Detecting IoT Botnet in 5G Core Network Using Machine Learning

As Internet of Things (IoT) devices with security issues are con-nected 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 detec-tion 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.