NeuroShield is a device-agnostic foundation model using a dual-stage transformer for EEG authentication, pretrained on 15,762 subjects across three datasets and showing EER reductions of 0.44-8.06 pp on two unseen downstream datasets after fine-tuning.
An introduction to biometric recognition,
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SRAM PUF fingerprints with Hamming code and temporal majority voting enable IIoT authentication with post-correction BER below 1%, using a design budget to trade response length against error-correction overhead.
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NeuroShield: A Device-Agnostic Foundation Model for EEG Authentication
NeuroShield is a device-agnostic foundation model using a dual-stage transformer for EEG authentication, pretrained on 15,762 subjects across three datasets and showing EER reductions of 0.44-8.06 pp on two unseen downstream datasets after fine-tuning.
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Secure Authentication in Wireless IoT: Hamming Code Assisted SRAM PUF as Device Fingerprint
SRAM PUF fingerprints with Hamming code and temporal majority voting enable IIoT authentication with post-correction BER below 1%, using a design budget to trade response length against error-correction overhead.