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Detecting IoT Botnet in 5G Core Network Using Machine Learning

Ye-Eun Kim1, Min-Gyu Kim2, Hwankuk Kim2,*

1 Department of Electronics Information and System Engineering, Sangmyung University, Cheonan-si, Korea
2 Department of Information Security Engineering, Sangmyung University, Cheonan-si, Korea

* Corresponding Author: Hwankuk Kim. Email:

Computers, Materials & Continua 2022, 72(3), 4467-4488.


As Internet of Things (IoT) devices with security issues are connected to 5G mobile networks, the importance of IoT Botnet detection research in mobile network environments is increasing. However, the existing research focused on AI-based IoT Botnet detection research in wired network environments. In addition, the existing research related to IoT Botnet detection in ML-based mobile network environments have been conducted up to 4G. Therefore, this paper conducts a study on ML-based IoT Botnet traffic detection in the 5G core network. The binary and multiclass classification was performed to compare simple normal/malicious detection and normal/three-type IoT Botnet malware detection. In both classification methods, the IoT Botnet detection performance using only 5GC’s GTP-U packets decreased by at least 22.99% of accuracy compared to detection in wired network environment. In addition, by conducting a feature importance experiment, the importance of feature study for IoT Botnet detection considering 5GC network characteristics was confirmed. Since this paper analyzed IoT botnet traffic passing through the 5GC network using ML and presented detection results, think it will be meaningful as a reference for research to link AI-based security to the 5GC network.


Cite This Article

Y. Kim, M. Kim and H. Kim, "Detecting iot botnet in 5g core network using machine learning," Computers, Materials & Continua, vol. 72, no.3, pp. 4467–4488, 2022.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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