One-Class Classification as GLRT for Jamming Detection in Private 5G Networks
This addresses security vulnerabilities in 5G networks for industry automation applications, but it is incremental as it adapts existing methods to a specific domain.
The paper tackles jamming detection in private 5G networks by proposing a CNN-based detector that implements a generalized likelihood ratio test (GLRT), trained with real legitimate signals and artificially generated jamming data, and demonstrates effectiveness using experimental data.
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.