Concurrent Structure-Independent Fault Detection Schemes for the Advanced Encryption Standard
Bibliographic record
Abstract
The Advanced Encryption Standard (AES) has been lately accepted as the symmetric cryptography standard for confidential data transmission. However, the natural and malicious injected faults reduce its reliability and may cause confidential information leakage. In this paper, we study concurrent fault detection schemes for reaching a reliable AES architecture. Specifically, we propose low-cost structure-independent fault detection schemes for the AES encryption and decryption. We have obtained new formulations for the fault detection of SubBytes and inverse SubBytes using the relation between the input and the output of the S-box and the inverse S-box. The proposed schemes are independent of the way the S-box and the inverse S-box are constructed. Therefore, they can be used for both the S-boxes and the inverse S-boxes using lookup tables and those utilizing logic gates based on composite fields. Our simulation results show the error coverage of greater than 99 percent for the proposed schemes. Moreover, the proposed and the previously reported fault detection schemes have been implemented on the most recent Xilinx Virtex FPGAs. Their area and delay overheads have been compared and it is shown that the proposed schemes outperform the previously reported ones.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".