Concurrent Structure-Independent Fault Detection Schemes for the Advanced Encryption Standard
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it