A Study on the Implementation of a Network Function for Real-time False Base Station Detection for the Next Generation Mobile Communication Environment

Abstract

The threat posed by false base stations remains pertinent across the 4G, 5G, and forthcoming 6G generations of mobile communication. In response, this paper introduces a real-time detection method for false base stations employing two approaches: machine learning and specification-based. Utilizing the UERANSIM open 5G RAN (Radio-Access Network) test platform, we assess the feasibility and practicality of applying these techniques within a 5G network environment. Emulating real-world 5G conditions, we implement a functional split in the 5G base station and deploy the False Base Station Detection Function (FDF) as a 5G NF (Network Function) within the CU (Central Unit). This setup enables real-time detection seamlessly integrated into the network. Experimental results indicate that specification-based detection outperforms machine learning, achieving a detection accuracy of 99.6% compared to 96.75% for the highest-performing machine learning model XGBoost. Furthermore, specification-based detection demonstrates superior efficiency, with CPU usage of 1.2% and memory usage of 16.12MB, compared to 1.6% CPU usage and 182.4MB memory usage for machine learning on average. Consequently, the implementation of specification-based detection using the proposed method emerges as the most effective technique in the 5G environment.