Misbehavior detection with spatio-temporal graph neural networks

Graph Neural Networks (GNNs) gained the attention of researchers following advancements in Representational Learning. Unlike classical machine learning (ML) methods, its ability to represent more concepts on its data structure gives GNN a clear head start. On the other hand, Misbehavior Detection (M...

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Veröffentlicht in:Computers & electrical engineering 2024-05, Vol.116, p.109198, Article 109198
Hauptverfasser: Yuce, Mehmet Fatih, Erturk, Mehmet Ali, Aydin, Muhammed Ali
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Sprache:eng
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Zusammenfassung:Graph Neural Networks (GNNs) gained the attention of researchers following advancements in Representational Learning. Unlike classical machine learning (ML) methods, its ability to represent more concepts on its data structure gives GNN a clear head start. On the other hand, Misbehavior Detection (MBD) has become a security solution for authorized vehicles to protect against malicious activities on Vehicles to Everything (V2X) Networks. Although authorities standardize MBD, there are not enough MBD datasets for ML, and existing ones are unsuitable for GNNs. In this study, we address this issue by providing an algorithm to convert classical MBD datasets for GNN. Then, a novel GNN model is proposed to detect misbehaving activity on V2X networks. Obtained results show that the proposed GNN model outperforms existing methods with 99.92% accuracy, 99.9196% recall, 31% better runtime efficiency, and 97.35% Matthews correlation coefficient. [Display omitted] •V2X Misbehavior Detection.•Spatio-Temporal Graph Neural Networks (GNN).•A generic method of creating Spatio-Temporal GNN datasets from existing tabular datasets.•A GNN loader to load and process these datasets into existing tools.•A customized GNN model to evaluate and experiment with these datasets.
ISSN:0045-7906
1879-0755
DOI:10.1016/j.compeleceng.2024.109198