An Efficient and Reliable Lightweight PUF for IoT-based Applications
Bibliographic record
Abstract
Silicon physical unclonable functions (sPUFs) exploit manufacturing process variations of semiconductor integrated circuits (ICs) to protect IoT-based devices from new cyberattacks. In this paper, a novel security technique, namely, an efficient lightweight configurable-based ring oscillator PUFs (c-ROPUFs)design, is proposed to enhance IoT-based reliability. The c-ROPUF is a low area design, mapped in a single CLB, and easy to implement on reconfigurable hardware. Data samples are collected under varying temperatures and supply voltage over a population of 30 Spartan-3E FPGA chips. The reliability ofc-ROPUF is identified and evaluated based on the standards of the International Organization for Standardization (ISO) in terms of reliability. With the application of the proposed 1-out-of-encoding algorithm, our results demonstrate that c-ROPUF shows magnified average reliability of 99.63% as compared to earlier PUF designs. Finally, the results also show that the c-ROPUFdesign is immune from accelerated aging impacts with no bitflip, leading to reliability issues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".