A Sliding Window Phase-Only Correlation Method for Side-Channel Alignment in a Smartphone
Bibliographic record
Abstract
Future wireless embedded devices will be increasingly powerful, supporting many more applications including one of the most crucial, security. Although many embedded devices offer resistance to bus probing attacks due to their compact size and high levels of integration, susceptibility to attacks on their electromagnetic side channel must be analyzed. This side channel is often quite complex to analyze due to the complexities of the embedded device including operating system, interrupts, and so forth. This article presents a new methodology for analyzing a complex system's vulnerability to the EM side channel. The methodology proposes a sliding window phase-only correlation method for aligning electromagnetic emanations from a complex smartphone running native code utilizing an on-chip cache. Unlike previous research, experimental results demonstrate that data written to on-chip cache within an advanced 312MHz 0.13um processor executing AES can be attacked utilizing this new methodology. Furthermore, for the first time, it has been shown that the point of side-channel attack is not a spike of increased EM but an area of low EM amplitude, unlike what is noted in previous findings. This research is important for advancing side-channel analysis understanding in complex embedded processors and ensuring secure implementations in future embedded ubiquitous devices.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".