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

TL;DR

This paper presents a novel AI-driven framework to enhance the reliability and resilience of critical network infrastructures like 5G/6G against cyber-physical threats, using anomaly detection, adaptive routing, and redundancy.

Contribution

It introduces a comprehensive, resilience-aware framework combining AI techniques and fault-tolerance mechanisms specifically designed for cyber-physical attack scenarios in critical networks.

Findings

Significant improvement in fault recovery and service stability.

Reduction in packet loss and latency under attack conditions.

Enhanced resilience index compared to baseline methods.

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.