@article{Muktar2025, 
author = {Bappa Muktar and Vincent Fono and Adama Nouboukpo},
title = {Machine Learning-Based Detection of DDoS Attacks in VANETs for Emergency Vehicle Communication},
year = {2025},
journal = {Computers, Materials & Continua},
volume = {85},
number = {3},
pages = {4705-4727},
keywords = {VANET, DDoS attacks, emergency vehicles, machine learning, intrusion detection, NS-3, SUMO, traffic classification, supervised learning, artificial neural network},
url = {https://www.sciopen.com/article/10.32604/cmc.2025.067733},
doi = {10.32604/cmc.2025.067733},
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.}
}