Sec-IoV
Bibliographic record
Abstract
Rapid progress in Intelligent Transportation Systems (ITS) during the last few decades has resulted in the emergence of the Internet of Vehicles (IoV), in which smart vehicles communicate with each other for information sharing. The exponential increase in the number of vehicles, together with an increasing data demands from in-vehicle users, has led to a tremendous growth of upstream data in the IoV infrastructure. However, the highly dynamic topology and distributed nature of vehicular networks exposes the vehicular traffic to higher security risks. To address these challenges, in this paper, "Sec-IoV", a multi-stage model for anomaly detection is specifically proposed for securing vehicle-to-vehicle (V2V) communications in IoV setups. The proposed anomaly detection model comprises of multiple stages: (a) relevant feature set selection, (b) optimization of Support Vector Machine (SVM) parameters, and (c) classification of vehicular traffic into benign and anomalous. The first two stages are expressed using the multi-objective optimization problems, which are iteratively computed using the hybridization of a meta-heuristic approach "Artificial Bee Colony (ABC) Optimization" with a Cauchy based mutation operator. This coupling is referred to as "C-ABC". It improves the local search capability of the optimizer with faster convergence. The last stage of data classification is then performed by employing SVM with a refined set of parameters. For the extensive evaluation of the proposed model, different state-of-the-art models have been executed on OMNET++ and SUMO. The obtained results in terms of the detection rate, accuracy, and false positive rate reflect the effectiveness of the proposed Sec-IoV model against the existing state-of-the-art schemes.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.123 | 0.089 |
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".