RFID Scheme for IoT Devices Based on LSTM-CNN

Abstract

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%.