Independent Boot Process Verification using Side-Channel Power Analysis
Bibliographic record
Abstract
Firmware attacks on embedded systems can have disastrous security implications. Through the firmware update mechanism, an attacker can tamper with the firmware to open known vulnerabilities, change security settings, or deploy custom backdoors, to pave the way for subsequent attacks or gain complete machine control. Firmware protection solutions often share the flaw of requiring the cooperation of the machine they aim to protect. If the machine gets compromised, the results from the protection mechanism become untrustworthy. One solution to this problem is to leverage an independent source of information to assess the integrity of the firmware and the boot-up sequence. In this paper, we propose a physics-based Intrusion Detection System called the Boot Process Verifier that only relies on side-channel power consumption measurement to verify the integrity of the boot-up sequence. The BPV works in complete independence from the machine to protect and requires only a few nominal training samples to establish a baseline of nominal behaviour. The range of application of this approach potentially extends to any embedded systems. We present three test cases that illustrate the performances of the BPV on micro-PC, network equipment (switches and wireless access points), and a drone.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".