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Updated: May 29, 2025

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Enhancing counterfeit RFID tag classification through distance based cognitive risk control.

Haifeng Wu1,2, Siyuan Wang3, Chongrong Pu3

  • 1School of Electrical and Information Engineering, Yunnan Minzu University, Kunming, 650500, China. whf5469@gmail.com.

Scientific Reports
|February 3, 2025
PubMed
Summary
This summary is machine-generated.

This study enhances radio frequency identification (RFID) tag authentication by using cognitive risk control to improve signal-to-noise ratio (SNR) and classification accuracy in low SNR settings. The method boosts accuracy by 11%, offering a robust solution against counterfeit tags.

Keywords:
Anti-counterfeitingCRCPHY identificationRFIDTag

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Cybersecurity

Background:

  • Physical layer identification is crucial for cost-effective anti-counterfeiting in radio frequency identification (RFID) systems.
  • Low signal-to-noise ratio (SNR) environments significantly degrade the accuracy of RFID tag classification.
  • Existing methods struggle to maintain high recognition precision under challenging detection conditions.

Purpose of the Study:

  • To introduce a cognitive risk control strategy to enhance RFID tag classification accuracy in low SNR environments.
  • To investigate the impact of fine-tuning tag distance for improved SNR and recognition precision.
  • To evaluate the benefits of expanded feature sets for classification efficacy.

Main Methods:

  • Implemented a cognitive risk control strategy adjusting tag distance to optimize SNR.
  • Extracted an enriched feature set comprising 104 time and frequency domain features.
  • Conducted classification experiments using software-defined radio devices with seven RFID tag types from three manufacturers.

Main Results:

  • The cognitive risk control strategy increased average tag classification accuracy by approximately 11%.
  • Utilizing 104 features improved accuracy by 4.3% and 5.3% compared to traditional 28 and 7 features, respectively.
  • The proposed methods demonstrated significant performance gains in low SNR conditions.

Conclusions:

  • Cognitive risk control is effective in improving RFID tag classification accuracy, especially in low SNR scenarios.
  • Expanding the feature set size further enhances classification performance.
  • The study provides a viable approach for combating counterfeit RFID tags through improved physical layer identification.