World State Attack to Blockchain Based IoV and Efficient Protection With Hybrid RSUs Architecture
Bibliographic record
Abstract
Blockchain technology is developing rapidly and has been widely applied in the field of Internet of Vehicles (IoV) to solve trust and security problems. However, due to the high security requirements in IoV scenarios, the security threats of blockchain itself become a big challenge for its applications in IoV. As the largest distributed platform supporting smart contract, Ethereum becomes one of the popular blockchain platforms that has been applied in IoV applications. In Ethereum, the local world state (stored on Road Side Units (RSUs) in IoV) is applied to facilitate account query and transaction verification. However, previous works showed that the local database can be easily tampered, so attackers may issue invalid transactions based on the modified world state, which is not acceptable for IoV applications. In this paper, the success probability and expected time for such an attack are first analyzed theoretically, including the effect of portion of tampered RSUs and the number of required confirmation blocks. Then experiment evaluation verifies the correctness of the theoretical analysis and shows that the attack would succeed with a higher probability within a shorter time when the local database on more RSUs are attacked. On the contrary, increasing of confirmation blocks can effectively reduce the success probability of a single attack and extend the confirmation time of the invalid transaction. Finally, efficient attack detection and recovery methods are proposed based on a novel hierarchical architecture with hybrid RSUs, and the effectiveness and complexity are verified by theoretical analysis and experiments.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".