A Multi-Sampling Convolutional Neural Network-Based RF Fingerprinting Approach for Low-Power Devices

Radio frequency (RF) fingerprint-based identification and authentication can improve the security of the Internet of Things (IoT). However, with the ever-increasing scale of low-power devices in IoT, how to address the semi-steady behavior of low-power devices owing to sleep mode switching and improve the identification accuracy in large-scale scenario become a new challenge. To tackle the above problems, this paper presents a multi-sampling convolutional neural network (MSCNN) to extract RF fingerprint based on the adaptive region of interest (ROI) selection strategy. The proposed MSCNN use multiple downsampling transformations for multi-scale feature extraction and classification automatically. Extensive experiments with 54 CC2530 devices as targets are conducted to demonstrate the feasibility and reliability of this method. The classification accuracy is as high as 97.0% under the line-of-sight (LOS) scenarios around SNR=30 dB. Our scheme is robust over a wide range of SNRs under the LOS scenarios as well as under the non-line-of-sight (NLOS) scenarios.