Dong-Wook Kim, Gun-Yoon Shin, Ji-Young Yun, Sang-Soo Kim, Myung-Mook Han, Journal of Internet Computing and Services, Vol. 22, No. 3, pp. 45-52, Jun. 2021
10.7472/jksii.2021.22.3.45, Full Text:  HTML
Keywords: Unknown Attack, Discrete wavelet transform, Anomaly Detection, One-class SVM

Abstract

Although many studies have been conducted to identify unknown attacks in cyber security intrusion detection systems, studies based on outliers are attracting attention. Accordingly, we identify outliers by defining categories for unknown attacks. The unknown attacks were investigated in two categories: first, there are factors that generate variant attacks, and second, studies that classify them into new types. We have conducted outlier studies that can identify similar data, such as variants, in the category of studies that generate variant attacks. The big problem of identifying anomalies in the intrusion detection system is that normal and aggressive behavior share the same space. For this, we applied a technique that can be divided into clear types for normal and attack by discrete wavelet transformation and detected anomalies. As a result, we confirmed that the outliers can be identified through One-Class SVM in the data reconstructed by discrete wavelet transform.

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