Secure and Privacy Preserving 5G Network for Connected Vehicle
Coastal Virginia Node
Sachin Shetty, associate director, Virginia Modeling, Analysis, and Simulation Center, Old Dominion University
Cong Shen, assistant professor, electrical and computer engineering, University of Virginia; Duminda Wijesekera, professor, computer science, George Mason University; Kai Zeng, associate professor, electrical and computer engineering, GMU; Tan Le, research assistant professor, artificial intelligence, ODU; Jeffrey Reed, CCI's chief technology officer and the Willis G. Worcester professor of electrical and computer engineering, Virginia Tech
The integration of 5G and IoT technologies will result in connected vehicular networks that can profoundly change logistics management in the maritime and transportation industry. However, deployment of 5G empowered vehicular networks will also introduce attack surfaces targeting 5G-IoT ecosystem. This proposal aims to develop a secure and privacy-preserving 5G network capability in connected vehicular networks that can provide trusted information to maritime and transportation domains while protecting networked systems from being abused by malicious actors. Although there are several research efforts in 5G security, several research challenges that need to address to realize an integrated secure and privacy-preserving 5G-enabled vehicular network. In this proposal, we address specific challenges in securing 5G-enabled vehicular networks due to dynamic mobility, unknown and changing deployment environment, open wireless medium and limitations of centralized security infrastructure. Our specific differentiators include: (a) To maintain the effectiveness in changing deployment environment, federated learning can be automatically and continuously updated to fit the needs of specific deployment with no additional cost; (b) We provide an integrated security and privacy enhancing capability that will ensure maximum participation from end users; (c) Our Blockchain assisted distributed trust management employs advanced artificial intelligence approaches for data- and entity-centric trust models to ensure security of messages and vehicle trust; (d) Federated learning is integrated in our delay-aware CAN bus intrusion detection to build a robust and resilient distributed detection; (e) Finally, our solutions are deployed in resource constrained devices, such as, software defined radios, that will reduce cost while maintaining security effectiveness.