EsaNet: Environment Semantics Enabled Physical Layer Authentication

TL;DR

EsaNet leverages environment semantics and deep learning to authenticate wireless devices at the physical layer, effectively detecting spoofing attacks in 6G networks with robustness to environmental changes.

Contribution

The paper introduces EsaNet, a novel environment semantics enabled deep learning framework for physical layer authentication in wireless networks, combining CSI analysis and object detection techniques.

Findings

EsaNet accurately detects spoofing attacks in MIMO systems.

The method is robust in time-varying wireless environments.

EsaNet achieves high detection speed and reliability.

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 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.