DeepPUFSCA: Deep learning for Physical Unclonable Function attack based on Side Channel Analysis support
Bibliographic record
Abstract
Physical Unclonable Function (PUF) poses a vulnerability that it could be imitated by machine learning attacks and side channel attacks, which break its physical uniqueness and unpredictable characteristic. Hence, many works are concerned with enhancing PUF design by introducing more nonlinear modules inside to differentiate approximating PUF behavior from the attacker side. However, the safety of these PUFs are still an open area and need to be verified. In this paper, we propose DeepPUFSCA, which is a deep learning-based model that uniquely combines both challenge and side-channel information features during training to attack PUF. To gather the data, we conduct a design of an arbiter PUF on FPGA and measure its power consumption. Our intensive experiments on this dataset demonstrate that DeepPUFSCA outperforms other machine learning-based methods in terms of attacking accuracy, even the novel ensemble algorithms. Moreover, we also show that combined side channel information boosts the model performance compared to attacking with challenge-response only.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".