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RBS-MLP: A Deep Learning based Rogue Base Station Detection Approach for 5G Mobile Networks

Saedi, M. ORCID: 0000-0001-6436-1057, Moore, A., Perry, P. & Luo, C. RBS-MLP: A Deep Learning based Rogue Base Station Detection Approach for 5G Mobile Networks. IEEE Transactions on Vehicular Technology,


The 3GPP Security Group has identified the detection of Rogue Base Stations (RBS) in 5G networks as one of the leading security challenges for users and network infrastructure. Motivated by this, RBS-MLP, a novel deep learning model, has been developed to identify RBSs. The model uses signal strength measurements in each mobile device’s periodic measurement reports as input data, a reliable metric readily available to the system. We investigate the impacts of various sizes of datasets, different window sizes of received signal strength, and different proportional splits of the dataset into training and test data to evaluate the performance of the proposed model. We further demonstrate RBS-MLP using a realistic dataset of received signal strength measurements for a vehicle driving along various sections of a road, providing a use case to demonstrate the use of RBS-MLP to improve the safety of mobile networks. Experimental results reveal that RBS-MLP is well suited as a 99.999% accuracy classification model and provides a new baseline method for RBS detection.

Publication Type: Article
Additional Information: © 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher Keywords: Rogue Base Station (RBS), 5G Mobile Networks, Attack Detection, Vehicle Platooning, Machine Learning (ML), Received Signal Strength (RSS), Measurement Report (MR), gNodeB
Subjects: H Social Sciences > HE Transportation and Communications
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology
Departments: School of Science & Technology
School of Science & Technology > Computer Science
SWORD Depositor:
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