Cyber Threat Susceptibility Assessment for Heavy-Duty Vehicles Based on ISO/SAE 21434
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
TARA, which stands for Threat Analysis and Risk Assessment, serves as the foundational stage of cybersecurity implementation, particularly in the context of vehicular systems. While various considerations and risk assessment frameworks have been discussed in recent years, there is a notable lack of TARA models specifically designed for heavy-duty (HD) vehicles. The security considerations and vulnerabilities in HD vehicles differ significantly from those in light-duty (LD) vehicles, leading to different security impacts and varying attack feasibility. This makes existing models inadequate for accurately assessing risks in the context of HD vehicles. This study introduces a novel risk assessment model tailored for HD vehicles, addressing gaps in existing TARA frameworks such as EVITA, HEAVENS, and ISO/SAE 21434. The key contribution of this work lies in the customization of impact and feasibility metrics within the ISO/SAE framework to better account for the unique security challenges posed by HD vehicles. Unlike prior models, this approach adapts the impact criteria to reflect the diverse range of security concerns specific to HD vehicles, which have been inadequately addressed in existing frameworks. Additionally, through a comprehensive analysis of threat vectors and vehicle interfaces, the model refines feasibility criteria, ensuring a more accurate and context-aware assessment of security risks. By adopting these enhancements, the proposed model offers more precise risk assessments that align with HD vehicle considerations, helping to prioritize threats and make optimal decisions regarding risk treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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