Byeoungjun Min, Jihoon Yoo, Sangsoo Kim, Dongil Shin, Dongkyoo Shin, Journal of Internet Computing and Services, Vol. 22, No. 1, pp. 13-22, Feb. 2021
10.7472/jksii.2021.22.1.13, Full Text:  HTML
Keywords: Anomaly Detection, Network intrusion detection, Autoencoder, NSL-KDD

Abstract

Recently network based attack technologies are rapidly advanced and intelligent, the limitations of existing signature-based intrusion detection systems are becoming clear. The reason is that signature-based detection methods lack generalization capabilities for new attacks such as APT attacks. To solve these problems, research on machine learning-based intrusion detection systems is being actively conducted. However, in the actual network environment, attack samples are collected very little compared to normal samples, resulting in class imbalance problems. When a supervised learning-based anomaly detection model is trained with such data, the result is biased to the normal sample. In this paper, we propose to overcome this imbalance problem through One-Class Anomaly Detection using an auto encoder. The experiment was conducted through the NSL-KDD data set and compares the performance with the supervised learning models for the performance evaluation of the proposed method.

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