A Lightweight High-Performance Fault Detection Scheme for the Advanced Encryption Standard Using Composite Fields
Bibliographic record
Abstract
The faults that accidently or maliciously occur in the hardware implementations of the Advanced Encryption Standard (AES) may cause erroneous encrypted/decrypted output. The use of appropriate fault detection schemes for the AES makes it robust to internal defects and fault attacks. In this paper, we present a lightweight concurrent fault detection scheme for the AES. In the proposed approach, the composite field S-box and inverse S-box are divided into blocks and the predicted parities of these blocks are obtained. Through exhaustive searches among all available composite fields, we have found the optimum solutions for the least overhead parity-based fault detection structures. Moreover, through our error injection simulations for one S-box (respectively inverse S-box), we show that the total error coverage of almost 100% for 16 S-boxes (respectively inverse S-boxes) can be achieved. Finally, it is shown that both the application-specific integrated circuit and field-programmable gate-array implementations of the fault detection structures using the obtained optimum composite fields, have better hardware and time complexities compared to their counterparts.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".