With the development of wireless communication technology, more and more information leakage is realized through a wireless covert channel, which brings great challenges to the security of wireless communication. Compared with the wireless covert channel on the upper layer, the wireless covert channel based on the physical layer (WCC-P) has better concealment and greater capacity. As the most widely used scheme of WCC-P, the wireless covert channel with the modulation of the constellation point (WCC-MC) has attracted more and more attention. In this paper, a deep learning scheme based on amplitude-phase characteristics is proposed to detect and classify the WCC-MC scheme. We first extract the amplitude and phase characteristic of error vector magnitude (EVM) and constellation points and then map the amplitude and phase characteristic to the grayscale image, respectively. Finally, the generated feature images are trained, detected, and classified with the adjusted convolution neural network. The experimental results show that the detection accuracy of our proposed scheme can reach 98.5%, and the classification accuracy can reach 81.7%.

中文翻译：

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基于调整CNN模型的星座调制无线隐蔽信道检测

随着无线通信技术的发展，越来越多的信息泄露是通过无线隐蔽信道实现的，这给无线通信的安全带来了极大的挑战。与上层的无线隐蔽信道相比，基于物理层的无线隐蔽信道（WCC-P）具有更好的隐蔽性和更大的容量。作为WCC-P应用最广泛的方案，星座点调制的无线隐蔽信道(WCC-MC)越来越受到关注。本文提出了一种基于幅相特征的深度学习方案，对WCC-MC方案进行检测和分类。我们首先提取误差矢量幅度（EVM）和星座点的幅度和相位特征，然后将幅度和相位特征分别映射到灰度图像。最后，生成的特征图像通过调整后的卷积神经网络进行训练、检测和分类。实验结果表明，我们提出的方案检测准确率可达98.5%，分类准确率可达81.7%。