A Digital Twin Platform for QoS Optimization Under DoS Attacks for Next Generation Radio Networks

Abstract

Digital Twins are being used as an enabling technology in 6G applications across various domains, valued for their data-driven insights and real-time decision-making capabilities. However, integrating Digital Twins into 6G environments presents challenges in maintaining consistent network services under adverse conditions such as including denial-of-service (DoS) attacks, while ensuring consistent Quality of Service (QoS). In this work, we present a Digital Twin Platform to facilitate bidirectional communication between User Equipment (UEs) and application-specific digital twins to enhance UE traffic under UDP flood attacks. By leveraging AI to analyze key digital twin parameters such as throughput and delay, our framework derives actionable insights that enhance QoS management in DoS attack scenarios, ultimately advancing real-world applications of digital twins in critical infrastructure domains. The performance of this Digital Twin Platform is validated through an emergency management use-case in 6G networks while the network is under attack with UDP flood attacks in terms of packet reception success rate, average packet delay, and average throughput metrics.