Fifth-generation (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 convolutional neural network (CNN) implementing a generalized likelihood ratio test (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.

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

Ardizzon, Francesco;Tomasin, Stefano
2024

Abstract

Fifth-generation (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 convolutional neural network (CNN) implementing a generalized likelihood ratio test (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.
2024
Proc. of 2024 IEEE 25th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
2024 IEEE 25th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3535164
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