A new correlation frequency analysis of the side channel
Bibliographic record
Abstract
Security in embedded computing systems is now an important concern for a diverse set of applications. However, the embedded hardware implementation may unintentionally leak information, through its electromagnetic emanations or current draw, which may lead to the revelation of secrets used in the cryptographic computations being performed. This paper presents an attack methodology and an empirical study, based on Correlation Analysis in the Frequency domain (CAF) with pre-characterization of the embedded system. Unlike previous research this analysis exploits the fact that a few frequencies are more likely to leak computing information, and are independent of the system clock (rather a function of the technology). Results indicate that the secret key can be reliably extracted from both hardware and software implementations of AES. The analysis presented is additionally tolerant to trace misalignments and has been tested with real power and electromagnetic (EM) traces used to extract 8-bit keys and full 128-bit keys. This research is important for providing more secure cryptographic computations necessary in many embedded systems.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 0.001 |
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".