Detecting 5G Narrowband Jammers with CNN, k-nearest Neighbors, and Support Vector Machines

2024/05/07 by Matteo Varotto, Florian Heinrichs, Varotto, Matteo +7 · 1 citation
Computer Science · Engineering · #Biometric Identification and Security #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Networking and Internet Architecture (cs.NI) #Radar Systems and Signal Processing #Signal Processing (eess.SP) #Wireless Signal Modulation Classification #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2405.09564

openalex publication_date 2024/05/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

5G cellular networks are particularly vulnerable against narrowband jammers that target specific control sub-channels in the radio signal. One mitigation approach is to detect such jamming attacks with an online observation system, based on machine learning. We propose to detect jamming at the physical layer with a pre-trained machine learning model that performs binary classification. Based on data from an experimental 5G network, we study the performance of different classification models. A convolutional neural network will be compared to support vector machines and k-nearest neighbors, where the last two methods are combined with principal component analysis. The obtained results show substantial differences in terms of classification accuracy and computation time.

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