Efficient Normalizing Flow-Based Radio Frequency Fingerprinting Identification for Network Security

Device authentication plays a key role in securing Internet of Things (IoT), where radio frequency fingerprinting (RFF) identification is an emerging physical layer security technique by exploiting intrinsic and unique hardware impairments of wireless devices. However, recent works mainly focus on the identification of authorized devices, while neglecting the harm misidentification of illegal devices. Thus, this paper proposes a normalizing flow-based RFF method (NFRFF) for illegal device identification. The proposed NFRFF designs parallel flows and a fusion flow to handle the distributions of radio frequency signal samples and employs the dual attention mechanism to enhance the efficiency of information fusion and the ability to recognize illegal wireless devices. Ultimately, NFRFF is capable of assigning lower likelihoods to signal samples from illegal devices and higher ones to those from authorized devices. Simulation results verify that after extracting multi-scale radio frequency feature maps using VGG-16, NFRFF achieves an AUROC of 0.991 under 120 authorized devices and 30 illegal devices, achieving excellent identification performance.