Secure Vehicular Ad-Hoc Networks (VANETs) using Machine Learning Approaches

Abstract

Vehicular Ad-Hoc Networks (VANETs) are pivotal in enabling intelligent transportation systems, facilitating vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. However, VANETs face significant security challenges, including data integrity threats, spoofing, denial-of-service (DoS) attacks, and privacy concerns. Traditional security mechanisms are insufficient to tackle the dynamic and decentralized nature of VANETs, necessitating adaptive, real-time threat detection and mitigation strategies. This study explores machine learning (ML) approaches to enhance security in VANETs by detecting anomalies and malicious behaviors effectively.

We present a comprehensive framework employing supervised and unsupervised ML algorithms, such as Support Vector Machines (SVM), Random Forests, and clustering methods, for intrusion detection and misbehavior identification in VANET communication data. Feature extraction is performed on network traffic metrics, vehicle mobility patterns, and message payloads to characterize normal and malicious activities.

Experimental evaluation using publicly available VANET datasets and simulated network environments demonstrates that ML models can achieve detection accuracies exceeding 95%, with low false positive rates. The use of ensemble learning techniques further improves robustness against evolving attack patterns. Additionally, the framework supports real-time processing using lightweight algorithms suitable for resource-constrained vehicular nodes.

The study highlights the potential of ML-based solutions to provide adaptive, scalable, and efficient security for VANETs. Challenges related to data heterogeneity, feature selection, and model deployment in decentralized environments are discussed. Future work aims to incorporate deep learning models and federated learning to enhance detection capabilities while preserving privacy.

This research advances secure VANET communications by integrating intelligent, data-driven methods that can proactively safeguard vehicular networks against emerging cyber threats, promoting safer and more reliable intelligent transportation systems.