Data-Enhancement-Aided Protocol-Agnostic Transmitter Recognition for Open-Set in IoT

Abstract

Radio Frequency Fingerprinting Identification (RFFI), based on hardware inherent imperfections, is an effective technique for physical authentication in the Internet of Things (IoT). However, due to the nonstationary and open factors in realistic scenes, problems such as inaccurate fingerprinting extractions and unreliable transmitter identifications often occur. Specifically, in actual scenarios, there are challenges such as varying channel status, variable transmission content, insufficient dataset acquisition, and unknown number of rogue devices. In this article, a robust and protocol-agnostic RFFI system is proposed for open-set recognition, which is achieved by a slicing-enhanced preprocessing with noise augmentation and a well-designed bone network with a modified open layer. Moreover, in order to verify the effectiveness and transferability of the designed RFFI system in applications of IoT, comparative experiments are conducted using a real-world dataset of Automatic Dependent Surveillance-Broadcast (ADS-B) signals and the open-source FIT/CorteXlab dataset. The performance of the designed extractor and classifier, under varying channel and openness conditions, verifies the channel feasibility and openness generalization of our designed system. The results of the ablation experiment indicate that the designed RFFI system can achieve an accuracy of 96.28% in the classification of 50 registered IoT transmitters and 87.52% in open-set recognition of mixed 50 registered and 10 unregistered IoT transmitters.