Collaborative Security in Vehicular Cloud Computing: A Game Theoretic View
Bibliographic record
Abstract
Connected vehicular cloud computing (CVCC) is a promising paradigm that utilizes the rich resources of connected cars. However, it also introduces new cyber-attack surfaces that may compromise the security or privacy of the vehicles. In reality, security in vehicular cloud computing lies in the willingness of vehicle owners who are concerned with their vehicles' protection from various threats. Increasing the participation of vehicles will improve the security in vehicular cloud computing as a whole. Therefore, for a CVCC service provider, it is very critical to encourage vehicle owners to invest in their own security for achieving deeper security of CVCC systems. In this article, we first present a CVCC architecture and its applications. Then we study several security issues in vehicular cloud computing. Afterward, we model a CVCC network by a two-phase heterogeneous public good game, and then investigate the influence of different incentive mechanisms and the structure of a complex network describing the vehicles' connectivity on the vehicles' investment rate. Finally, we present our conclusion.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".