RF Fingerprinting Research Articles

Cost-Effective RF Fingerprinting Based on Hybrid CVNN-RF Classifier With Automated Multidimensional Early-Exit Strategy

Cost-Effective RF Fingerprinting Based on Hybrid CVNN-RF Classifier With Automated Multidimensional Early-Exit Strategy

Multi-Channel CNN-Based Open-Set RF Fingerprint Identification for LTE Devices

Multi-Channel CNN-Based Open-Set RF Fingerprint Identification for LTE Devices

Novel Micro-UAV Identification Approach Assisted by Combined RF Fingerprint

Novel Micro-UAV Identification Approach Assisted by Combined RF Fingerprint

A Graphical Deep Learning Approach to RF Fingerprinting in the Time-Frequency Domain

A Graphical Deep Learning Approach to RF Fingerprinting in the Time-Frequency Domain

Bluetooth and WiFi Dataset for Real World RF Fingerprinting of Commercial Devices

Bluetooth and WiFi Dataset for Real World RF Fingerprinting of Commercial Devices

FeRHA: Fuzzy-Extractor-Based RF and Hardware Fingerprinting Two-Factor Authentication

FeRHA: Fuzzy-Extractor-Based RF and Hardware Fingerprinting Two-Factor Authentication

Channel-Resilient RF Fingerprint Identification Based on Nonlinear Features With Memory Effect

Channel-Resilient RF Fingerprint Identification Based on Nonlinear Features With Memory Effect

Semisupervised RF Fingerprinting With Consistency-Based Regularization

Semisupervised RF Fingerprinting With Consistency-Based Regularization

RF Fingerprinting Identification in Low SNR Scenarios for Automatic Identification System

RF Fingerprinting Identification in Low SNR Scenarios for Automatic Identification System

Real-World Aircraft Recognition Based on RF Fingerprinting With Few Labeled ADS-B Signals

Real-World Aircraft Recognition Based on RF Fingerprinting With Few Labeled ADS-B Signals

RF-TESI: Radio Frequency Fingerprint-based Smartphone Identification under Temperature Variation

Radio frequency fingerprint identification (RFFI) is a promising technique for smartphone identification. However, we find that the temperature of the RF front end in smartphones can significantly impact the RF features, including the carrier frequency offset (CFO) and statistical RF features. The unstable RF features caused by temperature changes can negatively affect the performance of state-of-the-art RFFI approaches. To this end, we propose the RF-TESI solution for smartphone identification under temperature variation. First, we construct a dataset by extracting temperature and RF features. In the dataset, the extracted temperature values constitute a set of temperature values and each registered temperature value corresponds to a group of RF features. Next, we evaluate the distinctiveness of RF features across smartphones to select the most suitable RF fingerprint. Then, we train multiple random forest models, each tagged with a registered temperature. In addition, because there are still many temperatures out of the temperature set, we design an RF fingerprint estimation method to estimate RF fingerprints at unregistered temperatures. Finally, the experiments show RF-TESI demonstrates satisfactory performance under different scenarios, taking into account variations in temperature, time and position. Besides, our proposed approach is better than all state-of-the-art approaches in smartphone identification.

Toward Intelligent Lightweight and Efficient UAV Identification With RF Fingerprinting

Toward Intelligent Lightweight and Efficient UAV Identification With RF Fingerprinting

GSM-based Mobile Handset Identification Using RF Fingerprints and Deep Learning on the Edge Computing Devices

GSM-based Mobile Handset Identification Using RF Fingerprints and Deep Learning on the Edge Computing Devices

Channel-Robust RF Fingerprint Identification Using Multi-Task Learning and Receiver Collaboration

Channel-Robust RF Fingerprint Identification Using Multi-Task Learning and Receiver Collaboration

Enhancing RF Fingerprint Generation in Power Amplifiers: Unequally Spaced Multitone Design Approaches and Considerations

Enhancing RF Fingerprint Generation in Power Amplifiers: Unequally Spaced Multitone Design Approaches and Considerations

CSI-RFF: Leveraging Micro-Signals on CSI for RF Fingerprinting of Commodity WiFi

CSI-RFF: Leveraging Micro-Signals on CSI for RF Fingerprinting of Commodity WiFi

Efficient Channel-Temporal Attention for Boosting RF Fingerprinting

Efficient Channel-Temporal Attention for Boosting RF Fingerprinting

Fingerprint Extraction Through Distortion Reconstruction (FEDR): A CNN-Based Approach to RF Fingerprinting

Fingerprint Extraction Through Distortion Reconstruction (FEDR): A CNN-Based Approach to RF Fingerprinting

Fine-Grained Radio Frequency Fingerprint Recognition Network Based on Attention Mechanism.

With the rapid development of the internet of things (IoT), hundreds of millions of IoT devices, such as smart home appliances, intelligent-connected vehicles, and wearable devices, have been connected to the network. The open nature of IoT makes it vulnerable to cybersecurity threats. Traditional cryptography-based encryption methods are not suitable for IoT due to their complexity and high communication overhead requirements. By contrast, RF-fingerprint-based recognition is promising because it is rooted in the inherent non-reproducible hardware defects of the transmitter. However, it still faces the challenges of low inter-class variation and large intra-class variation among RF fingerprints. Inspired by fine-grained recognition in computer vision, we propose a fine-grained RF fingerprint recognition network (FGRFNet) in this article. The network consists of a top-down feature pathway hierarchy to generate pyramidal features, attention modules to locate discriminative regions, and a fusion module to adaptively integrate features from different scales. Experiments demonstrate that the proposed FGRFNet achieves recognition accuracies of 89.8% on 100 ADS-B devices, 99.5% on 54 Zigbee devices, and 83.0% on 25 LoRa devices.

RF fingerprint extraction and device recognition algorithm based on multi-scale fractal features and APWOA-LSSVM

RF fingerprints can be used for device identification and network access authentication. An RF fingerprint extraction and device identification algorithm based on multi-scale fractal features and APWOA-LSSVM is proposed. First, a Hilbert transform is performed on the original RF signal, and differential operations are performed on the I/Q signal. Then, the variational modal decomposition is performed on the differentially processed I/Q signals separately to obtain a number of band-limited Intrinsic Modal Function (IMF) components. The fractal box dimension of each IMF component is calculated separately as the first-dimensional RF fingerprint. The multi-fractal spectrum of the original RF signal is further computed by Multi-fractal Detrended Fluctuation Analysis (MFDFA) as the second-dimensional RF fingerprint. The RF fingerprint feature vector consists of a combination of the first- and second-dimensional RF fingerprints and is used to train the LSSVM model. Since the penalty factor and kernel parameter width directly affect the recognition accuracy and generalization ability of the LSSVM model, APWOA is used to optimize the hyperparameters of the LSSVM model. Finally, the LSSVM model is constructed using the optimal hyperparameters and the performance of the model is verified on a test dataset. The experimental results show that the proposed model achieves an average recognition accuracy of 99.13%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} for 16 Bluetooth devices. Multi-scale fractal features consisting of fractal box dimensions and multiple fractal dimensions are more beneficial in the recognition of wireless devices as compared to single features. In addition, the proposed model possesses superior recognition capabilities when compared to classical machine learning algorithms.