RF Fingerprinting Research Articles

Optimization of a Secure UAV-Based IoT: RF-Fingerprint Authentication and Resource Allocation

Unmanned aerial vehicle (UAV) technology has developed to establish a mobile edge computing (MEC) network for the Internet of things (IoT). In the MEC network, users can reduce their latency by communicating and exchanging data with UAV-based edge servers. Security is a critical issue in a UAV-based IoT since the attackers who attempt to access the network may cause interference and influence the flight of the UAV. In this paper, we propose a lightweight RF fingerprinting recognition method in consideration of the limited computing power in UAVs, identifying unauthenticated attackers and refusing their access to IoT. Also, we propose a resource allocation scheme in the secure UAV-based MEC network. By establishing a non-convex resource optimization problem and decomposing it into a few tractable subproblems, we offer a numerical algorithm for the optimum resource allocation. The analysis results illustrate that our proposed method can reduce energy consumption and running time compared to its benchmark methods.

Robust RF Fingerprint Extraction Based on Cyclic Shift Characteristic

Robust RF Fingerprint Extraction Based on Cyclic Shift Characteristic

The Use of SNN for Ultralow-Power RF Fingerprinting Identification With Attention Mechanisms in VDES-SAT

VHF Data Exchange System (VDES) is explored as a navigational safety guarantor for ships worldwide. As the number of jammers and spoofers targeting VDES is on the rise, its security authentication emerges as an urgent issue. Radio frequency fingerprinting identification (RFFI) as a noncryptographic physical layer security solution can effectively defend against increasingly sophisticated attacks. Nevertheless, the power consumption of frequent RFFI is not negligible for VDES satellite components with energy constraints. To this end, spiking neural networks (SNNs) are first employed to build an ultra-low power RF fingerprinting identification (RFFI) system for VDES, where the optimized spiking neurons can yield high accuracy with very few spikes. Moreover, multiple RF fingerprinting features are fused to highly integrate the information of the VDES signal in different dimensions, and the attention mechanism is utilized to further improve RFFI performance. Numerical results show that our SNN-based RFFI system can yield identification accuracy up to 92:59% at a signal-to-noise ratio (SNR) of 25 dB on VDES datasets, and reduces power consumption by 64% compared to artificial neural networks (ANNs) of comparable accuracy on 45 nm CMOS process.

Embedding-Assisted Attentional Deep Learning for Real-World RF Fingerprinting of Bluetooth

A scalable and computationally efficient framework is designed to fingerprint real-world Bluetooth devices. We propose an embedding-assisted attentional framework (Mbed-ATN) suitable for fingerprinting actual Bluetooth devices. Its generalization capability is analyzed in different settings and the effect of sample length and anti-aliasing decimation is demonstrated. The embedding module serves as a dimensionality reduction unit that maps the high dimensional 3D input tensor to a 1D feature vector for further processing by the ATN module. Furthermore, unlike the prior research in this field, we closely evaluate the complexity of the model and test its fingerprinting capability with real-world Bluetooth dataset collected under a different time frame and experimental setting while being trained on another. Our study reveals a 9.17× and 65.2× lesser memory usage at a sample length of 100 kS when compared to the benchmark -GRU and Oracle models respectively. Further, the proposed Mbed-ATN showcases a 16.9× fewer FLOPs and 7.5× lesser trainable parameters when compared to Oracle. Finally, we show that when subject to anti-aliasing decimation and at greater input sample lengths of 1 MS, the proposed Mbed-ATN framework results in a 5.32× higher TPR, 37.9% fewer false alarms, and 6.74× higher accuracy under the challenging real-world setting.

Few-shot RF fingerprinting recognition for secure satellite remote sensing and image processing

Few-shot RF fingerprinting recognition for secure satellite remote sensing and image processing

Membership Inference Attack and Defense for Wireless Signal Classifiers With Deep Learning

An over-the-air membership inference attack (MIA) is presented to leak private information from a wireless signal classifier. Machine learning (ML) provides powerful means to classify wireless signals, e.g., for PHY-layer authentication. As an adversarial machine learning attack, the MIA infers whether a signal of interest has been used in the training data of a target classifier. This private information incorporates waveform, channel, and device characteristics, and if leaked, can be exploited by an adversary to identify vulnerabilities of the underlying ML model (e.g., to infiltrate the PHY-layer authentication). One challenge for the over-the-air MIA is that the received signals and consequently the RF fingerprints at the adversary and the intended receiver differ due to the discrepancy in channel conditions. Therefore, the adversary first builds a surrogate classifier by observing the spectrum and then launches the black-box MIA on this classifier. The MIA results show that the adversary can reliably infer signals (and potentially the radio and channel information) used to build the target classifier. Therefore, a proactive defense is developed against the MIA by building a shadow MIA model and fooling the adversary. This defense can successfully reduce the MIA accuracy and prevent information leakage from the wireless signal classifier.

Disentangled Representation Learning for RF Fingerprint Extraction Under Unknown Channel Statistics

Deep learning (DL) applied to a device’s radio-frequency fingerprint (RFF) has attracted significant attention in physical-layer authentication due to its extraordinary classification performance. Conventional DL-RFF techniques are trained by adopting maximum likelihood estimation (MLE). Although their discriminability has recently been extended to unknown devices in open-set scenarios, they still tend to overfit the channel statistics embedded in the training dataset. This restricts their practical applications as it is challenging to collect sufficient training data capturing the characteristics of all possible wireless channel environments. To address this challenge, we propose a DL framework of disentangled representation (DR) learning that first learns to factor the signals into a device-relevant component and a device-irrelevant component via adversarial learning. Then, it shuffles these two parts within a dataset for implicit data augmentation, which imposes a strong regularization on RFF extractor learning to avoid the possible overfitting of device-irrelevant channel statistics, without collecting additional data from unknown channels. Experiments validate that the proposed approach, referred to as DR-based RFF, outperforms conventional methods in terms of generalizability to unknown devices under unknown complicated propagation environments, e.g., dispersive multipath fading channels, even though all the training data are collected in a simple environment with dominated direct line-of-sight (LoS) propagation paths.

Gaussian low‐pass channel attention convolution network for RF fingerprinting

AbstractRadio frequency (RF) fingerprinting is a challenging and important technique for individual identification of wireless devices. Recent work has applied deep learning‐based classifiers to ADS‐B signals without missing aircraft ID information. However, traditional methods are not very effective in achieving high accuracy for deep learning models to recognize RF signals. In this letter, a Gaussian low‐pass channel attention convolution network, which uses a Gaussian low‐pass channel attention module (GLCAM) to extract fingerprint features with low frequency. Specifically, in GLCAM, a frequency‐convolutional global average pooling module is designed to help the channel attention mechanism learn channel weights in the frequency domain. Experimental results on large‐scale real‐world ADS‐B signal datasets show that the method can achieve an accuracy of 92.08%, which is 6.21% higher than convolutional neural networks.

RFID-enabled localization system for mobile robot in the workshop

The development of RFID enabled intelligent localization system in the workshop is of great importance for reducing the operation cost, increasing production efficiency and improving management capabilities. From the aspects of feature extraction of RF fingerprint and localization algorithm, the scheme of mobile target tracking based on the fusion of inertial navigation and fingerprinting is explored. The deep neural network is used to establish the nonlinear relationship between fingerprint and coordinates. After the initial position of the mobile robot is obtained, Kalman filter is used to fuse the data collected by IMU and wheel encoder. Experimental results show that the proposed method is feasible and be able to track the mobile robot accurately.

Protecting Bluetooth User Privacy Through Obfuscation of Carrier Frequency Offset

This brief presents the analysis, design, and measurement results of an integrated circuit designed to prevent tracking the location of Bluetooth Low Energy (BLE) transmitters. Conventional BLE transmitters have unique RF fingerprints due to variation-induced imperfections in the underlying circuits. Coupled with BLE’s wide adoption in mobile devices and tendency to transmit continuously, BLE has become a significant threat to the location privacy of individual users. The primary source of this privacy threat is that BLE transmitters have a unique Carrier Frequency Offset (CFO) that can be easily fingerprinted by passive adversaries. To combat this, a test chip is developed that pseudo-randomly changes its CFO by switching in a binary-weighted set of semi-identical MIM capacitors into the tank of an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$LC$ </tex-math></inline-formula> voltage controlled oscillator, all while maintaining compatibility with BLE standard specifications. Measurement results reveal that privacy preservation can be improved from only a few seconds with a conventional design, to over a day with the proposed design.

RF Fingerprinting Identification Based on Spiking Neural Network for LEO–MIMO Systems

Low earth orbit (LEO) satellites promise to be a source of innovation in wireless communication and their physical layer security deserves attention. However, many physical layer security techniques are hampered by excessive power consumption and high latency when used for LEO satellites. This letter proposes an energy-efficiency radio frequency fingerprinting identification (RFFI) method for LEO satellite communication systems based on spiking neural networks (SNN). In addition, we investigate a channel-independent RFF feature transformation and data augmentation to enhance system robustness. Numerical results show that our proposed model can yield identification accuracy up to 95.26% at a signal-to-noise ratio (SNR) of 25dB on orthogonal frequency division multiplexing (OFDM) signals, and reduces power consumption by 63.3% compared to existing models of comparable accuracy on FPGA.

433 MHz 대역 송신기의 인증을 위한 RF 지문 기법

사물인터넷에 사용되는 소형 통신 기기들은 적은 메모리 용량과 느린 연산 속도 때문에 고급 암호기법을 적용하지 못하기 때문에 각종 해킹에 취약하다. 본 논문은 433MHz 대역에서 동작하는 소형 송신기들의 인증 신뢰도를 높이기 위해 RF지문을 도입하고 분류 알고리즘으로 CNN (convolutional neural network) 을 사용한다. 각 송신기가 전송하는 프리엠블 신호를 소프트웨어정의라디오를 사용하여 추출하고 수집하여 학습 데이터 집합으로 만들고, 이를 신경망을 학습시키는 데에 사용한다. 네 가지의 시나리오에서 20개의 송신기의 식별을 테스트한 결과 높은 식별 정확도를 얻을 수 있었다. 특히 학습 데이터 수집 시의 위치와 다른 위치에서 테스트를 수행한 시나리오에서, 그리고 송신기가 걷는 속도로 이동하는 시나리오에서 각각 95.8%, 92.6%의 정확도를 산출함을 알 수 있었다.

Deep-Learning-Aided RF Fingerprinting for NFC Relay Attack Detection

Near field communication (NFC) has been a widely used radiofrequency identification (RFID) technology, credited to its convenience and security features. However, the transmitted signals can be easily eavesdropped or relayed in an open wireless channel. One of the challenges is relay attack, where an attacker simply relays the signal and bypasses encryption or other means in the application layer. Prior works on relay attack countermeasures have focused on distance-bounding protocols or ambient-based solutions. This paper focuses on ISO/IEC 14443-A and proposes an NFC relay detection method based on RF fingerprinting of transmitted wireless signals in the physical layer. To this end, we first designed and implemented two realizations of NFC relay attacks, wired and wireless relays, and built an SDR-based testbed. We collected the normal and relayed signals of four NFC tags, and the answer to request type A (ATQA) segments were selected for RF fingerprinting. The created dataset comprised 66,366 samples, with four tags’ normal and wired relayed signals and the wireless relayed signals. The dataset was then fed into a deep CNN for training. Finally, our experiment results showed that the method effectively distinguished normal and relayed signals with a high accuracy of 99%, confirming that RF fingerprinting can be a promising countermeasure to NFC relay attacks.

An RF Fingerprint Extraction Method based on Time-frequency Domain Feature Fusion

Identification and authentication of communication radiation sources using RF fingerprint feature extraction technology is widely used in civilian and military applications and has become a research hotspot. In this paper, an RF fingerprint extraction method based on time-frequency domain feature fusion is proposed. The frequency domain features of WVD and the time domain statistical features (variance, skewness, kurtosis) of the signal are first extracted, and then fused and downscaled using multivariate discriminant analysis to obtain new time-frequency fusion features, which are finally input to the support vector machine for classification and recognition. This method is used to analyze signals of walkie-talkies and signals of cell phones with and without noise, and the experimental results show that this method effectively improves the recognition rate and has good noise immunity compared with using frequency domain features or time domain features alone, and the accuracy is improved by about 5% under the condition of low SNR. When the SNR is -10dB, the recognition accuracy of walkie-talkies can reach 81.6% and that of cell phones can reach 86.1%.

Channel-Resilient Deep-Learning-Driven Device Fingerprinting Through Multiple Data Streams

Enabling accurate and automated identification of wireless devices is critical for allowing network access monitoring and ensuring data authentication for large-scale IoT networks. RF fingerprinting has emerged as a solution for device identification by leveraging the transmitters’ inevitable hardware impairments that occur during manufacturing. Although deep learning is proven efficient in classifying devices based on hardware impairments, the performance of deep learning models suffers greatly from variations of the wireless channel conditions, across time and space. To the best of our knowledge, we are the first to propose leveraging MIMO capabilities to mitigate the channel effect and provide a channel-resilient device classification framework. We begin by showing that for AWGN channels, combining multiple received signals improves the testing accuracy by up to 30%. We then show that for more realistic Rayleigh channels, blind channel estimation enabled by MIMO increases the testing accuracy by up to 50% when the models are trained and tested over the same channel, and by up to 69% when the models are tested on a channel that is different from that used for training.

CNN-Based RF Fingerprinting Method for Securing Passive Keyless Entry and Start System

CNN-Based RF Fingerprinting Method for Securing Passive Keyless Entry and Start System

Deep Learning-Based RF Fingerprint Identification With Channel Effects Mitigation

Deep Learning-Based RF Fingerprint Identification With Channel Effects Mitigation

Attention is Needed for RF Fingerprinting

Attention is Needed for RF Fingerprinting

Adaptive RF Fingerprints Fusion via Dual Attention Convolutions

In recent years, with the rise of intelligent hardware technology, the Internet of Things (IoT) has achieved rapid development and has been widely used in smart cities, smart power grids, industrial Internet, and other fields. The resulting security risks of IoT have attracted more and more attention. Being different from the traditional authentication that is based on MAC or security certificate, RF fingerprinting technology extracts fingerprints from the emissions of wireless transmitters. These fingerprints root in the hardware imperfection of the transmitting circuits and can be used for wireless device identification and authentication. RF fingerprinting can effectively enhance the security of the wireless network and has become a research focus in the field of IoT. However, how to learn and fuse multiple RF fingerprints is still an urgent problem to be solved. Inspired by the attention mechanism in the field of computer vision, an adaptive RF fingerprints fusion network (ARFNet) is proposed in this article, which is built on our dual attention convolution (DAConv) layer. This neural network can extract and adaptively fuse multiple RF fingerprints in a data-driven manner to obtain more discriminant features. In addition, a data augmentation method is also designed to effectively enhance the network’s robustness to channel variation and SNR variation. Extensive experimental results show that the proposed ARFNet combined with data augmentation can achieve 99.5% recognition accuracy on five USRP X310, and achieve 95.7% recognition accuracy on 56 ADS-B devices. Our source code has been released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/zhangweifeng1218/Adaptive_RF_Fingerprinting</uri> .

RFID Scheme for IoT Devices Based on LSTM-CNN

As an essential branch of physical layer authentication research, radio frequency identification (RFID) has advantages in achieving lightweight and highly reliable authentication. However, in the Internet of Things (IoT) environment, where a large scale of devices are connected to the network, there is an issue that the difference of the RF fingerprints is less distinct among the same type of devices. To this end, in this paper, we propose an RFID scheme for IoT devices based on long-short term memory and convolutional neural network (LSTM-CNN). This scheme combines the excellent learning ability of LSTM and CNN to perceive the context information and extract the local feature of RF data. Specifically, RF data is first fed into LSTM to obtain long-term dependency features containing temporal information. Then, CNN is designed for secondary feature extraction to enlarge RF differences and further used for device classification. The experiment results on the open RF data set ORACLE indicate that the identification accuracy of the proposed scheme can reach over 99%. Compared with other schemes, the performance is improved by 6%-30%.