Reliability and Resilience of AI-Driven Critical Network Infrastructure under Cyber-Physical Threats

2025-10-22

AI Summary

To address the insufficient reliability and resilience of AI-driven 5G/6G critical networks under complex cyber-physical threats, this paper proposes a fault-tolerant framework integrating AI-based anomaly detection, adaptive routing, and multi-layer redundancy. The method jointly models lightweight graph neural network (GNN)-enabled anomaly detection with cross-domain dynamic resource scheduling to enable real-time threat awareness and elastic response in distributed virtualized environments. Experimental evaluation on the NS-3 platform demonstrates that, compared to baseline approaches, the framework reduces failure recovery time by 42.3%, decreases packet loss rate by 37.6%, and improves resilience index by 29.1%. It effectively mitigates cascading failure propagation while ensuring service continuity. The key contribution lies in the first synergistic integration of lightweight GNN-based detection and cross-domain resource orchestration for resilient 5G/6G network operation.

Technology Category

• Planning, Routing, and Scheduling: Plan Execution and Monitoring
• Computer Vision: Adversarial Attacks & Robustness
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

The increasing reliance on AI-driven 5G/6G network infrastructures for mission-critical services highlights the need for reliability and resilience against sophisticated cyber-physical threats. These networks are highly exposed to novel attack surfaces due to their distributed intelligence, virtualized resources, and cross-domain integration. This paper proposes a fault-tolerant and resilience-aware framework that integrates AI-driven anomaly detection, adaptive routing, and redundancy mechanisms to mitigate cascading failures under cyber-physical attack conditions. A comprehensive validation is carried out using NS-3 simulations, where key performance indicators such as reliability, latency, resilience index, and packet loss rate are analyzed under various attack scenarios. The deduced results demonstrate that the proposed framework significantly improves fault recovery, stabilizes packet delivery, and reduces service disruption compared to baseline approaches.

Problem

• Enhancing reliability of AI-driven networks against cyber threats
• Mitigating cascading failures in critical infrastructure systems
• Improving resilience through adaptive routing and anomaly detection

Innovation

• AI-driven anomaly detection for threat identification
• Adaptive routing mechanisms to prevent cascading failures
• Redundancy integration for enhanced fault tolerance