Enhancing Security of Over-the-Air Updates in Connected and Autonomous Vehicles using Blockchain: Proof of Concept
Bibliographic record
Abstract
Over-the-Air (OTA) updates provide a convenient and cost-effective way to deliver software updates to vehicles without needing a visit to the dealership in person. However, they also introduce significant cybersecurity risks known to come with wireless connectivity, such as Man-in-the-middle and replay attacks. This paper presents a novel method for enhancing the security of OTA updates in Connected and Autonomous Vehicles (CAVs) using permissioned Blockchain technology with smart contracts for automation. Our proposed solution involves adding a second point of trust to the traditional OTA update architecture, utilizing permissioned Blockchain technology to verify the integrity of the OTA update before the CAV proceeds with the installation. Thereby ensuring the security and reliability of the vehicle’s functionality. This paper outlines the context and motivation for this research, reviews current Blockchain-based secure mechanisms for OTA updates, details the proposed method, and presents the implementation and evaluation results from our proof of concept. The results demonstrate that the proposed Blockchain-based verification system significantly enhances the security of OTA updates against common cyber attacks, with minimal overhead and low impact on system performance. Additionally, the proposed architecture can be easily integrated into existing OTA update infrastructures, making it a cost-effective solution for protecting CAVs against potential cyber threats at a scale.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".