Evaluating the Robustness of ADVENT on the VeReMi-Extension Dataset
Bibliographic record
Abstract
In this paper, we extend and evaluate the effectiveness of ADVENT (Attack/Anomaly Detection in VANETs), a machine learning-based system designed for early attack detection and malicious node identification in Vehicular Ad Hoc Networks (VANETs). ADVENT combines machine learning with federated learning to detect the onset of attacks while preserving user privacy. The system detects and reports malicious nodes to neighboring vehicles, allowing proactive defense against attacks. We focus on its robustness against various Distributed Denial-of-Service (DDoS) attacks. Using the Vehicular Reference Misbehavior Extension (VeReMi-Extension) dataset, we assess ADVENT across five distinct types of (D)DoS attacks, each representing diverse attack characteristics. Based on our findings, we enhance ADVENT by refining its malicious node detection step through a time slicing mechanism, improving both False Positive Rate (FPR) and F1-score metrics. Our evaluation shows that ADVENT consistently excels in detecting attack onsets and identifying attackers, even under different attack types. The results emphasize its adaptability and effectiveness in strengthening VANET security.
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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 | 0.001 | 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 | 0.001 | 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".