ADVENT: Attack/Anomaly Detection in VANETs

Authors: Hamideh Baharlouei, Adetokunbo Makanju, Nur Zincir-Heywood
Published: 2024-01-16

Source: https://arxiv.org/abs/2401.08564

PDF: https://arxiv.org/pdf/2401.08564

AIにより推定されたラベル

連合学習 悪意のあるノード検出 データセット生成

※ こちらのラベルはAIによって自動的に追加されました。そのため、正確でないことがあります。
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Abstract

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 F1-score of approximately 97.85 stages enhances privacy and improves the efficiency of malicious node detection, effectively reducing the false negative rate.