FWSResNet: An Edge Device Fingerprinting Framework Based on Scattering and Convolutional Networks

Lightweight device authentication is a critical aspect in edge computing to guarantee the rightness of edge device identities and services. Radio frequency fingerprinting (RFF) is such an enabling technology able to provide robust and affordable security by employing the unique device and channel features which are usually extracted via machine learning (ML) method. The challenge is how to realize strong interpretability and support sufficient generalization ability under non-stationary channel characteristics. To solve this, we in this paper propose a novel hybrid network named FWSResNet which exploits fractional wavelet scattering transform and residual neural network to deal with the device and channel features subtlety. In particular, the proposed FWSResNet uses the scattering network based on fractional domain wavelet transform to extract the low and high-frequency features of the input signal through multiscale fractional wavelet. We find that this design can be robust to non-stationary signals and can extract the key features of noise signals. We also present a comprehensive theoretical analysis of the performance of FWSResNet under non-stationary signal distortion. Finally, we evaluate the hybrid network under largescale long term evolution (LTE) data in the practical application scenario and show that our proposed FWSResNet can achieve 93% recognition accuracy rate with only 280 training samples per device, and achieve up to 99.5% when training samples increase to 4200 per device.