Open Set RF Fingerprinting Identification: A Joint Prediction and Siamese Comparison Framework

TL;DR

This paper introduces JRFFP-SC, a novel framework combining fingerprint prediction and siamese comparison to improve open set radio frequency device identification, effectively detecting unknown devices and reducing interference.

Contribution

The paper proposes a joint prediction and comparison framework that enhances open set RFFI by addressing unknown device detection and feature interference issues.

Findings

Achieves high rogue device detection accuracy

Demonstrates strong generalization to unseen devices

Reduces inter-class feature interference

Abstract

Radio Frequency Fingerprinting Identification (RFFI) is a lightweight physical layer identity authentication technique. It identifies the radio-frequency device by analyzing the signal feature differences caused by the inevitable minor hardware impairments. However, existing RFFI methods based on closed-set recognition struggle to detect unknown unauthorized devices in open environments. Moreover, the feature interference among legitimate devices can further compromise identification accuracy. In this paper, we propose a joint radio frequency fingerprint prediction and siamese comparison (JRFFP-SC) framework for open set recognition. Specifically, we first employ a radio frequency fingerprint prediction network to predict the most probable category result. Then a detailed comparison among the test sample's features with registered samples is performed in a siamese network. The proposed JRFFP-SC framework eliminates inter-class interference and effectively addresses the challenges associated with open set identification. The simulation results show that our proposed JRFFP-SC framework can achieve excellent rogue device detection and generalization capability for classifying devices.