pre-processing methods to overcome performance degradation problems cashed by rare classes. We experimented classification performance of a Machine Learning algorithm by reconstructing data set based on rare classes and semi rare classes. After reconstructing data into three different sets, wrapper and filter feature selection methods are applied continuously. Each data set is regularized by a quantile scaler. Depp neural network model is used for learning and validation. The evaluation results are compared by true positive values and false negative values. We acquired improved classification performances on all of three data sets.


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Cite this article
[IEEE Style]
R. K. Joon, S. DongIl, S. DongKyoo, P. JeongChan, K. JinGoog, "A Pre-processing Study to Solve the Problem of Rare Class Classification of Network Traffic Data," KIPS Transactions on Software and Data Engineering, vol. 9, no. 12, pp. 411-418, 2020. DOI: https://doi.org/10.3745/KTSDE.2020.9.12.411.

[ACM Style]
Ryu Kyung Joon, Shin DongIl, Shin DongKyoo, Park JeongChan, and Kim JinGoog. 2020. A Pre-processing Study to Solve the Problem of Rare Class Classification of Network Traffic Data. KIPS Transactions on Software and Data Engineering, 9, 12, (2020), 411-418. DOI: https://doi.org/10.3745/KTSDE.2020.9.12.411.