With the evolution of 5G wireless communications, the Synchronization Signal
Block (SSB) plays a critical role in the synchronization of devices and
accessibility of services. However, due to the predictable nature of SSB
transmission, including the Primary and Secondary Synchronization Signals (PSS
and SSS), jamming attacks are critical threats. By leveraging RF domain
knowledge, this work presents a novel deep learning-based technique for
detecting jammers in 5G networks. Unlike the existing jamming detection
algorithms that mostly rely on network parameters, we introduce a double
threshold deep learning jamming detector by focusing on the SSB. The detection
method is focused on RF domain features and improves the robustness of the
network without requiring integration with the pre-existing network
infrastructure. By integrating a preprocessing block that extracts PSS
correlation and energy per null resource elements (EPNRE) characteristics, our
method distinguishes between normal and jammed received signals with high
precision. Additionally, by incorporation of Discrete Wavelet Transform (DWT),
the efficacy of training and detection are optimized. A double threshold double
Deep Neural Network (DT-DDNN) is also introduced to the architecture
complemented by a deep cascade learning model to increase the sensitivity of
the model to variations of signal to jamming noise ratio (SJNR). Results show
that the proposed method achieves 96.4% detection rate in extra low jamming
power, i.e., SJNR between 15 to 30 dB which outperforms the single threshold
DNN design with 86.0% detection rate and unprocessed IQ sample DNN design with
83.2% detection rate. Ultimately, performance of DT-DDNN is validated through
the analysis of real 5G signals obtained from a practical testbed,
demonstrating a strong alignment with the simulation results.

使用深度学习和 RF 领域特征提取的双阈值检测方法，通过提取 PSS 相关性和 EPNRE 特征，用于区分正常和受干扰的 5G 信号，进一步优化了训练和检测效果，并通过实际测试结果验证了 DT-DDNN 的性能。

用于 5G 射频领域中干扰检测的无线空中双阈值深度学习器

Over-The-Air Double-Threshold Deep Learner for Jamming Detection in 5G  RF domain

Jamming and intrusion detection are critical in 5G research, aiming to
maintain reliability, prevent user experience degradation, and avoid
infrastructure failure. This paper introduces an anonymous jamming detection
model for 5G based on signal parameters from the protocol stacks. The system
uses supervised and unsupervised learning for real-time, high-accuracy
detection of jamming, including unknown types. Supervised models reach an AUC
of 0.964 to 1, compared to LSTM models with an AUC of 0.923 to 1. However, the
need for data annotation limits the supervised approach. To address this, an
unsupervised auto-encoder-based anomaly detection is presented with an AUC of
0.987. The approach is resistant to adversarial training samples. For
transparency and domain knowledge injection, a Bayesian network-based causation
analysis is introduced.

本文介绍了一个基于 5G 协议栈中信号参数的匿名干扰检测模型，该系统通过监督学习和无监督学习实时高精度地检测干扰，包括未知类型干扰。监督模型的 AUC 为 0.964 至 1，相比之下，LSTM 模型的 AUC 为 0.923 至 1。然而，需要数据标注限制了监督方法的应用。为解决这个问题，本文提出了一种无监督的基于自编码器的异常检测方法，其 AUC 为 0.987。该方法对抗对抗性训练样本具有抵抗性。为了提高透明度和领域知识注入，还引入了基于贝叶斯网络的因果分析。