Hua Fu

Radio Frequency Fingerprint Identification (RFFI) exploits inherent hardware imperfections in devices for identification, and hardware imperfections generate Radio Frequency Fingerprint (RFF) including various features. The nonlinearity of the power amplifier (PA) is an important feature of RFF that has been widely used in radio frequency identification (RFFI) technology. In this paper, we constructed a testbed consisting of 60 IEEE 802.15.4 devices and a universal software radio peripheral (USRP) X310 software-defined radio (SDR) platform as the receiver for the estimation of PA parameters. Using this testbed, we modeled and estimated PA parameters using 60 real IEEE 802.15.4 devices to obtain real PA parameters and generate synthetic PA parameters for generation of synthetic devices. When the number of devices requiring identification or research is limited, this method can be employed to expand the device sample size to conduct identification model training or related studies. Experimental results demonstrated the feasibility of the synthetic data-based pre-training approach, whose performance approaches that of trained models without pre-training. In practical applications, legal device identity registration in device identification can be achieved with a low-cost fine-tuning compared to retraining.

Radio Frequency Fingerprinting (RFF) is a promising technique for physical-layer hardware identification. Among various hardware-induced features, nonlinear distortions introduced by power amplifiers (PAs) contribute significantly to the overall RFF. However, fingerprint components related to PA behavior are sensitive to PA module replacement, variations in transmit power, and their coupling with multipath wireless channels, which can degrade identification performance in practical scenarios. To address these challenges, we propose a unified system model that jointly models and compensates for both PA nonlinear distortions and wireless channel convolution effects. Building on this model, we develop a Half-Quadratic Splitting (HQS)-based alternating optimization strategy to iteratively estimate the channel matrix and PA nonlinear coefficients. Experiments on real-world LoRa datasets show that the proposed method achieves robust identification performance: 95.22% accuracy under LOS and 91.19% under NLOS conditions when tested against unseen PA modules, and 97.50%(LOS) and 90.78%(NLOS) accuracy under varying transmit power levels.

The identification of radio frequency fingerprint (RFF) extracted from wireless signals subjected to channel interference faces a formidable challenge. Traditional equalization-based channel removal methods require known pilot signals and may potentially eliminate RFF features while suppressing channel effects. To address this challenge, the channel divide-and-conquer normalization (CDCN) algorithm is proposed to remove effects of arbitrary channels while preserving RFF features in the sense of cosine similarity. Through cross-scenario experiments on 60 off-the-shelf ZigBee devices in eight different scenarios, the CDCN algorithm with region of interest (ROI) achieves high accuracy of up to 99.98% on ResNet-9 and is verified to be generalized and practical.

The vehicle to everything (V2X) serves as a crucial foundation for future intelligent transportation systems. Security concerns within the V2X have garnered significant attention and key generation from wireless channels have emerged as a promising technique. However, applying key generation to V2X is quite challenging because the fast moving vehicles result in very small coherence time and impact channel measurements correlation. This paper designed a practical V2X key generation by enhancing channel state information (CSI) reciprocity and carried out extensive experimental evaluation. In particular, the designed key generation consists of channel probing, CSI preprocessing, CSI compensation and key establishment. In the channel probing, we deliberately reduced the time delay between uplink and downlink transmissions, to allow almost simultaneous measurements. We then carefully designed CSI preprocessing to remove hardware carrier leakage and eliminate noise effects. Furthermore, we devised CSI compensation by using interpolation or deep learning prediction to further improve the reciprocity. Finally, key establishment converted the measured CSI into binary sequences and reconcile on a common key via low-density parity-check (LDPC) code. We adopted universal software radio peripheral (USRP) X310 platforms for channel measurements and implemented the above algorithms. We carried out extensive experiments in real-road environments with various vehicle speeds. These carefully designed algorithms enabled our system working robustly even in high mobility scenarios, e.g., 40 km/h. Experimental results demonstrated common and random key can be generated with a key block error rate (BER) less than 0.1.

The radio frequency fingerprint (RFF) has gained significant traction in the identification of wireless Internet of Things (IoT) devices. However, RFFs extracted from wireless signals are inherently susceptible to noise, particularly for narrowband signals. Furthermore, the noisy domain adaptation (NDA) problem presents a substantial challenge for RFF identification due to the variable noise interference across different noisy domains. To address this, the squared cross power spectral density (SCPSD) as new device RFFs is derived theoretically as a function of signal-to-noise ratio (SNR). Combined with the proposed high-precision SNR estimation algorithm, SCPSDs under low SNR can be reconstructed to the same feature distribution as those under high SNR. Because of the interpretability, ten samples under high SNR from each device under test (DUT) and a shallow convolutional neural network (CNN) are trained for experimental evaluation on the NDA problem. Tested on 60 off-the-shelf ZigBee DUTs, the improvement of identification accuracy is around 26% for SNR between 5 dB and 10 dB, and the overall improvement is more than 20% compared to the baseline. It outperforms the three other compared methods across all testing SNR and is highly practical.

In radio frequency fingerprint identification (RFFI) systems, mitigating channel interference remains a critical challenge. This paper introduces a robust RFFI system to tackle this issue effectively. Specifically, taking the IEEE 802.11 signal as the case study, a signal representation is designed based on the logarithmic spectrum, while an RFF extractor based on the U-Net neural network is employed which is guided by a proposed Channel2Channel (C2C) algorithm and powered by a designed data augmentation method. Furthermore, a collaborative identification mechanism is proposed based on a support vector machine (SVM) classifier, where a multi-frame RFF fusion method is designed to exploit the diversity across different frames of received signal. Extensive experimental evaluations are performed in various real-world scenarios using 7 mobile phones and a universal software radio peripheral (USRP) X310 receiver, where an average classification accuracy of 95.72% is obtained with a single frame of received signal, outperforming the neural network-based benchmarks, and an average accuracy of 99.46% is acquired with 10 signal frames based on the proposed collaborative identification method. In addition, the deployability of the system on a resource-constrained computing platform is also validated.

Radio frequency fingerprint identification (RFFI) is a promising technique that exploits the transmitter-specific characteristics of the RF chain for identification. Disregarding its massive deployment, long-term evolution (LTE) systems have not fully benefited from RFFI. In this paper, an RFFI technique is designed to authenticate LTE devices. Three segments of the LTE physical layer random access channel (PRACH) preambles are captured, namely the transient-on, transient-off, and modulation parts. The segments are first converted into differential constellation trace figures (DCTFs), and then a specific type of neural network called multi-channel convolutional neural network (MCCNN) is used for identification. Additionally, the protocol is able to be applied for open-set identification, i.e., unknown device detection. Experiments are conducted with ten LTE mobile phones. The results show that the proposed RFFI scheme is robust against location changes. In the known device classification problem, the classification accuracy can reach 98.70% in the line-of-sight (LOS) scenario and 89.40% in the non-line-of-sight (NLOS) scenario. In the open-set unknown device detection problem, the identification equal error rate (EER) and area under the curve (AUC) reach 0.0545 and 0.9817, respectively, among six known devices and four unknown devices.

With the development of IoT and cloud networks, the security of edge networks, borderless networks and obscure networks are essential, so there are many security problems that need to be tackled, including over-trust in trust areas and security only based on security boundaries in traditional security architecture. According to characters of zero trust security architectures and integrative trust model, the zero trust architectures better adapt to handle these security problems compared to the integrative trust model for the Internet of Things (IoT) networks. Meanwhile, the radio frequency fingerprint (RFF) identification keeps high accuracy and high stability with researchers’ investigation, which makes RFF authentication feasible. Therefore, we propose a mechanism that combines the RFF authentication technique and zero trust architecture to improve security in IOT networks, including edge networks, borderless networks and obscure networks. The method resolves the difficulty of over-reliance on a trustable center or trust chain, and the method is suitable for borderless networks and obscure networks. Besides, this method resists data leakage, counterfeit attack and rouge AP attack with RFF authentication, and it can reduce the risk caused by compromised devices with zero trust concepts. With the analysis in the paper, the proposed method keeps high-level security and performance that method effectively against spoofing identity, tampering and information disclosure. The authentication accuracy of the method has reached 99%, and the authentication owns robustness in time cost and collision-resistant.