System Engineering-Based Threat Modeling for Heavy-Duty Vehicles
Bibliographic record
Abstract
With rapid advancements in connected vehicle technologies, modern vehicles have become increasingly vulnerable to cyber-attacks. While concerns about automotive cybersecurity are growing, research specifically addressing the cybersecurity of heavy-duty vehicles, such as freight trucks, remains limited. Given that disruptions in the operation of these vehicles can have significant impacts on the economy, supply chains, and transportation systems, it is urgent to explore the cybersecurity of such vehicles. This study addresses this gap by performing a cybersecurity threat analysis of a freight truck. As highlighted in ISO/SAE 21434, threat analysis and risk assessment are fundamental steps in implementing cybersecurity. This study focuses on the interfaces of heavy-duty vehicles, using the STRIDE framework. The process begins by identifying critical assets and analyzing potential attack vectors. Following a systems engineering approach, the truck's system is decomposed into subsystems to identify potential cybersecurity vulnerabilities. After constructing a data flow diagram based on the system engineering results, the STRIDE framework is then applied using the Microsoft Threat Modeling Tool to systematically identify and classify possible attack scenarios. The study also outlines the features of extracted threats related to heavy-duty vehicle interfaces. This work presents a structured approach to threat modeling, focusing on truck interfaces, and contributes to a deeper understanding of cybersecurity risks in connected heavyduty 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".