An FPGA implementation of AES with fault analysis countermeasures
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
Fault analysis attacks are powerful cryptanalytic tools that are applicable to many types of cryptosystems. Inducing multiple transient faults and observing the output of the faulty cryptographic device may allow the attacker to collect sufficient information for extracting secret keys and even using the device after breaking the cipher. In this paper, we investigate several options for fault analysis resistant FPGA implementations of the Advanced Encryption Standard (AES), which has become the default choice for various security services in many applications since its adaption as a new encryption standard by NIST. In particular, we compare the throughput and area overheads associated with parity based error detection and (algorithm level, round level and operation level) redundancy based countermeasures. Our comparison also include implementations that already employ some additional countermeasures against power analysis attacks.
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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 | 0.001 |
| 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) | 0.000 | 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