Machine Learning-Based Detection of DDoS Attacks in VANETs for Emergency Vehicle Communication

TL;DR

This paper presents a machine learning framework that effectively detects DDoS attacks in VANETs, enhancing the security and reliability of emergency vehicle communications in real-time scenarios.

Contribution

It introduces a scalable detection framework using synthetic and real-world data, benchmarking multiple classifiers, and identifying XGBoost and CatBoost as top performers.

Findings

XGBoost and CatBoost achieved 96% F1-score.

The framework is robust and suitable for real-time deployment.

Synthetic and real-world data integration improves detection accuracy.

Abstract

Vehicular Ad Hoc Networks (VANETs) play a key role in Intelligent Transportation Systems (ITS), particularly in enabling real-time communication for emergency vehicles. However, Distributed Denial of Service (DDoS) attacks, which interfere with safety-critical communication channels, can severely impair their reliability. This study introduces a robust and scalable framework to detect DDoS attacks in highway-based VANET environments. A synthetic dataset was constructed using Network Simulator 3 (NS-3) in conjunction with the Simulation of Urban Mobility (SUMO) and further enriched with real-world mobility traces from Germany's A81 highway, extracted via OpenStreetMap (OSM). Three traffic categories were simulated: DDoS, VoIP, and TCP-based video streaming (VideoTCP). The data preprocessing pipeline included normalization, signal-to-noise ratio (SNR) feature engineering, missing value imputation, and class balancing using the Synthetic Minority Over-sampling Technique (SMOTE). Feature importance was assessed using SHapley Additive exPlanations (SHAP). Eleven classifiers were benchmarked, among them XGBoost (XGB), CatBoost (CB), AdaBoost (AB), GradientBoosting (GB), and an Artificial Neural Network (ANN). XGB and CB achieved the best performance, each attaining an F1-score of 96%. These results highlight the robustness of the proposed framework and its potential for real-time deployment in VANETs to secure critical emergency communications.