A Quantitative Analysis of a Novel SEU-Resistant SHA-2 and HMAC Architecture for Space Missions Security
Bibliographic record
Abstract
The increasing demand for more secure operation of space missions has led to emergence of cryptographic mechanisms aboard spacecrafts. However, cryptographic applications are extremely sensitive to bit-flips caused by radiation-induced single event upsets (SEUs). A traditional approach to mitigate SEUs in space applications has been the triple modular redundancy (TMR). However, such technique incurs large overheads in implementation area and power. An efficient approach to achieve fault tolerance in the secure hash standard (SHS) and in the keyed-hash message authentication code (HMAC) is introduced. When compared with TMR the proposed scheme not only achieves higher resistance against SEUs, but it also reduces implementation area requirements and power consumption. Results obtained through field-programmable gate array (FPGA) implementation show that HMAC/SHA-512 (secure hash algorithm) utilizes, on average, 53% less area and less power compared with the traditional TMR technique. Furthermore, the memory and registers of the HMAC/SHA-512 module are approximately 171 and 491 times more resistant against SEUs than TMR. This research is crucial for enabling the efficient employment of security mechanisms onboard 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) | 0.005 | 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".