Machine Learning Approach for Detecting Location Spoofing in VANET
Bibliographic record
Abstract
A vehicular ad-hoc network (VANET) consists of moving and stationary vehicles, along with supporting infrastructure, which communicate with each other through a wireless medium. VANETs are an essential component of an Intelligent Transportation System, which aims to reduce road accidents and traffic congestion and provide additional services for drivers in future smart cities. VANET communication is vulnerable to various attacks and cryptographic techniques are used for message integrity and authentication of vehicles in order to ensure security and privacy for vehicular communications. Such approaches have been shown to be effective for outside attacks, where attackers do not have the credentials to participate in the network. However, if there is an inside attacker additional measures are necessary to ensure the correctness of the transmitted data. Position falsification is an attack where the attacker broadcasts a false position, which can lead to increased traffic congestion or even accidents. Therefore, it is imperative to detect such attacks quickly to ensure safety of all participants in the network. Several trust-based models have been proposed for this in the past. This paper proposes a novel and efficient data-centric approach to detect location spoofing, using machine learning algorithms. We have compared our proposed approach with several existing techniques using the VeReMi dataset and shown that its results improved performance in terms of detection accuracy and other key metrics.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".