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

2025-11-23

AI Summary

To address the challenge of maintaining Quality of Service (QoS) in 6G networks under Denial-of-Service (DoS) attacks, this paper proposes an AI-driven digital twin architecture enabling end-to-end, bidirectional coupling between physical and virtual network layers. The architecture integrates real-time network monitoring, lightweight AI analytics models, and dynamic QoS control mechanisms, achieving closed-loop optimization based on key performance indicators—including throughput, latency, and packet reception rate. Innovatively, digital twin technology is deeply embedded into 6G resilience frameworks to enable adaptive traffic scheduling and runtime resource reconfiguration under UDP flood attacks. Evaluated in an emergency communications use case, the solution improves packet reception success rate by 23.6%, reduces average latency by 41.2%, and significantly enhances throughput stability—demonstrating robust support for high-reliability digital twin applications in critical infrastructure.

Technology Category

• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Planning, Routing, and Scheduling: Plan Execution and Monitoring
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Optimizing Quality of Service during DoS attacks in 6G networks
• Enhancing network performance under UDP flood attack conditions
• Improving bidirectional communication between User Equipment and digital twins

Innovation

• Digital Twin Platform enables bidirectional communication for QoS
• AI analyzes throughput and delay to enhance QoS
• Framework validated under UDP flood attacks in 6G