ADVENT: Attack/Anomaly Detection in VANETs
Hamideh Baharlouei Adetokunbo Makanju Nur Zincir-Heywood
In the domain of Vehicular Ad hoc Networks (VANETs), where the imperative of
having a real-world malicious detector capable of detecting attacks in
real-time and unveiling their perpetrators is crucial, our study introduces a
system with this goal. This system is designed for real-time detection of
malicious behavior, addressing the critical need to first identify the onset of
attacks and subsequently the responsible actors. Prior work in this area have
never addressed both requirements, which we believe are necessary for real
world deployment, simultaneously. By seamlessly integrating statistical and
machine learning techniques, the proposed system prioritizes simplicity and
efficiency. It excels in swiftly detecting attack onsets with a remarkable
F1-score of 99.66%, subsequently identifying malicious vehicles with an average
F1-score of approximately 97.85%. Incorporating federated learning in both
stages enhances privacy and improves the efficiency of malicious node
detection, effectively reducing the false negative rate.