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

Raj Kumar^1*, Sakshi Pandey², Asha KS³, Rakesh Kumar Yadav⁴, Abhinav Mishra⁵, Sunil Sharma⁶

¹Assistant Professor, Department of uGDX, ATLAS SkillTech University, Mumbai, Maharashtra, India            

 ²Centre of Research Impact and Outcome, Chitkara University, Rajpura- 140417, Punjab, India

³Associate Professor - 1, Department of Electronics and Communication Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bangalore, Karnataka, India      

⁴Associate Professor, Maharishi School of Engineering & Technology, Maharishi University of Information Technology, Uttar Pradesh, India    

⁵Chitkara Centre for Research and Development, Chitkara University, Himachal Pradesh-174103 India

⁶Assistant Professor, Department of Computer Science & Engineering, Vivekananda Global University, Jaipur, India

Emils: raj.kumar@atlasuniversity.edu.in; sakshi.pandey.orp@chitkara.edu.in; ks.asha@jainuniversity.ac.in; rkymuit@gmail.com; abhinav.mishra.orp@chitkara.edu.in; sunil.sharma@vgu.ac.in

Abstract

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.

Keywords: VANET; Roadside units (RSUs); Cyber security; Graph Neural Networks (GNNs); Cross-layer adaptation; Intrusion detection.