Cyber-Security Risk Assessment Framework for Blockchains in Smart Mobility
Bibliographic record
Abstract
Use of distributed ledger technologies like blockchain is becoming more common in transportation/mobility ecosystems. However, cyber-security failures may occur at places where the blockchain system connects with the real world. In this paper, we propose a novel risk assessment framework for blockchain applications in smart mobility. We aim at systematically quantifying the risk by presenting ordinal values because although vulnerabilities exist in a system, it's the probability that they can be exploited and the impact of this exploitation that determine if in fact, the vulnerability corresponds to a significant risk. As a case study, we carry out an analysis in terms of quantifying the risk associated to a multi-layered Blockchain framework for Smart Mobility Data-markets (BSMD). We first construct an actor-based analysis to determine the impact of the attacks. Then, a scenario-based analysis determines the probability of occurrence of each threat. Finally, a combined analysis is developed to determine which attack outcomes have the highest risk. In the case study of the public permissioned BSMD, the outcomes of the risk analysis highlight the highest risk factors according to their impact on the victims in terms of monetary, privacy, integrity and trust. The analysis uncovers specific blockchain technology security vulnerabilities in the transportation ecosystem by exposing new attack vectors.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".