Radio frequency fingerprint identification(RFFI)shows great potential as a means for authenticating wireless devices.As RFFI can be addressed as a classification problem,deep learning techniques are widely utilized in...Radio frequency fingerprint identification(RFFI)shows great potential as a means for authenticating wireless devices.As RFFI can be addressed as a classification problem,deep learning techniques are widely utilized in modern RFFI systems for their outstanding performance.RFFI is suitable for securing the legacy existing Internet of Things(IoT)networks since it does not require any modifications to the existing end-node hardware and communication protocols.However,most deep learning-based RFFI systems require the collection of a great number of labelled signals for training,which is time-consuming and not ideal,especially for the Io T end nodes that are already deployed and configured with long transmission intervals.Moreover,the long time required to train a neural network from scratch also limits rapid deployment on legacy Io T networks.To address the above issues,two transferable RFFI protocols are proposed in this paper leveraging the concept of transfer learning.More specifically,they rely on fine-tuning and distance metric learning,respectively,and only require only a small amount of signals from the legacy IoT network.As the dataset used for transfer is small,we propose to apply augmentation in the transfer process to generate more training signals to improve performance.A Lo Ra-RFFI testbed consisting of 40 commercial-off-the-shelf(COTS)Lo Ra IoT devices and a software-defined radio(SDR)receiver is built to experimentally evaluate the proposed approaches.The experimental results demonstrate that both the fine-tuning and distance metric learning-based RFFI approaches can be rapidly transferred to another Io T network with less than ten signals from each Lo Ra device.The classification accuracy is over 90%,and the augmentation technique can improve the accuracy by up to 20%.展开更多
基金in part supported by UK Engineering and Physical Sciences Research Council under grant ID EP/V027697/1in part by the National Key Research and Development Program of China under grant ID 2020YFE0200600
文摘Radio frequency fingerprint identification(RFFI)shows great potential as a means for authenticating wireless devices.As RFFI can be addressed as a classification problem,deep learning techniques are widely utilized in modern RFFI systems for their outstanding performance.RFFI is suitable for securing the legacy existing Internet of Things(IoT)networks since it does not require any modifications to the existing end-node hardware and communication protocols.However,most deep learning-based RFFI systems require the collection of a great number of labelled signals for training,which is time-consuming and not ideal,especially for the Io T end nodes that are already deployed and configured with long transmission intervals.Moreover,the long time required to train a neural network from scratch also limits rapid deployment on legacy Io T networks.To address the above issues,two transferable RFFI protocols are proposed in this paper leveraging the concept of transfer learning.More specifically,they rely on fine-tuning and distance metric learning,respectively,and only require only a small amount of signals from the legacy IoT network.As the dataset used for transfer is small,we propose to apply augmentation in the transfer process to generate more training signals to improve performance.A Lo Ra-RFFI testbed consisting of 40 commercial-off-the-shelf(COTS)Lo Ra IoT devices and a software-defined radio(SDR)receiver is built to experimentally evaluate the proposed approaches.The experimental results demonstrate that both the fine-tuning and distance metric learning-based RFFI approaches can be rapidly transferred to another Io T network with less than ten signals from each Lo Ra device.The classification accuracy is over 90%,and the augmentation technique can improve the accuracy by up to 20%.
