A Deep Learning Approach to Detecting Multiple Types of Sybil Nodes in VANETs
Bibliographic record
Abstract
Sybil attacks occur when a single node mimics multiple automobiles and poses a tremendous threat to the authenticity and credibility of VANETs communications. Existing detection methods are not robust against dynamic and cooperative Sybil attacks, calling for stronger defense mechanisms. This paper proposes a deep learning solution for Sybil attack detection in VANETs, where malicious nodes mimic multiple vehicles, threatening communication authenticity. Our approach integrates hierarchical feature engineering and GNNs to identify spatiotemporal correlations. We have extended the VeReMi dataset by adding preset route spoofing and periodic location spoofing attacks. Hierarchical feature engineering divides vehicle behavior into instant kinematics, short-time dynamics, and long-time consistency to address feature fragmentation. A GCN_GRU hybrid model is introduced, combining graph convolutions for spatiotemporal interactions and GRUs for trajectory analysis. Evaluated using SUMO-OMNeT++ simulations under six attack classes and dynamic Oakland traffic, the framework achieves a 99. 91% F1 score, outperforming conventional models by 10% in complex scenarios. This work advances IoV security with a fault-tolerant, real-time Sybil detection solution for safer vehicular communications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".