Evaluating the Robustness of ADVENT on the VeReMi-Extension Dataset

In this paper, we extend and evaluate the effectiveness of ADVENT (Attack/Anomaly Detection in VANETs), a machine learning-based system designed for early attack detection and malicious node identification in Vehicular Ad Hoc Networks (VANETs). ADVENT combines machine learning with federated learning to detect the onset of attacks while preserving user privacy. The system detects and reports malicious nodes to neighboring vehicles, allowing proactive defense against attacks. We focus on its robustness against various Distributed Denial-of-Service (DDoS) attacks. Using the Vehicular Reference Misbehavior Extension (VeReMi-Extension) dataset, we assess ADVENT across five distinct types of (D)DoS attacks, each representing diverse attack characteristics. Based on our findings, we enhance ADVENT by refining its malicious node detection step through a time slicing mechanism, improving both False Positive Rate (FPR) and F1-score metrics. Our evaluation shows that ADVENT consistently excels in detecting attack onsets and identifying attackers, even under different attack types. The results emphasize its adaptability and effectiveness in strengthening VANET security.