Static Transformation of Power Consumption for Software Attestation
Bibliographic record
Abstract
Software attestation seeks to verify the authenticity of a system without the aid of trusted hardware, and has important applications in the field of security. Such attestation schemes are of particular interest in the embedded domain, where simplicity and limited resources constrain more complex security solutions. At the same time, these properties enable attestation approaches that rely on predictable side-effects. Most software attestation schemes aim to verify the integrity of memory using a combination of cryptographic schemes, internal side-effects like TLB misses, and known timing constraints. However, little attention has been paid to leveraging non-traditional side-effects, in particular, externally observable side-effects such as power consumption. In this paper we introduce a method for software attestation using power consumption as the side-effect. We show how to circumvent the undecidable nature of program execution for this purpose and present a static compiler transformation which implements the technique. Our approach is less intrusive than traditional software attestation because the system does not require interruption to compute a cryptographic checksum. It is particularly well suited to real-time systems where consistent timing is more important than speed.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".