A Time-Varying and Time-Invariant RF Fingerprint Extraction Approach for IoT Device Identification

Radio Frequency Fingerprinting (RFF)-based identification methods have the potential to enhance the security of the Internet of Things (IoT). However, conventional fingerprinting techniques based on standard sample rates face limitations related to noise and device scale. The utilization of high sample rate receivers offers a promising solution to mitigate these constraints. Nonetheless, the challenge lies in extracting RFFs from the collected ultra-long signals. Image-based methods, which accumulate signals in the time domain, reduce the difficulty of extracting RFFs from long signals but overlook the fine-grained RFFs in the time domain. To solve this problem, this paper proposes RFF modeling for long signals, emphasizing the importance of obtaining short-term time-varying RFFs and time-invariant RFFs. Combining an analysis of the inductive biases of convolutional neural networks, we propose a backbone network named GResNet, which is capable to effectively extract these two types of RFFs. An information fusion module is added to improve identification performance. Extensive experiments are conducted with 100 LoRa devices, demonstrating that our method outperforms existing RFFI techniques based on standard sample rate or high sample rate signals. Furthermore, our approach maintains robust performance within a wide range of SNRs.

I. Introduction

The development of the Internet of Things (IoT) allows more and more wireless network devices to connect with each other, which also brings security risks. IoT devices can be easily impersonated by attackers due to their strict restrictions in hardware costs, power consumption and device size [1]. Radio frequency fingerprint identification (RFFI) technology is proposed for device identity authentication [2]. Signal distortions caused by device hardware impairments are utilized as RF fingerprints (RFFs) of transmitters. The security of an RFFI system is ensured by the fact that RFFs are unique characteristics and are difficult to forge.