Non-intrusive program tracing and debugging of deployed embedded systems through side-channel analysis
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
One of the hardest aspects of embedded software development is that of debugging, especially when faulty behavior is observed at the production or deployment stage. Non-intrusive observation of the system's behavior is often insufficient to infer the cause of the problem and identify and fix the bug. In this work, we present a novel approach for non-intrusive program tracing aimed at assisting developers in the task of debugging embedded systems at deployment or production stage, where standard debugging tools are usually no longer available. The technique is rooted in cryptography, in particular the area of side-channel attacks. Our proposed technique expands the scope of these cryptographic techniques so that we recover the sequence of operations from power consumption observations (power traces). To this end, we use digital signal processing techniques (in particular, spectral analysis) combined with pattern recognition techniques to determine blocks of source code being executed given the observed power trace. One of the important highlights of our contribution is the fact that the system works on a standard PC, capturing the power traces through the recording input of the sound card. Experimental results are presented and confirm that the approach is viable.
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 | 0.001 |
| 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) | 0.000 | 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