Enhanced TARA model for heavy-duty vehicles using ISO/SAE 21434 and Fuzzy Analytic Hierarchy Process (FAHP)
Bibliographic record
Abstract
In recent years, the automotive industry has increasingly recognized the importance of Threat Analysis and Risk Assessment (TARA) as a critical first step in cybersecurity planning. While standards such as ISO/SAE 21,434 provide a structured framework, they are primarily designed for light-duty vehicles and do not fully address the unique characteristics, operational environments, and vulnerabilities of heavy-duty (HD) commercial vehicles—despite their crucial role in public transportation, freight systems, and the supply chain. This study addresses that gap by customizing the ISO/SAE 21,434 risk assessment model specifically for HD vehicles. It employs a Multi-Criteria Decision-Making (MCDM) methodology to assign weights to the severity and feasibility criteria using expert input through the Fuzzy Analytic Hierarchy Process (FAHP). Expert-driven pairwise comparisons were conducted to evaluate the relative importance of each criterion separately for severity and feasibility assessment. The resulting model provides a tailored and robust approach for analyzing and assessing cybersecurity threats in HD vehicles, reflecting their distinct needs and risk profiles.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".