Metis: An Integrated Morphing Engine CPU to Protect Against Side Channel Attacks
Bibliographic record
Abstract
Power consumption and electromagnetic emissions analyses are well established attack avenues for secret values extraction in a large range of embedded devices. Countermeasures against these attacks are approached at different levels, from modified logic styles, to changes in the software implementations. In this work, we propose a microarchitectural modification to a compact RISC-V SoC, the OpenTitan open source silicon root of trust, providing a code morphing countermeasure against power and electromagnetic emissions side channel attacks. Our approach allows the countermeasure to be applied transparently, without the need for any software modification to the cryptographic primitive running on OpenTitan. Our microarchitecture integration of a morphing engine also allows us to provide transparent protection to memory operations. We validate our approach through measurements on an actual FPGA prototype on a Xilinx Artix-7. Our integrated morphing engine increases the FPGA resource consumption by less than 8%, plus the resources required by an RNG of choice, with respect to the original OpenTitan SoC. Our design shows a side channel attack resistance improvement of at least 250× in the Measurements-To-Disclose metric with respect to the unprotected design. We benchmark the performance of our proposed architecture on all the ISO/IEC standard symmetric block ciphers, including, among the other AES, reducing the execution time overhead by 21× to 141× with respect to a continuously morphing software solution.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".