The radio frequency fingerprint (RFF)-based device identification is a promising physical layer authentication technique. However, the wireless channel significantly affects the RFF features of the wideband wireless devices. In this paper, we extensively investigate the impact of channel variation on RFF identification using 20 MHz IEEE 802.11 signal. A time domain least mean square (LMS) equalization-based feature extraction method has been proposed. This method progressively restores the transmitted signal and preserves more details of RFF features than the classical frequency domain equalization (FDE) method. Moreover, a hybrid identifier is proposed to take advantage of both LMS-based and FDE-based methods. With the equalized samples, a four-layer convolutional neural network is designed for device identification. An experimental system has been set up to capture the waveform of 68 802.11 devices at different positions. The experimental results show that the LMS-based method outperforms others when the acquisition positions of the training dataset are the same as those of the testing dataset. On the other hand, the FDE-based method is shown to be more effective when the acquisition positions of the training dataset do not fully include those of the testing dataset. Moreover, the hybrid identifier achieves an improvement of 2% for overall identification accuracy.

Radio frequency fingerprint identification (RFFI) is a promising authentication mechanism for physical layer security. In this paper, we thoroughly validate the feasibility of using RFFI for cellular long-term evolution (LTE) devices. Firstly, we conduct simulations to examine the subtle impacts of hardware impairments on LTE signals. The simulated results reveal that I/Q imbalance and power amplifier non-linearity introduce significant distortions within in-band spectrum, forming unique hardware fingerprints. We then leverage the strong channel correlation between adjacent subcarriers and separate the channel-robust radio frequency fingerprints (RFF) from uplink demodulation reference signal (DMRS) in Msg3. Subsequently, we construct a hybrid feature matrix to serve as input for a shallow long short-term memory (LSTM) network. Due to the more effective channel mitigation strategy, our method outperforms three benchmarks in terms of classification accuracy under cross-scenario testing. Additionally, we explore the impacts of bandwidth configuration on RFFI, and experimental findings demonstrate that LTE terminals will exhibit more distinct RFF when occupying a larger number of physical resource blocks (RB) during transmission. We also investigate the stability of RFF towards frequency band variations. The results suggest that there will be a significant accuracy loss under training with one band but testing with another, indicating the importance of frequency band-independent feature extraction in practical environments. Lastly, we expose four key implications to pave the way for exploring corresponding solutions. To the best of our knowledge, it is the first performance evaluation of the RFFI system on different frequency bands and with multiple bandwidth configurations.

As a novel solution for identification of Internet of Things (IoT) devices, radio frequency fingerprint (RFF) has excellent features, such as uniqueness, stability, and nonreproducibility. We propose an RFF extraction method for a type of signals with the cyclic shift characteristic (CSC). The extracted RFF is robust in terms of data independence, noise resistance, and channel immunity. A unified RFF representation is derived by the cross-power spectral density (CPSD)-based extraction method, which utilizes all different symbols within one signal frame under the random sending symbol conditions. Theoretical analysis demonstrates that this representation can be superimposed to obtain effective device identification performance under the low signal-to-noise ratio (SNR) conditions. Additionally, a preamble spectrum correlation (PSC) algorithm is proposed to fast estimate and compensate the large carrier frequency offset (CFO) in the preprocessing process of CPSD-based feature extraction. Our method is verified by conducting extensive experiments with over 34000 frames from 60 ZigBee devices in various scenarios, including indoor/outdoor and line-of-sight/non-LOS (LOS/NLOS). The identification accuracy reaches 96.66% for 60 devices in the LOS scenario and 98.81% for 36 devices in the NLOS scenario both with the shortest frame compared to the existing methods in practice. Thanks to the robust RFF extraction, experimental results show that our method adopting a simple linear discriminant analysis (LDA) classifier outperforms the state-of-the-art RFF identification methods using deep learning-based neural networks.

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.

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.

The growing popularity of 4 G/5 G mobile devices has led to an increase in demand for wireless security. Radio frequency fingerprint (RFF) technique is an emerging approach for device authentication using intrinsic and unique hardware impairments. In this paper, we propose an RFF-based method to identify rogue/unknown long term evolution (LTE) terminals. This is achieved by combining wavelet transform (WT) coefficient graphs and differential spectrum. The proposed method involves extracting 48 levels of wavelet coefficients from the transient power-off of the physical random access channel (PRACH) signal and representing them in a WT graph. The steady-state part of the PRACH signal after a frequency domain differential processing between the adjacent spectrum is extracted. To detect unknown attack devices, an identification scheme based on an autoencoder (AE) is designed. Two different AE network structures are designed based on the proposed features, and a hybrid identification structure is proposed. An experimental evaluation system is set up with seven mobile phones from three categories and one universal software radio peripheral (USRP) software-defined radio (SDR) platform. Training and testing datasets are collected under different conditions such as location, working times, and dates. Experimental results show that rogue devices can be identified with an accuracy up to 98.84% for different categories and 90.27% for different individuals.