Signal-Layer Security and Trust-Boundary Identification based on Hardware-Software Interface Definition
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
An important trend in the automotive domain is to adapt established functional safety processes and methods for security engineering. Although functional safety and cyber-security engineering have a considerable overlap, the trend of adapting methods from one domain to the other is often challenged by non-domain experts. Just as safety became a critical part of the development in the late 20th century, modern vehicles are now required to become resilient against cyber-attacks. As vehicle providers gear up for this challenge, they can capitalize on experiences from many other domains, but must also face several unique challenges. Such as, that cyber-security engineering will now join reliability and safety as a cornerstone for success in the automotive industry and approaches need to be integrated into the mainly safety oriented development lifecycle of the domain. The recently released SAE J3061 guidebook for cyber-physical vehicle systems focus on designing cyber-security aware systems in close relation to the automotive safety standard ISO 26262. The key contribution of this paper is to analyse a method to identify attack vectors on complex automotive systems via signal interfaces and propose a security classification scheme and protection mechanisms on signal layer. To that aim, the hardware-software interface (HSI), a central development artefact of the ISO 26262 functional safety development process, is used and extended to support the cyber-security engineering process and provide cyber-security countermeasures on signal layer.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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