A VeReMi-based Dataset for Predicting the Effect of Attacks in VANETs
Bibliographic record
Abstract
Vehicular Ad Hoc Networks (VANETs) have received considerable attention because of their potential to improve road safety. However, reactive security approaches in VANETs are of concern; thus, proactive security is needed to prevent cyberattacks. The current VANET datasets are limited in their ability to evaluate proactive security approaches, limiting research in this area. This paper presents a VeReMi-based dataset named VeReMi for Attack Prediction (VeReMiAP). Developed from the Framework For Misbehavior Detection (F2MD). VeReMiAP incorporates three key elements: Cooperative Awareness Messages (CAMs), a new class of attacks known as Fake Reporting Attacks, and an evaluation of the impact of this attack, which in this case manifests as a road hazard. The ripple effect of this attack goes beyond the targeted vehicle, making it a threat to the overall security and reliability of VANETs. The VeReMiAP dataset evaluates cyberattack prediction techniques and generates countermeasure solutions for VANET attacks. To test the dataset, we conducted a temporal analysis to observe the effect of the attack on velocity and a geospatial analysis to enhance our understanding of the spatial distribution of hazards within the road network. Results show that VeReMiAP is a potential tool to advance security research in VANETs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".