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

arXiv: https://arxiv.org/abs/2405.09565 · HTML: https://mlsanity.com/paper/2405.09565/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 7 May 2024.

## Builds on (most cited first, 4 of 4)

- [Jamming Attacks and Anti-Jamming Strategies in Wireless Networks: A Comprehensive Survey](https://mlsanity.com/paper/2101.00292/) (2021)
- [Physical Layer Authentication and Security Design in the Machine Learning Era](https://mlsanity.com/paper/2305.09748/) (2023)
- [Learning The Likelihood Test With One-Class Classifiers for Physical Layer Authentication](https://mlsanity.com/paper/2210.12494/) (2022)
- [On the Optimal Spoofing Attack and Countermeasure in Satellite Navigation Systems](https://mlsanity.com/paper/2302.01841/) (2023)

## Built on by (most cited first, 2 of 2)

- [Model-Driven Learning-Based Physical Layer Authentication for Mobile Wi-Fi Devices](https://mlsanity.com/paper/2603.19972/) (2026)
- [Jamming Detection in Cell-Free MIMO with Dynamic Graphs](https://mlsanity.com/paper/2601.06075/) (2025)

## Abstract (arXiv)

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.