Multi-Factor Authentication (MFA) has emerged as a widely adopted and robust authentication mechanism across various scenarios. Whereas, existing methods suffer serious threats, because of their sacrifice of privacy protection, identity provider offline, or other critical features. In theorem, the above threats come from the technical challenge of the combination of biometric and secret key factors. To solve these problems and achieve a reliable access system, this paper introduces TrustMFA, a hybrid authentication framework that leverages anonymous credentials and fuzzy extractors to construct a unified and secure authentication scheme. TrustMFA transforms multiple factors, including biometric factors, into attributes in the anonymous credential, thereby fully guaranteeing the trust and privacy of user. To enhance the practicality, we propose ECC-LSH, an Error-Correcting Code compatible Locality-Sensitive Hashing, which ensures that Hamming distances between biometric features extracted from the same person remain within the error-correcting capability. Comprehensive security analysis and implementation results demonstrate TrustMFA’s effectiveness, showing acceptable computational and communication overhead while maintaining high security levels.

Zero-trust has emerged as a promising security paradigm for next-generation networks (NGN). However, conventional cryptographic schemes struggle with continuous and dynamic authentication due to their coarse granularity and cumbersome processes. Radio frequency fingerprint identification (RFFI), as a prospective solution, enables physical-layer user-transparent identity authentication. Whereas, facing the dynamic topology and device mobility of NGN, such as Internet of Vehicles (IoV), Drone networks, etc., there exists a current deficiency in addressing the significant performance degradation across different receivers. In this paper, we propose a novel RFFI scheme for zero-trust continuous authentication in dynamic NGN environments, enabling unified high-performance cross-receiver identification. A two-stage unsupervised domain adaptation model is designed to extract receiver-independent transmitter-specific features. The receiver-side impact on RFFI, modeled as domain shift, is addressed through adversarial training for global alignment and local maximum mean discrepancy (LMMD)-based subdomain adaptation for eliminating subdomain confusion. Moreover, we further optimize RFFI through data augmentation to enhance robustness, multi-sample fusion inference to handle dynamic uncertainties, and an adaptive few-sample selection strategy for efficient fine-tuning. Extensive experiments on public datasets demonstrate the excellent performance of our proposed scheme in cross-receiver zero-trust wireless networks.

The technology development of wireless communication has brought about the rapid growth of various wireless devices, but also brings in many security threats. This paper focuses on the security and privacy problems in wireless authentication and proposes a novel authentication scheme based on the design of reusable fuzzy extractor (RFE) for device's radio frequency (RF) fingerprinting. Firstly, unlike the traditional authentication protocol, our scheme can accomplish the mutual authentication without the storage of long-term secret key, thus tackles with the key-compromise threats. Furthermore, although the scheme authenticates devices based on their RF fingerprint, it does not store RF fingerprinting information explicitly to safeguard it from eavesdroppers who may use it to impersonate the identity of valid users. Finally, our designed protocol relies on the correspondent peer to measure the fingerprint, rather than the device itself, thus is more secure against various adversaries. The security analysis shows the resiliency against theft of secret keys, wireless channel attacks and privacy disclosure. And the performance evaluation demonstrates that the design of RFE for device's RF fingernrinting is efficient in terms of recognition accuracy.