Abstract
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%.
Highlights
A covert channel is considered a technique for secretly transmitting information from a malicious entity to other entities
Research on the detection of Wireless covert channel (WCC) is currently relatively rare, mainly divided into two types, one is the detection for wireless covert channel based on upper layer protocol (WCC-U), and the other is for wireless covert channel based on the physical layer (WCC-P)
We have proposed a detection scheme based on a convolutional neural network for the wireless covert channel with the modulation of the constellation points (WCC-MC)
Summary
A covert channel is considered a technique for secretly transmitting information from a malicious entity to other entities. (1) A detection and classification scheme is proposed for WCC-MC signal based on deep learning, which uses the amplitude and phase characteristics of EVM and constellation points. (2) e amplitude-phase characteristics of EVM signal and constellation points will be converted into grayscale feature images. We propose a three-stage approach to detect and classify the WCC-P signal based on the amplitude-phase feature images and adjusted CNN. En these feature images will be subject to statistical computations which will be fed to the final block (deep neural network) for training/testing purposes and eventually WCC-MC signal classification (detection). Due to the different signal-to-noise ratios (SNRs), the EVM signals received by the detector are significantly different, which leads to the amplitude-phase characteristic difference of the constellation points generated under different SNR conditions even for the legitimate communication signals. Where ΔS is the error of softmax layer, ΔP is pooling layer, and ΔK is convolution layer
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