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Physical-Layer Authentication (PLA) has been recently believed as anendogenous-secure and energy-efficient technique to recognize IoT terminals.However, the major challenge of applying the state-of-the-art PLA schemesdirectly to 6G-enabled IoT is the inaccurate channel fingerprint estimation inlow Signal-Noise Ratio (SNR) environments, which will greatly influence thereliability and robustness of PLA. To tackle this issue, we propose aconfigurable-fingerprint-based PLA architecture through Intelligent ReflectingSurface (IRS) that helps create an alternative wireless transmission path toprovide more accurate fingerprints. According to Baye's theorem, we propose aGaussian Process Classification (GPC)-based PLA scheme, which utilizes theExpectation Propagation (EP) method to obtain the identities of unknownfingerprints. Considering that obtaining sufficient labeled fingerprint samplesto train the GPC-based authentication model is challenging for future 6Gsystems, we further extend the GPC-based PLA to the Efficient-GPC (EGPC)-basedPLA through active learning, which requires fewer labeled fingerprints and ismore feasible. We also propose three fingerprint selecting algorithms to choosefingerprints, whose identities are queried to the upper-layers authenticationmechanisms. For this reason, the proposed EGPC-based scheme is also alightweight cross-layer authentication method to offer a superior securitylevel. The simulations conducted on synthetic datasets demonstrate that theIRS-assisted scheme reduces the authentication error rate by 98.69% compared tothe non-IRS-based scheme. Additionally, the proposed fingerprint selectionalgorithms reduce the authentication error rate by 65.96% to 86.93% and 45.45%to 70.00% under perfect and imperfect channel estimation conditions,respectively, when compared with baseline algorithms.

Efficient Gaussian Process Classification-based Physical-Layer Authentication with Configurable Fingerprints for 6G-Enabled IoT