Radio Frequency(RF) fingerprinting is one physical-layer authentication method for wireless communication, which uses the unique hardware characteristic of the transmitter to identify its true identity.To improve the ...Radio Frequency(RF) fingerprinting is one physical-layer authentication method for wireless communication, which uses the unique hardware characteristic of the transmitter to identify its true identity.To improve the performance of RF Fingerprint(RFF)based on preamble with fixed duration, a nonlinear RF fingerprinting method based on payload symbols is proposed for the wireless OFDM communication with the bit mapping scheme of QPSK. The wireless communication system is modeled as a Hammerstein system containing the nonlinear transmitter and multipath fading channel. A parameter separation technique based on orthogonal polynomial is presented for the estimation of the parameters of the Hammerstein system. The Hammerstein system parameter separation technique is firstly used to estimate the linear parameter with the training signal, which is used to compensate the adverse effect of the linear channel for the demodulation of the successive payload symbols. The demodulated payload symbols are further used to estimate the nonlinear coefficients of the transmitter with the Hammerstein system parameter separation technique again, which is used as the novel RFF for the authentication of the QPSK-OFDM device. Numerical simulations have verified the proposed method, which can also be extended to the OFDM signals with other bit mapping schemes.展开更多
The rapid growth of modern vehicles with advanced technologies requires strong security to ensure customer safety.One key system that needs protection is the passive key entry system(PKES).To prevent attacks aimed at ...The rapid growth of modern vehicles with advanced technologies requires strong security to ensure customer safety.One key system that needs protection is the passive key entry system(PKES).To prevent attacks aimed at defeating the PKES,we propose a novel radio frequency(RF)fingerprinting method.Our method extracts the cepstral coefficient feature as a fingerprint of a radio frequency signal.This feature is then analyzed using a convolutional neural network(CNN)for device identification.In evaluation,we conducted experiments to determine the effectiveness of different cepstral coefficient features and the convolutional neural network-based model.Our experimental results revealed that the Gammatone Frequency Cepstral Coefficient(GFCC)was the most compelling feature compared to Mel-Frequency Cepstral Coefficient(MFCC),Inverse Mel-Frequency Cepstral Coefficient(IMFCC),Linear-Frequency Cepstral Coefficient(LFCC),and Bark-Frequency Cepstral Coefficient(BFCC).Additionally,we experimented with evaluating the effectiveness of our method in comparison to existing approaches that are similar to ours.展开更多
在分析现有基于(EPC Class 1Gen,2EPCGen2)标准的轻量级RFID相互认证协议的基础上,提出了一种符合EPCGen2标准的基于射频指纹的RFID认证协议。协议融合了RFID设备的物理层信息,实现了RFID标签的跨层融合认证,具有增强RFID系统安全强度...在分析现有基于(EPC Class 1Gen,2EPCGen2)标准的轻量级RFID相互认证协议的基础上,提出了一种符合EPCGen2标准的基于射频指纹的RFID认证协议。协议融合了RFID设备的物理层信息,实现了RFID标签的跨层融合认证,具有增强RFID系统安全强度的特点。分析显示,提出协议具有相互认证、私密性、防止重放攻击、防止去同步攻击等安全性能,尤其能有效对抗RFID标签的克隆攻击。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
随着物联网的发展和无线射频识别技术(radio frequency identification,RFID)的广泛应用,RFID系统的安全问题越发突出。其中克隆标签的出现极大地阻碍了RFID系统的大规模发展,成为当前一个亟待解决的难题。通过总结分析目前RFID克隆标...随着物联网的发展和无线射频识别技术(radio frequency identification,RFID)的广泛应用,RFID系统的安全问题越发突出。其中克隆标签的出现极大地阻碍了RFID系统的大规模发展,成为当前一个亟待解决的难题。通过总结分析目前RFID克隆标签检测领域的一些主流方法,旨在为后续研究更有效的RFID克隆标签检测策略奠定基础。针对目前已知的一些检测方法,该文将克隆标签检测方法归纳总结为射频指纹、同步秘密、轨迹分析和碰撞检测四大类,并较为系统地对这四大类方法所包含的具体策略进行了研究,同时对这些方法策略进行了横向与纵向的对比分析。目前这四类方法都存在一定的缺陷,导致其无法直接应用于现有RFID系统进行克隆标签检测或者应用条件较苛刻。针对目前这些方法存在的问题,认为匿名RFID系统克隆标签的分布式检测是未来的一个主要研究方向。展开更多
提出了一种根据接收正交频分复用(orthogonal frequency division multiplexing,OFDM)信号估计发射机IQ不平衡与非线性,并以此作为发射机指纹进行通信设备身份认证的方法.首先根据共轭对称导频估计多径信道脉冲响应,接着根据信道脉冲响...提出了一种根据接收正交频分复用(orthogonal frequency division multiplexing,OFDM)信号估计发射机IQ不平衡与非线性,并以此作为发射机指纹进行通信设备身份认证的方法.首先根据共轭对称导频估计多径信道脉冲响应,接着根据信道脉冲响应估计、共轭反对称导频与非线性功放的线性近似放大倍数估计发射机的IQ不平衡参数组合,然后进行发射机非线性的B-Spline神经网络模型系数估计,最后从非线性模型系数估计中提取相似因子,与IQ不平衡参数组合估计构成发射机指纹的特征矢量后进行通信设备身份的识别或确认.理论推导与数值仿真显示,该方法可用于OFDM通信设备的物理层高强度认证与防假冒等.展开更多
With the recent introduction of NarrowBand Internet of Things(NB-IoT)technology in the 4th and 5th generations of mobile radio networks,the mobile communications context opens up significantly to the world of sensors....With the recent introduction of NarrowBand Internet of Things(NB-IoT)technology in the 4th and 5th generations of mobile radio networks,the mobile communications context opens up significantly to the world of sensors.By means of NB-IoT,the mobile systems within 3GPP standardization introduce the peculiar functions of sensor networks,thus making it possible to satisfy very specific requirements with respect to those which characterize traditional mobile telecommunications.Among the functions of interest for sensor networks,the possibility of locating the positions of the sensors without an increase in costs and energy consumption of the sensor nodes is of utmost interest.The present work describes a procedure for locating the NB-IoT nodes based on the quality of radio signals received by the mobile terminals,which therefore does not require further hardware implementations on board the nodes.This procedure,based on the RF fingerprinting technique and on machine learning processing,has been tested experimentally and has achieved interesting performances.展开更多
文摘Radio Frequency(RF) fingerprinting is one physical-layer authentication method for wireless communication, which uses the unique hardware characteristic of the transmitter to identify its true identity.To improve the performance of RF Fingerprint(RFF)based on preamble with fixed duration, a nonlinear RF fingerprinting method based on payload symbols is proposed for the wireless OFDM communication with the bit mapping scheme of QPSK. The wireless communication system is modeled as a Hammerstein system containing the nonlinear transmitter and multipath fading channel. A parameter separation technique based on orthogonal polynomial is presented for the estimation of the parameters of the Hammerstein system. The Hammerstein system parameter separation technique is firstly used to estimate the linear parameter with the training signal, which is used to compensate the adverse effect of the linear channel for the demodulation of the successive payload symbols. The demodulated payload symbols are further used to estimate the nonlinear coefficients of the transmitter with the Hammerstein system parameter separation technique again, which is used as the novel RFF for the authentication of the QPSK-OFDM device. Numerical simulations have verified the proposed method, which can also be extended to the OFDM signals with other bit mapping schemes.
基金supported by Institute of Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea Government(MIST)(No.2022-0-01022,Development of Collection and Integrated Analysis Methods of Automotive Inter/Intra System Artifacts through Construction of Event-Based Experimental System).
文摘The rapid growth of modern vehicles with advanced technologies requires strong security to ensure customer safety.One key system that needs protection is the passive key entry system(PKES).To prevent attacks aimed at defeating the PKES,we propose a novel radio frequency(RF)fingerprinting method.Our method extracts the cepstral coefficient feature as a fingerprint of a radio frequency signal.This feature is then analyzed using a convolutional neural network(CNN)for device identification.In evaluation,we conducted experiments to determine the effectiveness of different cepstral coefficient features and the convolutional neural network-based model.Our experimental results revealed that the Gammatone Frequency Cepstral Coefficient(GFCC)was the most compelling feature compared to Mel-Frequency Cepstral Coefficient(MFCC),Inverse Mel-Frequency Cepstral Coefficient(IMFCC),Linear-Frequency Cepstral Coefficient(LFCC),and Bark-Frequency Cepstral Coefficient(BFCC).Additionally,we experimented with evaluating the effectiveness of our method in comparison to existing approaches that are similar to ours.
文摘在分析现有基于(EPC Class 1Gen,2EPCGen2)标准的轻量级RFID相互认证协议的基础上,提出了一种符合EPCGen2标准的基于射频指纹的RFID认证协议。协议融合了RFID设备的物理层信息,实现了RFID标签的跨层融合认证,具有增强RFID系统安全强度的特点。分析显示,提出协议具有相互认证、私密性、防止重放攻击、防止去同步攻击等安全性能,尤其能有效对抗RFID标签的克隆攻击。
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.
文摘随着物联网的发展和无线射频识别技术(radio frequency identification,RFID)的广泛应用,RFID系统的安全问题越发突出。其中克隆标签的出现极大地阻碍了RFID系统的大规模发展,成为当前一个亟待解决的难题。通过总结分析目前RFID克隆标签检测领域的一些主流方法,旨在为后续研究更有效的RFID克隆标签检测策略奠定基础。针对目前已知的一些检测方法,该文将克隆标签检测方法归纳总结为射频指纹、同步秘密、轨迹分析和碰撞检测四大类,并较为系统地对这四大类方法所包含的具体策略进行了研究,同时对这些方法策略进行了横向与纵向的对比分析。目前这四类方法都存在一定的缺陷,导致其无法直接应用于现有RFID系统进行克隆标签检测或者应用条件较苛刻。针对目前这些方法存在的问题,认为匿名RFID系统克隆标签的分布式检测是未来的一个主要研究方向。
文摘提出了一种根据接收正交频分复用(orthogonal frequency division multiplexing,OFDM)信号估计发射机IQ不平衡与非线性,并以此作为发射机指纹进行通信设备身份认证的方法.首先根据共轭对称导频估计多径信道脉冲响应,接着根据信道脉冲响应估计、共轭反对称导频与非线性功放的线性近似放大倍数估计发射机的IQ不平衡参数组合,然后进行发射机非线性的B-Spline神经网络模型系数估计,最后从非线性模型系数估计中提取相似因子,与IQ不平衡参数组合估计构成发射机指纹的特征矢量后进行通信设备身份的识别或确认.理论推导与数值仿真显示,该方法可用于OFDM通信设备的物理层高强度认证与防假冒等.
文摘With the recent introduction of NarrowBand Internet of Things(NB-IoT)technology in the 4th and 5th generations of mobile radio networks,the mobile communications context opens up significantly to the world of sensors.By means of NB-IoT,the mobile systems within 3GPP standardization introduce the peculiar functions of sensor networks,thus making it possible to satisfy very specific requirements with respect to those which characterize traditional mobile telecommunications.Among the functions of interest for sensor networks,the possibility of locating the positions of the sensors without an increase in costs and energy consumption of the sensor nodes is of utmost interest.The present work describes a procedure for locating the NB-IoT nodes based on the quality of radio signals received by the mobile terminals,which therefore does not require further hardware implementations on board the nodes.This procedure,based on the RF fingerprinting technique and on machine learning processing,has been tested experimentally and has achieved interesting performances.
