A Low-Power High-Performance Concurrent Fault Detection Approach for the Composite Field S-Box and Inverse S-Box
Bibliographic record
Abstract
The high level of security and the fast hardware and software implementations of the Advanced Encryption Standard have made it the first choice for many critical applications. Nevertheless, the transient and permanent internal faults or malicious faults aiming at revealing the secret key may reduce its reliability. In this paper, we present a concurrent fault detection scheme for the S-box and the inverse S-box as the only two nonlinear operations within the Advanced Encryption Standard. The proposed parity-based fault detection approach is based on the low-cost composite field implementations of the S-box and the inverse S-box. We divide the structures of these operations into three blocks and find the predicted parities of these blocks. Our simulations show that except for the redundant units approach which has the hardware and time overheads of close to 100 percent, the fault detection capabilities of the proposed scheme for the burst and random multiple faults are higher than the previously reported ones. Finally, through ASIC implementations, it is shown that for the maximum target frequency, the proposed fault detection S-box and inverse S-box in this paper have the least areas, critical path delays, and power consumptions compared to their counterparts with similar fault detection capabilities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".