用生成对抗网络增强数据，提升5G信号干扰检测精度。
CWGAN-GP Augmented CAE for Jamming Detection in 5G-NR in Non-IID Datasets
- 用CWGAN-GP生成少数类样本，解决数据不平衡问题。
- 在非独立同分布数据上实现94.35%准确率，优于对比模型。
- 适合通信安全领域研究者，尤其关注信号异常检测的人。
在不断扩展的5G-NR无线蜂窝网络中，空中接口的干扰攻击日益普遍，会损害接收信号质量。本文通过向真实世界的I/Q OFDM数据集添加加性高斯白噪声（AWGN）模拟干扰环境。采用卷积自编码器（CAE）对异构的I/Q数据集进行干扰检测，提取同步信号块（SSB）信息，并处理少样本观测与显著类别不平衡问题。为解决数据不平衡，利用一维条件水印生成对抗网络-梯度惩罚（Conv1D CWGAN-GP）对多数类和少数类的SSB观测进行数据增强，获得平衡数据集。进一步将所提CAE模型在增强数据上的性能与基准模型——卷积去噪自编码器（CDAE）和卷积稀疏自编码器（CSAE）进行对比。尽管数据异构性复杂，该方法仍表现出鲁棒性，在多个指标上取得平均97.33%精度、91.33%召回率、94.08%F1分数和94.35%准确率，优于基线模型。
原文摘要 · Abstract (English)
In the ever-expanding domain of 5G-NR wireless cellular networks, over-the-air jamming attacks are prevalent as security attacks, compromising the quality of the received signal. We simulate a jamming environment by incorporating additive white Gaussian noise (AWGN) into the real-world In-phase and Quadrature (I/Q) OFDM datasets. A Convolutional Autoencoder (CAE) is exploited to implement a jamming detection over various characteristics such as heterogenous I/Q datasets; extracting relevant information on Synchronization Signal Blocks (SSBs), and fewer SSB observations with notable class imbalance. Given the characteristics of datasets, balanced datasets are acquired by employing a Conv1D conditional Wasserstein Generative Adversarial Network-Gradient Penalty(CWGAN-GP) on both majority and minority SSB observations. Additionally, we compare the performance and detection ability of the proposed CAE model on augmented datasets with benchmark models: Convolutional Denoising Autoencoder (CDAE) and Convolutional Sparse Autoencoder (CSAE). Despite the complexity of data heterogeneity involved across all datasets, CAE depicts the robustness in detection performance of jammed signal by achieving average values of 97.33% precision, 91.33% recall, 94.08% F1-score, and 94.35% accuracy over CDAE and CSAE.
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