  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.
