TEA-RFFI: Temperature adjusted radio frequency fingerprint-based smartphone identification

Article Computer Science, Theory & Methods

Towards Scalable and Channel-Robust Radio Frequency Fingerprint Identification for LoRa

Guanxiong Shen et al.

Summary: This paper presents a device authentication technique called Radio Frequency Fingerprint Identification (RFFI) based on transmitter hardware impairments. The proposed framework uses a deep learning-powered RFF extractor and channel independent features for scalable and channel-robust authentication.

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY (2022)

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Article Computer Science, Theory & Methods

RF Impairment Model-Based IoT Physical-Layer Identification for Enhanced Domain Generalization

Sekhar Rajendran et al.

Summary: For small, inexpensive, and power-constrained IoT devices, Radiofrequency fingerprinting (RF-fingerprinting) has emerged as a cost-effective security solution. However, the robustness and permanence of RF-fingerprints (RFFs) have been major challenges. This paper presents a solution to overcome these challenges by performing a reverse analysis of a typical RFIC and creating a parametric RF-impairment distribution model. This model is used to tailor a knowledge-based parametric signal processing and conditioning method, reducing dimensionality of data and simplifying the classifier needed for RF-fingerprinting.

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY (2022)

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Article Engineering, Electrical & Electronic

Radio Frequency Fingerprint Identification for LoRa Using Deep Learning

Guanxiong Shen et al.

Summary: The paper presents a deep learning-based RFFI scheme for LoRa systems, investigating signal representations and CFO compensation to improve system stability and accuracy. Experimental results suggest that the spectrogram-CNN model is optimal for classifying LoRa devices.

IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS (2021)

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Article Computer Science, Information Systems