A secure test solution for sensor nodes containing crypto-cores
Bibliographic record
Abstract
There is a tradeoff between the requirements for security and testability for a sensor node hardware. To test a sensor, it is desired to have access to the internal circuitry of the Device-Under-Test (DUT) to apply test stimuli and observe its responses. While such unrestricted access to the DUT is desired for testing, it can undermine the security. To secure a sensor node from attacks by malicious attackers, it is imperative to limit user access once the device has been adopted for in-field use. Efficient design-for-testability (DFT) techniques have been developed without taking into consideration the security threats posed by them. For instance, scan structure which is widely deployed in modern digital circuits, can be used as an effective tool to wage an attack and extract critical information from cryptographic cores. In this work, a new solution is presented to protect sensor nodes containing crypto-cores against scan-based attacks without compromising their testability at the manufacturing phase. In the proposed solution, a built-in self-test (BIST) technique is developed to carry out in-field tests for crypto-cores while a scan-based test method is utilized for manufacturing test. The proposed method prevents scan-based attacks without compromising testability during the manufacturing phase.
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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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".