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.
@article{arxiv.2405.09564,
title = {Detecting 5G Narrowband Jammers with CNN, k-nearest Neighbors, and Support Vector Machines},
author = {Matteo Varotto and Florian Heinrichs and Timo Schuerg and Stefano Tomasin and Stefan Valentin},
journal= {arXiv preprint arXiv:2405.09564},
year = {2024}
}