MRIS-SAD: Malicious RIS Spoofing Attack Detection Based on Hybrid Deep Autoencoder

Reconfigurable Intelligent Surfaces (RIS) can optimize spectrum and energy efficiency in the sixth-generation (6G) wireless communication system through dynamic electromagnetic wave manipulation. The programmable control of spatial electromagnetic signals by RIS presents a double-edged sword, and it can also be exploited by malicious attackers. However, few studies have focused on the detection and identification of such malicious RIS. To fill this gap, we propose a novel spoofing detection framework combining dynamic key-embedded phase codebooks with a dual-channel feature extraction mechanism. This approach jointly decodes wireless channel fingerprints and cryptographic signatures from received signals. A hybrid discriminator, integrating autoencoder-based signal reconstruction fidelity and key-matching validation, enables robust legitimacy verification. The prototype experiments using USRP SDR and RIS hardware show that the verification accuracy of the scheme can reach 100%, when the signal-to-noise ratio (SNR) is above 10dB, the number of training sample points is more than 128, and the codebook dimension is near 32.