Detecting 5G Narrowband Jammers with CNN, k-nearest Neighbors, and Support Vector Machines
This addresses security vulnerabilities in 5G cellular networks for network operators, but it is incremental as it compares existing methods on new data.
The paper tackled the problem of detecting narrowband jamming attacks in 5G networks using machine learning for binary classification, finding that a convolutional neural network outperformed support vector machines and k-nearest neighbors in accuracy and computation time.
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.