Enhancing Risk Assessment Models for Heavy Duty and Medium Duty Vehicles through Customization of the EVITA Framework
Bibliographic record
Abstract
Abstract 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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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 | 0.003 | 0.006 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".