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.

翻译：随着关键任务服务日益依赖人工智能驱动的5G/6G网络基础设施，其面对复杂信息物理威胁的可靠性与韧性需求愈发凸显。由于分布式智能、虚拟化资源及跨域融合等特性，此类网络极易遭受新型攻击面的威胁。本文提出一种容错与韧性感知框架，该框架融合了人工智能驱动的异常检测、自适应路由与冗余机制，以缓解信息物理攻击条件下的级联故障。研究采用NS-3仿真平台进行综合验证，在不同攻击场景下分析可靠性、时延、韧性指数及丢包率等关键性能指标。推导结果表明，相较于基准方法，所提框架能显著提升故障恢复能力、稳定数据包传输并减少服务中断。