A machine learning attack resistant multi-PUF design on FPGA
Bibliographic record
Abstract
Current approaches for building physical unclonable function (PUF) designs resistant to machine learning attacks often suffer from large resource overhead and are typically difficult to implement on field programmable gate arrays (FPGAs). In this paper we propose a new arbiter-based multi-PUF (MPUF) design that utilises a Weak PUF to obfuscate the challenges to a Strong PUF and is harder to model than the conventional arbiter PUF using machine learning attacks. The proposed PUF design shows a greater resistance to attacks, which have been successfully applied to other Arbiter PUFs. A mathematical model is presented to analyse the complexity and obfuscation properties of the proposed PUF design. Moreover, we show that it is feasible to implement the proposed MPUF design on a Xilinx Artix-7 FPGA, and that it achieves a good uniqueness result of 40.60 % and uniformity of 37.03 %, which significantly improves over previous work into multi-PUF designs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".