Misbehavior detection with spatio-temporal graph neural networks

Yuce M. F., ERTÜRK M. A., AYDIN M. A.

Computers and Electrical Engineering, vol.116, 2024 (SCI-Expanded, Scopus) identifier identifier

  • Publication Type: Article / Article
  • Volume: 116
  • Publication Date: 2024
  • Doi Number: 10.1016/j.compeleceng.2024.109198
  • Journal Name: Computers and Electrical Engineering
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Keywords: Dataset migration, Geometric leaning, Graph neural networks, Internet of vehicles, Misbehavior detection, Representational learning, Vehicles to everything
  • Istanbul University-Cerrahpasa Affiliated: Yes

Abstract

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.