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

arXiv: https://arxiv.org/abs/2405.09564 · HTML: https://mlsanity.com/paper/2405.09564/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 19 references that are in the map.
- First posted on arXiv 7 May 2024.

## Builds on (most cited first, 3 of 3)

- [Jamming Attacks and Anti-Jamming Strategies in Wireless Networks: A Comprehensive Survey](https://mlsanity.com/paper/2101.00292/) (2021)
- [Multi-stage Jamming Attacks Detection using Deep Learning Combined with Kernelized Support Vector Machine in 5G Cloud Radio Access Networks](https://mlsanity.com/paper/2004.06077/) (2020)
- [Narrowband Interference Detection via Deep Learning](https://mlsanity.com/paper/2301.09607/) (2023)

## Built on by (most cited first, 2 of 2)

- [A Two-Stage CAE-Based Federated Learning Framework for Efficient Jamming Detection in 5G Networks](https://mlsanity.com/paper/2501.15288/) (2025)
- [Jamming Detection in Cell-Free MIMO with Dynamic Graphs](https://mlsanity.com/paper/2601.06075/) (2025)

## Abstract (arXiv)

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.