Mitigating the Effects of Position-Based Routing Attacks in Vehicular Ad Hoc Networks
Bibliographic record
Abstract
In this paper, we investigate the effects of routing loop, sinkhole, and wormhole attacks on the position-based routing (PBR) in vehicular ad hoc networks (VANETs). We also introduce a new attack termed wormhole-aided sybil attack on PBR. Our study shows that the wormhole-aided sybil attack has the worst impact on the packet delivery in PBR. To ensure the reliability of PBR in VANETs, we propose a set of plausibility checks that can mitigate the impact of these PBR attacks. The proposed plausibility checks do not require adding extra hardware to the vehicles. In addition, they can adapt to different road characteristics and traffic conditions. Simulation results are given to demonstrate that the proposed plausibility checks are able to efficiently mitigate the impact of the previously mentioned PBR attacks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".