ADVENT: Attack/Anomaly Detection in VANETs
This addresses the critical need for secure and efficient malicious detection in VANETs, though it appears incremental by integrating existing techniques.
The study tackled the problem of real-time attack and anomaly detection in Vehicular Ad hoc Networks (VANETs) by developing a system that detects attack onsets with an F1-score of 99.66% and identifies malicious vehicles with an average F1-score of 97.85%.
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.