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.

Radio frequency fingerprint identification (RFFI) has been widely studied as a physical layer security scheme for device identification and authentication in wireless scenarios, such as Internet of Things (IoTs), industrial wireless networks, Internet of Vehicles (IoV), etc. Typical RFFI approaches train a model at the receiver to extract hardware defects of the transmitter RF front-end using a deep learning-based method and achieve classification. However, few works have taken into account its shortage in multiple-receiver scenarios, where the identification accuracy significantly decreases when migrating a model trained on the known receivers to the new ones, directly. In this paper, we propose a novel cross-receiver RFFI scheme to improve the performance and the generalization of the fingerprinting classification tasks on new receivers. This scheme tackles the shortage by two means: 1) we extract receiver- independent features using global domain adaptation based on adversarial training and relevant subdomain adaptation based on local maximum mean discrepancy (LMMD); 2) The performance is further improved by fine-tuning on few labeled samples when domain adaptation is not effective. The second mechanism brings in significant performance advantage, without a large amount of labeled data on new receivers. Experimental results on public datasets show the outstanding performance of the proposed scheme in cross-receiver scenarios.

Machine learning mechanisms are applied to detect the unique characteristics of the wireless interface or signaler that can distinguish one device's signal pattern from the others, which has been widely researched as the fingerprint for user identification. However, such fingerprinting can also be used for malicious purposes, i.e., identification tracking, undesired positioning, etc., as the unique features of the radio signal from a device is determined at the manufacturing stage and often cannot be easily removed afterward. To prevent privacy leakage from such radio frequency (RF) fingerprinting, in this paper, we propose an adversarial mechanism to hide the fingerprint whereby the device's identification cannot be induced by machine learning models from the preamble. Specially, we apply the adversarial attack method to attack the fingerprinting model by adding optimized adversarial perturbation to the preamble that can mislead the model classification results. To alleviate the adversarial sample's impact on communications and ensure the execution of packet detection at receivers, we improve the identification protection strategy with sparse perturbed features. In order to prevent further fingerprinting of re-training over the perturbed RF feature, we extend our method with the time-varying perturbations to further hide the device's identity. Extensive experiments are conducted, and we show that the proposed method can effectively hide the device identification from both the dedicated fingerprint model and the re-trained one from perturbed signals without disturbing the preamble functionality, which provides a gratifying confirmation of the proposed method.