A SEU-resistant, FPGA-based implementation of the substitution transformation in AES for security on satellites
Bibliographic record
Abstract
Designing single event upset (SEU)-resistant security for communications in satellites is an important yet challenging problem. For example, although SRAM-based FPGAs are beneficial for satellite applications, they are susceptible to SEUs. Harsh environments such as space where cosmic radiation is present increase the likelihood of these errors known as SEUs. However these errors are also expected to be prevalent in non-space applications of future nanometer technologies. Thus this is an important problem to be studied for future secure embedded systems. Satellites require an encryption mechanism for many purposes; for example, to provide secure communications with the ground station. A SEU detection technique for a symmetric encryption algorithm, such as the NIST standardized Advanced Encryption Standard (AES), is additionally challenging due to its complex non-linear task in the algorithm, namely the substitution transformation (sub_byte). This research presents an efficient solution for single-bit SEU detection in the substitution task of AES. This approach uses fewer memory cells, provides 100% single-bit SEU coverage and achieves a low failure in time (FIT). This research is important for secure communications in an error-prone harsh environment such as satellites where low cost and high reliability are important.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".