A Comparative Survey of Blockchain-Based Security Mechanisms for OTA updates in CAVs
Bibliographic record
Abstract
In Connected and Autonomous Vehicles (CAVs), there is an increasing demand and reliance on connected vehicle software systems. To keep these software systems up-to-date and secure from evolving threats, the OEM Manufacturers rely on the Over-the-Air (OTA) Updates. In this paper, we explore security challenges posed by remote OTA updates, emphasizing the need for robust OTA update mechanisms that preserve the integrity of the update and emerging requirements to adhere to global automotive cybersecurity regulations and standards. We provide a comparative survey of Blockchain-based security mechanisms for enhancing the security of OTA updates for CAVs by highlighting the existing approaches that use blockchain technology to create secure architectures for OTA updates compared to traditional methods. We also identified critical security challenges associated with OTA updates and outlined potential avenues for future research, guiding advancements in blockchain applications in CAVs.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".