City Research Online

Abstract

The 5G network has improved Rogue Base Stations (RBS) detection by concealing subscription permanent identifiers, and other security mechanisms passed down from legacy network generations to 5G. However, even with these enhancements, 5G is still vulnerable to RBS threats. A malicious actor can broadcast a signal of sufficient power to impersonate legitimate base station, thereby deceiving the user devices into connecting to the rogue and potentially compromising confidential info. This work designs and implements a novel zero-trust-based network-side detection system for RBS threats that operates through the filtering of Measurement Reports (MRs). The proposed model introduces a probation period, during which candidate base stations are excluded from handover decisions while their legitimacy is continuously validated. We propose a trust analysis component to implement the RBS detector, initially using rule-based approach. The proposed model is augmented with an extended handover protocol that systematically excludes suspected RBS. We also show that the model employs a realistic dataset of radio signal measurements collected from a vehicle traversing various road segments. This scenario provides a practical use case demonstrating the framework’s potential to enhance mobile network protection. The suggested model’s performance results show that in the case of a big dataset comprising 1000 base stations and 180 rogue agents, the accuracy is 0.98, incorrectly classifying fourteen RBS as legitimate, and sets a new standard procedure for RBS identification.

Publication Type:	Article
Subjects:	H Social Sciences > HE Transportation and Communications Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TA Engineering (General). Civil engineering (General)
Departments:	School of Science & Technology School of Science & Technology > Department of Computer Science
SWORD Depositor:	Symplectic Administrator

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