Radio frequency fingerprinting(RFF)is a remarkable lightweight authentication scheme to support rapid and scalable identification in the internet of things(IoT)systems.Deep learning(DL)is a critical enabler of RFF ide...Radio frequency fingerprinting(RFF)is a remarkable lightweight authentication scheme to support rapid and scalable identification in the internet of things(IoT)systems.Deep learning(DL)is a critical enabler of RFF identification by leveraging the hardware-level features.However,traditional supervised learning methods require huge labeled training samples.Therefore,how to establish a highperformance supervised learning model with few labels under practical application is still challenging.To address this issue,we in this paper propose a novel RFF semi-supervised learning(RFFSSL)model which can obtain a better performance with few meta labels.Specifically,the proposed RFFSSL model is constituted by a teacher-student network,in which the student network learns from the pseudo label predicted by the teacher.Then,the output of the student model will be exploited to improve the performance of teacher among the labeled data.Furthermore,a comprehensive evaluation on the accuracy is conducted.We derive about 50 GB real long-term evolution(LTE)mobile phone’s raw signal datasets,which is used to evaluate various models.Experimental results demonstrate that the proposed RFFSSL scheme can achieve up to 97%experimental testing accuracy over a noisy environment only with 10%labeled samples when training samples equal to 2700.展开更多
This study presents a radio frequency(RF)fingerprint identification method combining a convolutional neural network(CNN)and gated recurrent unit(GRU)network to identify measurement and control signals.The proposed alg...This study presents a radio frequency(RF)fingerprint identification method combining a convolutional neural network(CNN)and gated recurrent unit(GRU)network to identify measurement and control signals.The proposed algorithm(CNN-GRU)uses a convolutional layer to extract the IQ-related learning timing features.A GRU network extracts timing features at a deeper level before outputting the final identification results.The number of parameters and the algorithm’s complexity are reduced by optimizing the convolutional layer structure and replacing multiple fully-connected layers with gated cyclic units.Simulation experiments show that the algorithm achieves an average identification accuracy of 84.74% at a -10 dB to 20 dB signal-to-noise ratio(SNR)with fewer parameters and less computation than a network model with the same identification rate in a software radio dataset containing multiple USRP X310s from the same manufacturer,with fewer parameters and less computation than a network model with the same identification rate.The algorithm is used to identify measurement and control signals and ensure the security of the measurement and control link with theoretical and engineering applications.展开更多
Existing specific emitter identification(SEI)methods based on hand-crafted features have drawbacks of losing feature information and involving multiple processing stages,which reduce the identification accuracy of emi...Existing specific emitter identification(SEI)methods based on hand-crafted features have drawbacks of losing feature information and involving multiple processing stages,which reduce the identification accuracy of emitters and complicate the procedures of identification.In this paper,we propose a deep SEI approach via multidimensional feature extraction for radio frequency fingerprints(RFFs),namely,RFFsNet-SEI.Particularly,we extract multidimensional physical RFFs from the received signal by virtue of variational mode decomposition(VMD)and Hilbert transform(HT).The physical RFFs and I-Q data are formed into the balanced-RFFs,which are then used to train RFFsNet-SEI.As introducing model-aided RFFs into neural network,the hybrid-driven scheme including physical features and I-Q data is constructed.It improves physical interpretability of RFFsNet-SEI.Meanwhile,since RFFsNet-SEI identifies individual of emitters from received raw data in end-to-end,it accelerates SEI implementation and simplifies procedures of identification.Moreover,as the temporal features and spectral features of the received signal are both extracted by RFFsNet-SEI,identification accuracy is improved.Finally,we compare RFFsNet-SEI with the counterparts in terms of identification accuracy,computational complexity,and prediction speed.Experimental results illustrate that the proposed method outperforms the counterparts on the basis of simulation dataset and real dataset collected in the anechoic chamber.展开更多
Radio frequency fingerprint(RFF)identification is a promising technique for identifying Internet of Things(IoT)devices.This paper presents a comprehensive survey on RFF identification,which covers various aspects rang...Radio frequency fingerprint(RFF)identification is a promising technique for identifying Internet of Things(IoT)devices.This paper presents a comprehensive survey on RFF identification,which covers various aspects ranging from related definitions to details of each stage in the identification process,namely signal preprocessing,RFF feature extraction,further processing,and RFF identification.Specifically,three main steps of preprocessing are summarized,including carrier frequency offset estimation,noise elimination,and channel cancellation.Besides,three kinds of RFFs are categorized,comprising I/Q signal-based,parameter-based,and transformation-based features.Meanwhile,feature fusion and feature dimension reduction are elaborated as two main further processing methods.Furthermore,a novel framework is established from the perspective of closed set and open set problems,and the related state-of-the-art methodologies are investigated,including approaches based on traditional machine learning,deep learning,and generative models.Additionally,we highlight the challenges faced by RFF identification and point out future research trends in this field.展开更多
针对射频指纹识别中单一特征无法全面表示信号的完整性,且类间特征差异较小从而限制识别准确率等问题,提出了一种基于时频和双谱特征融合的DA-ResNeXt50(ResNeXt50 with dense connection and ACBlock)射频指纹识别方法。首先,对采集到...针对射频指纹识别中单一特征无法全面表示信号的完整性,且类间特征差异较小从而限制识别准确率等问题,提出了一种基于时频和双谱特征融合的DA-ResNeXt50(ResNeXt50 with dense connection and ACBlock)射频指纹识别方法。首先,对采集到的不同设备的信号分别进行短时傅里叶变换(short-time Fourier transform,STFT)和双谱变换,将得到的图像二值化处理并拼接,综合利用两种变换分别在时频域和高阶统计特性上的优势,更全面地提取和表征不同设备的射频指纹特征;然后,提出了DA-ResNeXt50网络模型,借鉴密集连接思想,使四层残差单元每一层都与前面所有层直接相连,促进了特征的复用和传递,能更好地捕捉类间细微差异;最后,使用非对称卷积模块(asymmetric convolution block,ACBlock)替换模型最后一层残差单元的3×3卷积,可以有效地增加网络的感受野,增强卷积核的骨架部分,从而提高射频指纹识别性能。实验结果表明,相较于使用单一特征提取方法,提出的特征融合方法的性能有较大的提升,改进后的模型与多种经典模型相比,具有较高的识别精度。展开更多
射频指纹识别(Radio Frequency Fingerprinting,RFF)技术为工业互联网提供了关键的数据采集和处理能力,然而,现存RFF技术存在识别率低的问题。因此,以人工智能模型为基础,提出一种基于轻量化残差神经网络的RFF算法,旨在优化射频信号的...射频指纹识别(Radio Frequency Fingerprinting,RFF)技术为工业互联网提供了关键的数据采集和处理能力,然而,现存RFF技术存在识别率低的问题。因此,以人工智能模型为基础,提出一种基于轻量化残差神经网络的RFF算法,旨在优化射频信号的特征提取和识别过程,在降低计算复杂度的同时,保持高识别性能。实验通过USRP2954设备采集信号并识别,结果显示,研究提出的方法在高信噪比环境下达到98.46%的识别率。研究的创新对工业互联网和智能制造领域具有重要意义,能够推动工业自动化和智能制造的进一步发展,提升整个工业系统的智能化水平。展开更多
基金supported by Innovation Talents Promotion Program of Shaanxi Province,China(No.2021TD08)。
文摘Radio frequency fingerprinting(RFF)is a remarkable lightweight authentication scheme to support rapid and scalable identification in the internet of things(IoT)systems.Deep learning(DL)is a critical enabler of RFF identification by leveraging the hardware-level features.However,traditional supervised learning methods require huge labeled training samples.Therefore,how to establish a highperformance supervised learning model with few labels under practical application is still challenging.To address this issue,we in this paper propose a novel RFF semi-supervised learning(RFFSSL)model which can obtain a better performance with few meta labels.Specifically,the proposed RFFSSL model is constituted by a teacher-student network,in which the student network learns from the pseudo label predicted by the teacher.Then,the output of the student model will be exploited to improve the performance of teacher among the labeled data.Furthermore,a comprehensive evaluation on the accuracy is conducted.We derive about 50 GB real long-term evolution(LTE)mobile phone’s raw signal datasets,which is used to evaluate various models.Experimental results demonstrate that the proposed RFFSSL scheme can achieve up to 97%experimental testing accuracy over a noisy environment only with 10%labeled samples when training samples equal to 2700.
基金supported by the National Natural Science Foundation of China(No.62027801).
文摘This study presents a radio frequency(RF)fingerprint identification method combining a convolutional neural network(CNN)and gated recurrent unit(GRU)network to identify measurement and control signals.The proposed algorithm(CNN-GRU)uses a convolutional layer to extract the IQ-related learning timing features.A GRU network extracts timing features at a deeper level before outputting the final identification results.The number of parameters and the algorithm’s complexity are reduced by optimizing the convolutional layer structure and replacing multiple fully-connected layers with gated cyclic units.Simulation experiments show that the algorithm achieves an average identification accuracy of 84.74% at a -10 dB to 20 dB signal-to-noise ratio(SNR)with fewer parameters and less computation than a network model with the same identification rate in a software radio dataset containing multiple USRP X310s from the same manufacturer,with fewer parameters and less computation than a network model with the same identification rate.The algorithm is used to identify measurement and control signals and ensure the security of the measurement and control link with theoretical and engineering applications.
基金supported by the National Natural Science Foundation of China(62061003)Sichuan Science and Technology Program(2021YFG0192)the Research Foundation of the Civil Aviation Flight University of China(ZJ2020-04,J2020-033)。
文摘Existing specific emitter identification(SEI)methods based on hand-crafted features have drawbacks of losing feature information and involving multiple processing stages,which reduce the identification accuracy of emitters and complicate the procedures of identification.In this paper,we propose a deep SEI approach via multidimensional feature extraction for radio frequency fingerprints(RFFs),namely,RFFsNet-SEI.Particularly,we extract multidimensional physical RFFs from the received signal by virtue of variational mode decomposition(VMD)and Hilbert transform(HT).The physical RFFs and I-Q data are formed into the balanced-RFFs,which are then used to train RFFsNet-SEI.As introducing model-aided RFFs into neural network,the hybrid-driven scheme including physical features and I-Q data is constructed.It improves physical interpretability of RFFsNet-SEI.Meanwhile,since RFFsNet-SEI identifies individual of emitters from received raw data in end-to-end,it accelerates SEI implementation and simplifies procedures of identification.Moreover,as the temporal features and spectral features of the received signal are both extracted by RFFsNet-SEI,identification accuracy is improved.Finally,we compare RFFsNet-SEI with the counterparts in terms of identification accuracy,computational complexity,and prediction speed.Experimental results illustrate that the proposed method outperforms the counterparts on the basis of simulation dataset and real dataset collected in the anechoic chamber.
基金supported in part by the National Natural Science Foundation of China under Grant 62171120 and 62001106National Key Research and Development Program of China(2020YFE0200600)+2 种基金Jiangsu Provincial Key Laboratory of Network and Information Security No.BM2003201Guangdong Key Research and Development Program under Grant2020B0303010001Purple Mountain Laboratories for Network and Communication Security
文摘Radio frequency fingerprint(RFF)identification is a promising technique for identifying Internet of Things(IoT)devices.This paper presents a comprehensive survey on RFF identification,which covers various aspects ranging from related definitions to details of each stage in the identification process,namely signal preprocessing,RFF feature extraction,further processing,and RFF identification.Specifically,three main steps of preprocessing are summarized,including carrier frequency offset estimation,noise elimination,and channel cancellation.Besides,three kinds of RFFs are categorized,comprising I/Q signal-based,parameter-based,and transformation-based features.Meanwhile,feature fusion and feature dimension reduction are elaborated as two main further processing methods.Furthermore,a novel framework is established from the perspective of closed set and open set problems,and the related state-of-the-art methodologies are investigated,including approaches based on traditional machine learning,deep learning,and generative models.Additionally,we highlight the challenges faced by RFF identification and point out future research trends in this field.
文摘针对射频指纹识别中单一特征无法全面表示信号的完整性,且类间特征差异较小从而限制识别准确率等问题,提出了一种基于时频和双谱特征融合的DA-ResNeXt50(ResNeXt50 with dense connection and ACBlock)射频指纹识别方法。首先,对采集到的不同设备的信号分别进行短时傅里叶变换(short-time Fourier transform,STFT)和双谱变换,将得到的图像二值化处理并拼接,综合利用两种变换分别在时频域和高阶统计特性上的优势,更全面地提取和表征不同设备的射频指纹特征;然后,提出了DA-ResNeXt50网络模型,借鉴密集连接思想,使四层残差单元每一层都与前面所有层直接相连,促进了特征的复用和传递,能更好地捕捉类间细微差异;最后,使用非对称卷积模块(asymmetric convolution block,ACBlock)替换模型最后一层残差单元的3×3卷积,可以有效地增加网络的感受野,增强卷积核的骨架部分,从而提高射频指纹识别性能。实验结果表明,相较于使用单一特征提取方法,提出的特征融合方法的性能有较大的提升,改进后的模型与多种经典模型相比,具有较高的识别精度。
文摘射频指纹识别(Radio Frequency Fingerprinting,RFF)技术为工业互联网提供了关键的数据采集和处理能力,然而,现存RFF技术存在识别率低的问题。因此,以人工智能模型为基础,提出一种基于轻量化残差神经网络的RFF算法,旨在优化射频信号的特征提取和识别过程,在降低计算复杂度的同时,保持高识别性能。实验通过USRP2954设备采集信号并识别,结果显示,研究提出的方法在高信噪比环境下达到98.46%的识别率。研究的创新对工业互联网和智能制造领域具有重要意义,能够推动工业自动化和智能制造的进一步发展,提升整个工业系统的智能化水平。
