One-Class Classification as GLRT for Jamming Detection in Private 5G Networks

• URL: http://arxiv.org/abs/2405.09565v1
• Date: Tue, 7 May 2024 14:02:34 GMT
• Title: One-Class Classification as GLRT for Jamming Detection in Private 5G Networks
• Authors: Matteo Varotto, Stefan Valentin, Francesco Ardizzon, Samuele Marzotto, Stefano Tomasin,
• Abstract summary: 5G mobile networks are vulnerable to jamming attacks that may jeopardize valuable applications such as industry automation. We propose to analyze radio signals with a dedicated device to detect jamming attacks.
• Score: 5.237876638041339
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 5G mobile networks are vulnerable to jamming attacks that may jeopardize valuable applications such as industry automation. In this paper, we propose to analyze radio signals with a dedicated device to detect jamming attacks. We pursue a learning approach, with the detector being a CNN implementing a GLRT. To this end, the CNN is trained as a two-class classifier using two datasets: one of real legitimate signals and another generated artificially so that the resulting classifier implements the GLRT. The artificial dataset is generated mimicking different types of jamming signals. We evaluate the performance of this detector using experimental data obtained from a private 5G network and several jamming signals, showing the technique's effectiveness in detecting the attacks.

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