An efficient fault-tolerance technique for the Keyed-Hash Message Authentication Code
Bibliographic record
Abstract
The growing demand for secure communications has lead to the utilization of cryptographic mechanisms on-board spacecrafts. However, that it not a trivial task due to sensitivity of cryptographic primitives to bit-flips, which are commonly caused by the radiation found in space. On-board processing has mitigated single event upsets (SEUs) by employing the traditional triple modular redundancy (TMR), but that technique incurs into huge area and energy penalties. This paper introduces an efficient approach to achieve fault tolerance in data origin authentication mechanisms based on the Keyed-Hash Message Authentication Code (HMAC). The proposed scheme achieves very high resistance against SEUs while reducing implementation area requirements and energy consumption compared to TMR. Results obtained through FPGA implementation show that HMAC-SHA512 utilizes 53% less area and consumes 25% less energy compared to the traditional TMR technique. Furthermore, the memory and registers of this hardware module are respectively 386 and 1140 times more resistant against SEUs than TMR. These results are crucial for substituting TMR with more efficient strategies therefore contributing to the achievement of higher levels of security in space systems.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".