  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.
