EsaNet: Environment Semantics Enabled Physical Layer Authentication
Ning Gao, Qiying Huang, Cen Li, Shi Jin, Michail Matthaiou
Abstract
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 user from the illegitimate user. Finally, the experimental results show that the proposed EsaNet can effectively detect a physical layer spoofing attack and is robust in time-varying wireless environments.