• Journal of Internet Computing and Services
    ISSN 2287 - 1136(Online) / ISSN 1598 - 0170 (Print)
    http://jics.or.kr/

Network Intrusion Detection with One Class Anomaly Detection Model based on Auto Encoder


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:
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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Cite this article
[APA Style]
Byeoungjun Min, Jihoon Yoo, Sangsoo Kim, Dongil Shin, & Dongkyoo Shin (2021). Network Intrusion Detection with One Class Anomaly Detection Model based on Auto Encoder. Journal of Internet Computing and Services, 22(1), 13-22. DOI: 10.7472/jksii.2021.22.1.13.

[IEEE Style]
B. Min, J. Yoo, S. Kim, D. Shin and D. Shin, "Network Intrusion Detection with One Class Anomaly Detection Model based on Auto Encoder," Journal of Internet Computing and Services, vol. 22, no. 1, pp. 13-22, 2021. DOI: 10.7472/jksii.2021.22.1.13.

[ACM Style]
Byeoungjun Min, Jihoon Yoo, Sangsoo Kim, Dongil Shin, and Dongkyoo Shin. 2021. Network Intrusion Detection with One Class Anomaly Detection Model based on Auto Encoder. Journal of Internet Computing and Services, 22, 1, (2021), 13-22. DOI: 10.7472/jksii.2021.22.1.13.