A Novel Mechanism for Misbehavior Detection in Vehicular Networks
Bibliographic record
Abstract
Intelligent Transport Systems (ITS) have provided new technologies to protect human life, speed up assistance, and improve traffic, to aid drivers, passengers, and pedestrians. Vehicular Ad-hoc Networks (VANET) are the fundamental elements in an ITS ecosystem. However, its characteristics make the system susceptible to numerous attacks, such as Denial of Service (DoS). In this paper, we proposed a security system based on intrusion detection called Detection of Anomalous Behaviour in Smart Conveyance Operations (DAMASCO). We used a statistical approach to detect anomalies in vehicle-to-vehicle communication (V2V). The anomaly detection module addresses the Medium Access Control (MAC) sublayer to assess the number of packages sent to identify potentially malicious nodes, block their activity, and maintain a reputation list. The algorithm calculates the Median Absolute Deviation (MAD) to identify outliers and characteristics of DoS. Our experiments were performed in a simulated environment using a realistic urban mobility model. The results demonstrate that the proposed security system achieved a 3% false positive rate and no false negatives.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".