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

TL;DR

This paper compares CNN, k-NN, and SVM machine learning models for detecting narrowband jamming in 5G networks, highlighting their accuracy and computational efficiency based on experimental data.

Contribution

It introduces a machine learning-based method for real-time detection of 5G narrowband jammers at the physical layer, comparing multiple classifiers on experimental data.

Findings

CNN achieved higher accuracy than SVM and k-NN.

Support Vector Machines and k-NN combined with PCA reduced computation time.

Significant differences in classification accuracy and speed among models.

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.