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

2025-01-26

AI Summary

Detecting unknown unauthorized wireless devices in open environments remains challenging, and radio-frequency (RF) fingerprint interference among legitimate devices severely degrades identification accuracy. Method: This paper proposes the first end-to-end open-set RF fingerprint recognition framework, innovatively integrating joint prediction with siamese comparison to decouple coarse-grained class discrimination from fine-grained similarity verification. It further incorporates hardware distortion modeling and a dedicated open-set loss function. Contribution/Results: The framework enables zero-shot detection and identification of previously unseen devices without requiring prior knowledge. Experimental results across multiple simulated scenarios demonstrate an unauthorized device detection accuracy of 98.7% and a 12.4% improvement in cross-device-class F1-score. These outcomes significantly enhance model generalization and robustness under realistic open-world conditions.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• RF Fingerprinting
• Wireless Device Identification
• Signal Interference

Innovation

• JRFFP-SC
• Predictive and Contrastive Buddy Framework
• Radio Frequency Fingerprint Recognition