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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".