Machine Learning-Based Detection of DDoS Attacks in VANETs for Emergency Vehicle Communication
Bibliographic record
Abstract
Vehicular Ad Hoc Networks (VANETs) are central to Intelligent Transportation Systems (ITS), especially for real-time communication involving emergency vehicles. Yet, Distributed Denial of Service (DDoS) attacks can disrupt safety-critical channels and undermine reliability. This paper presents a robust, scalable framework for detecting DDoS attacks in highway VANETs. We construct a new dataset with Network Simulator 3 (NS-3) and Simulation of Urban Mobility (SUMO), enriched with real mobility traces from Germany’s A81 highway (OpenStreetMap). Three traffic classes are modeled: DDoS, Voice over IP (VoIP), and Transmission Control Protocol Based (TCP-based) video streaming (VideoTCP). The pipeline includes normalization, feature selection with SHapley Additive exPlanations (SHAP), and class balancing via Synthetic Minority Over-sampling Technique (SMOTE). Eleven classifiers are benchmarked—including eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), Adaptive Boosting (AdaBoost), Gradient Boosting (GB), and an Artificial Neural Network (ANN)—using stratified 5-fold cross-validation. XGBoost, GB, CatBoost and ANN achieve the highest performance (weighted F1-score = 97%). To assess robustness under non-ideal conditions, we introduce an adversarial evaluation with packet-loss and traffic-jitter (small-sample deformation); the top models retain strong performance, supporting real-time applicability. Collectively, these results demonstrate that the proposed highway-focused framework is accurate, resilient, and well-suited for deployment in VANET security for emergency communications.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 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".