Position Verification in Connected Vehicles for Cyber Resilience Using Geofencing and Fuzzy Logic
Bibliographic record
Abstract
Position verification is essential in connected and autonomous vehicle technology to enable secure vehicle-to-everything communication. Previous attempts to verify location information have used specific hardware, traffic parameters, and statistical model-based techniques dependent on neighbouring vehicles and roadside infrastructure and whose judgements can be influenced by untrustworthy entities. Considering the back-and-forth communications during verification, these techniques are also unsuitable in the dynamic vehicular networking environment. In this context, this paper proposes a self-reliant trustbased position verification technique using dynamic geofencing, neural network, and Mamdani fuzzy logic controller. The method uses vehicular dynamics, such as distance between the sender and receiver vehicles, magnitude of the speed difference, and direction, to verify the trustworthiness of vehicle positions. An experimental analysis of a dataset of simulated driving scenarios in MATLAB demonstrates that the feedforward neural network records the highest direction classification performance at 99.8% in conjunction with the centroid defuzzification method. Subsequently, further quantitative analysis, including the Receiver Operating Characteristic curve with Area Under Curve and trust level distribution histograms, indicates that the suggested classification model outperforms a random classifier and effectively identifies false position data from the actual during trust computation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".