Enhancing Risk Assessment Models for Heavy Duty and Medium Duty Vehicles through Customization of the EVITA Framework
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
<title>Abstract</title> As a cornerstone of cybersecurity, risk assessment facilitates identifying and grading potential threats. Despite several risk assessment methodologies tailored for the automotive industry, such as EVITA and HEAVEN, a substantial knowledge gap persists in the context of heavy-duty (HD) and medium-duty (MD) vehicles. This study seeks to bridge this gap by introducing a customized model derived from EVITA, specifically designed for HD/MD vehicles. This model enhances the existing EVITA framework by integrating updated severity and probability weights that reflect the unique applications of HD/MD vehicles. Additionally, the attack tree initially presented in EVITA, is augmented by incorporating Common Weakness Enumeration (CWE) and Common Vulnerabilities and Exposures (CVE) standards. This research presents a comprehensive risk assessment methodology for HD/MD vehicles by building upon the EVITA model. The revised severity and likelihood weighting and the integration of CWE and CVE result in a more precise and effective strategy for assessing and mitigating potential risks. The insights gained from this research contribute to the evolution of risk assessment techniques in the automotive industry, with a particular emphasis on heavy-duty and medium-duty vehicles.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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