Design and Evaluation of a Hybrid Chaotic-Bistable Ring PUF
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A physical unclonable function (PUF) is a promising lightweight circuit that provides security and authentication capability for electronic devices with low computational resources. Among various PUFs, the bistable ring PUF (BR-PUF) is considered one of the robust configurations. However, it has been shown that the challenge-response pairs (CRPs) from BR-PUF are vulnerable to statistical machine learning (ML) attacks, such as k-junta learning, support vector machine (SVM), and logistic regression (LR). In this article, we first show that the k-junta attack can break CRPs from the BR-PUF. Then, we present a hybrid chaotic-BR-PUF structure that obfuscates the BR-PUF response with the nonlinearized chaotic response. The proposed PUF structure has been implemented and experimentally evaluated on Xilinx Artix-7 FPGA, and the PUF measurements were captured. The proposed PUF was tested with a powerful statistical method developed using k-junta-based learning to confirm its strength against such attacks and evaluated using CRPs collected. The proposed PUF provides better resistance against ML attacks and reduces the learning accuracy to 50%–60% compared with previously proposed PUFs.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it