Cyber Security Protection in Roadside Unit Based on Cross-Layer Adaptive Graph Neural Networks (Gnns) in Vanet

aspg-2-2886-1791417082 20261007235122 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Cybersecurity and Information Management JCIM 2769-7851 2690-6775 2024 14 1 Cyber Security Protection in Roadside Unit Based on Cross-Layer Adaptive Graph Neural Networks (Gnns) in Vanet Raj Kumar Department of uGDX, ATLAS SkillTech University, Mumbai, Maharashtra, India Sakshi Pandey Centre of Research Impact and Outcome, Chitkara University, Rajpura- 140417, Punjab, India Asha KS Department of Electronics and Communication Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bangalore, Karnataka, India Rakesh Kumar Yadav Associate Professor, Maharishi School of Engineering & Technology, Maharishi University of Information Technology, Uttar Pradesh, India Abhinav Mishra Chitkara Centre for Research and Development, Chitkara University, Himachal Pradesh-174103 India Sunil Sharma Assistant Professor, Department of Computer Science & Engineering, Vivekananda Global University, Jaipur, India 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. 2024 179 196 2886 https://creativecommons.org/licenses/by/4.0/ 10.54216/JCIM.140112 https://www.americaspg.com/journal/2/article/2886 https://www.americaspg.com/storage/51718444956.pdf