Wireless networks are vulnerable to physical layer spoofing attacks due to the wireless broadcast nature, thus, integrating communications and security (ICAS) is urgently needed for 6G endogenous security. In this letter, we propose an environment semantics enabled physical layer authentication network based on deep learning, namely EsaNet, to authenticate the spoofing from the underlying wireless protocol. Specifically, the frequency independent wireless channel fingerprint (FiFP) is extracted from the channel state information (CSI) of a massive multi-input multi-output (MIMO) system based on environment semantics knowledge. Then, we transform the received signal into a two-dimensional red green blue (RGB) image and apply the you only look once (YOLO), a single-stage object detection network, to quickly capture the FiFP. Next, a lightweight classification network is designed to distinguish the legitimate from the illegitimate users. Finally, the experimental results show that the proposed EsaNet can effectively detect physical layer spoofing attacks and is robust in time-varying wireless environments.

翻译：无线网络由于广播特性易受物理层欺骗攻击，因此迫切需要将通信与安全融合以实现6G内生安全。本文提出一种基于深度学习的环境语义物理层认证网络——EsaNet，用于从底层无线协议中鉴别欺骗行为。具体地，基于环境语义知识，从大规模多输入多输出系统的信道状态信息中提取频率无关无线信道指纹。随后将接收信号转换为二维红绿蓝图像，并采用单阶段目标检测网络YOLO快速捕获该指纹。进一步设计轻量级分类网络以区分合法用户与非法用户。实验结果表明，所提EsaNet能有效检测物理层欺骗攻击，且在时变无线环境中具有鲁棒性。