Exploring Security of Embedded SRAM in PIC and RISC-V Chips: Insights from Image Processing of Low-Cost Photon Emission Microscopy
Bibliographic record
Abstract
This paper delves into the analysis of photon emissions from SRAM in two distinct microcontrollers, utilizing a cost-effective setup. When visual inspection falls short, we harness the power of side channel attacks and image processing techniques to unearth valuable information from the SRAM blocks. In the first part of our study, we investigate the quiescent photon emissions from a PIC microcontroller's SRAM. By deploying a successful correlation attack, we recover the key for the AES algorithm. Furthermore, we apply the SSIM image processing method to discern the content of SRAM cells from the quiescent photon emission images, achieving a prediction accuracy of 97.86%. In the case of the second microcontroller, we investigate the photon emissions from different blocks of an SRAM block within a RISC-V chip for the first time. By employing the SSIM method, we were able to reveal the address of a targeted word, attaining a prediction accuracy of 93%. Our findings underline the efficacy of photon emission side channels for the security analysis of embedded 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".