Volume 14 , Issue 1 , PP: 179-196, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Raj Kumar 1 * , Sakshi Pandey 2 , Asha KS 3 , Rakesh Kumar Yadav 4 , Abhinav Mishra 5 , Sunil Sharma 6
Doi: https://doi.org/10.54216/JCIM.140112
The proposed systems can improve cyber security in VANET applications by enabling efficient detection of complex attacks on the RSU component. The subsequent sections discuss the systems that are applied and support the suggestions for improving the VANET trustworthiness. VANETs and show that the utilization of Cross-Layer Adaptive GNNs can improve cyber security and LEARNING in VANET-based RSUs. As a result, the suggested system can provide robust ways for detecting cyber-attacks by: modeling the network architecture using graphs while combining information regarding several protocol layers to detect complicated interactions between the network entities and find the abnormal activities. the nature of the GNN enables it to update in real-time by adapting to the evolving attack patterns and the shifting network conditions, making them sturdy and flexible defense ways for cyber security. The proposed network e systems can efficiently detect multiple cyber threats and focus on reducing the number of false positives while maintaining low computation costs. Therefore, chances are that incorporating the Cross-layer adaptive GNNs into the RSUs can improve cyber security in VANETs, enhancing the robustness and reliability of prospective smart transportation systems.
VANET , Roadside units (RSUs) , Cyber security , Graph Neural Networks (GNNs) , Cross-layer adaptation , Intrusion detection.
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