Securing Smart Vehicles: A Bilateral TARA Approach for ISO 21434 and ASPICE for CS Compliance
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
The increasing connectivity in modern vehicles has opened the door to a surge of cyberattacks, posing significant risks to vehicle safety and potentially leading to substantial financial losses.To address this, our paper introduces an optimized approach to risk assessment and threat analysis (TARA), specifically tailored for secure smart vehicles.We've developed a bilateral model that meticulously adheres to the ISO/SAE 21434 automotive cybersecurity standard and the Automotive SPICE for Cybersecurity (ASPICE for CS) base practices.This unique bilateral model considers both standards simultaneously for each model side, incorporating new requirements to ensure practitioners can achieve full compliance.We've also simplified the categorization of requirements, making them more intuitive and industry-driven.Throughout this paper, we detail the analysis, mapping, and development steps of our proposed model.Our observations and lessons learned from applying this model across various projects have significantly improved its maturity, directly reducing the risks associated with recovery costs and financial losses.To further improve the practical application of this model, we have developed a capable cybersecurity TARA tool based on the model to achieve compliance with both standards.This tool could be used by automotive manufacturers and suppliers during the entire vehicle development lifecycle, from initial design to postproduction updates to help identify potential vulnerabilities in electronic control units (ECUs), communication networks, and software, allowing for proactive risk mitigation.The documented and analyzed results from utilizing our model and tool show a remarkable 40% to 60% decrease in operational costs due to the significant reduction in quality and compliance efforts.
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 | 0.000 | 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 | 0.000 | 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 it