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

Journal of Cybersecurity and Information Management JCIM 2690-6775 2769-7851 10.54216/JCIM https://www.americaspg.com/journals/show/2886 2019 2019 Cyber Security Protection in Roadside Unit Based on Cross-Layer Adaptive Graph Neural Networks (Gnns) in Vanet Department of uGDX, ATLAS SkillTech University, Mumbai, Maharashtra, India Raj Raj Centre of Research Impact and Outcome, Chitkara University, Rajpura- 140417, Punjab, India Sakshi Pandey Department of Electronics and Communication Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bangalore, Karnataka, India Asha KS Associate Professor, Maharishi School of Engineering & Technology, Maharishi University of Information Technology, Uttar Pradesh, India Rakesh Kumar Yadav Chitkara Centre for Research and Development, Chitkara University, Himachal Pradesh-174103 India Abhinav Mishra Assistant Professor, Department of Computer Science & Engineering, Vivekananda Global University, Jaipur, India Sunil Sharma 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 2024 179 196 10.54216/JCIM.140112 https://www.americaspg.com/articleinfo/2/show/2886