On the Context-Aware Anomaly Detection in Vehicular Networks

Transportation systems are moving towards autonomous and intelligent vehicles due to advancements in embedded systems, control algorithms, and wireless communications. By enabling connectivity among vehicles, a vehicular network can be developed which offers safe and efficient driving applications. Security is a major challenge for vehicular networks as application reliability depends on it. In this paper, we highlight the security challenges faced by a vehicular network especially related to jamming and data integrity attacks. Such attacks cause major disruptions in the wireless connectivity of users with the centralized servers. We propose a context-aware anomaly detection technique for vehicular networks that considers factors such as signal strength, mobility, and data pattern to find abnormal behaviors and malicious users. We further discuss how an intelligent learning system can be developed using efficient anomaly detection. We implement a vehicular network scenario with malicious users and provide simulation results to highlight the performance gain of the proposed technique. We also highlight several appropriate future opportunities related to the security of vehicular network applications.

@article{Aljaafari2024,
  title     = {On the Context-Aware Anomaly Detection in Vehicular Networks},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {12},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Mohammed Abdullatif H. Aljaafari},
  doi       = {10.14569/IJACSA.2024.0151284},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151284}
}