A new correlation frequency analysis of the side channel
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
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.
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 | 0.002 |
| 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