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

• URL: http://arxiv.org/abs/2510.19295v1
• Date: Wed, 22 Oct 2025 06:56:44 GMT
• Title: Reliability and Resilience of AI-Driven Critical Network Infrastructure under Cyber-Physical Threats
• Authors: Konstantinos A. Lizos, Leandros Maglaras, Elena Petrovik, Saied M. Abd El-atty, Georgios Tsachtsiris, Mohamed Amine Ferrag,
• Abstract summary: This paper proposes a fault-tolerant and resilience-aware framework to mitigate cascading failures under cyber-physical attack conditions.<n>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.
• Score: 1.7614511833648008
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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