5G cellular networks are particularly vulnerable against narrowband jammers that target specific control subchannels 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.

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[1] Matteo Varotto, "Detecting 5G Narrowband Jammers with CNN, k-nearest Neighbors, and Support Vector Machines", IEEE Dataport, 2024. [Online]. Available: http://dx.doi.org/10.21227/0cft-pj37. Accessed: Dec. 05, 2024.
@data{0cft-pj37-24,
doi = {10.21227/0cft-pj37},
url = {http://dx.doi.org/10.21227/0cft-pj37},
author = {Matteo Varotto },
publisher = {IEEE Dataport},
title = {Detecting 5G Narrowband Jammers with CNN, k-nearest Neighbors, and Support Vector Machines},
year = {2024} }
TY - DATA
T1 - Detecting 5G Narrowband Jammers with CNN, k-nearest Neighbors, and Support Vector Machines
AU - Matteo Varotto
PY - 2024
PB - IEEE Dataport
UR - 10.21227/0cft-pj37
ER -
Matteo Varotto. (2024). Detecting 5G Narrowband Jammers with CNN, k-nearest Neighbors, and Support Vector Machines. IEEE Dataport. http://dx.doi.org/10.21227/0cft-pj37
Matteo Varotto, 2024. Detecting 5G Narrowband Jammers with CNN, k-nearest Neighbors, and Support Vector Machines. Available at: http://dx.doi.org/10.21227/0cft-pj37.
Matteo Varotto. (2024). "Detecting 5G Narrowband Jammers with CNN, k-nearest Neighbors, and Support Vector Machines." Web.
1. Matteo Varotto. Detecting 5G Narrowband Jammers with CNN, k-nearest Neighbors, and Support Vector Machines [Internet]. IEEE Dataport; 2024. Available from : http://dx.doi.org/10.21227/0cft-pj37
Matteo Varotto. "Detecting 5G Narrowband Jammers with CNN, k-nearest Neighbors, and Support Vector Machines." doi: 10.21227/0cft-pj37