  Cellular networks are potential targets of jamming attacks to disrupt
wireless communications. Since the fifth generation (5G) of cellular networks
enables mission-critical applications, such as autonomous driving or smart
manufacturing, the resulting malfunctions can cause serious damage. This paper
proposes to detect broadband jammers by an online classification of
spectrograms. These spectrograms are computed from a stream of in-phase and
quadrature (IQ) samples of 5G radio signals. We obtain these signals
experimentally and describe how to design a suitable dataset for training.
Based on this data, we compare two classification methods: a supervised
learning model built on a basic convolutional neural network (CNN) and an
unsupervised learning model based on a convolutional autoencoder (CAE). After
comparing the structure of these models, their performance is assessed in terms
of accuracy and computational complexity.
