A Novel PHY-Layer Spoofing Attack Detection Scheme Based on WGAN-Encoder Model

PHY-layer spoofing attack is a potential critical issue in wireless network communication security, which could lead to catastrophic consequences for critical mission and applications, especially in Industrial Internet of Things scenarios with enormous number of devices. In this paper, we propose a novel spoofing attack detection scheme exploiting Channel State Information (CSI) phase difference. Firstly, we establish a mapping between CSI phase difference and the location of wireless communication devices to achieve the goal of spoofing attack detection. Due to the stable property of CSI phase difference, we convert CSI phase difference into heatmaps for subsequent training of the neural network model. Then we propose Wasserstein generative adversarial network and Encoder (WGAN-Encoder) deep-learning-based model in the scheme. This model utilizes discriminator feature residual error and image reconstruction error to get anomaly score for spoofing attack detection. This model overcomes the limitations of traditional detection methods on prior knowledge the attacker's real CSI under real communication scenarios. Finally, we carry out extensive experimental evaluations about the detection performance and robustness of the proposed scheme based on data collected in time-varying scenarios. The results have successfully demonstrated that the proposed scheme exhibits outstanding performance.