Bibliographic record
Abstract
The Physical Unclonable Function (PUF) is a lightweight hardware solution that offers affordable hardware-based security for electronic devices and systems. Due to their unpredictable response generation and inability to be replicated, their utilization is rapidly growing in authentication and security applications. It presents an alternative to conventional cryptographic systems that require large chip area and memory storage. However, a significant concern lies in the vulnerability of PUFs to popular machine learning attacks, such as Covariance Matrix Adaptability and Evaluation Strategy (CMA-ES) attacks and Linear Regression (LR) attacks, among others. To mitigate these vulnerabilities, numerous PUF models have been proposed, aiming to make it difficult to determine the Challenge-Response Pairs (CRPs) for those PUFs. Among these models, Multi-PUFs (MPUFs) have gained popularity for their success in enhancing security. however, MPUFs demand considerable resources and exhibit relatively low PUF metric values, despite their improved security against ML attacks. To increase resource efficiency further, two new Multi-PUF technique has been proposed in this paper, aiming to achieve a balance between resource utilization and challenge obfuscation complexity. The key idea involves reducing the number of resources used in a PUF line while concurrently increasing complexity during challenge obfuscation. The proposed design is implemented and verified on the Arty A7 100t FPGA board using AMD Vivado and Vitis Environment. Experimental results show that resource usage has been significantly reduced, by 9 and 46.5 percent respectively, for the two proposed techniques.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".