Cyber Resilience in Next-Generation Networks: Threat Landscape, Theoretical Foundations, and Design Paradigms

TL;DR

This paper explores how next-generation network architectures like SDN, NFV, and cloud-native systems impact cyber resilience, emphasizing new threat landscapes and innovative design paradigms involving AI and self-healing strategies.

Contribution

It provides a comprehensive interdisciplinary framework for understanding, evaluating, and enhancing resilience in evolving network environments with novel paradigms and AI integration.

Findings

Identification of emerging cyber and physical threats in modern networks.

Development of new resilience evaluation frameworks beyond traditional robustness.

Highlighting AI's role in autonomous threat response and network control.

Abstract

The evolution of networked systems, driven by innovations in software-defined networking (SDN), network function virtualization (NFV), open radio access networks (O-RAN), and cloud-native architectures, is redefining both the operational landscape and the threat surface of critical infrastructures. This book offers an in-depth, interdisciplinary examination of how resilience must be re-conceptualized and re-engineered to address the multifaceted challenges posed by these transformations. Structured across six chapters, this book begins by surveying the contemporary risk landscape, identifying emerging cyber, physical, and AI-driven threats, and analyzing their implications for scalable, heterogeneous network environments. It then establishes rigorous definitions and evaluation frameworks for resilience, going beyond robustness and fault-tolerance to address adaptive, anticipatory, and retrospective mechanisms across diverse application domains. The core of the book delves into advanced paradigms and practical strategies for resilience, including zero trust architectures, game-theoretic threat modeling, and self-healing design principles. A significant portion is devoted to the role of artificial intelligence, especially reinforcement learning and large language models (LLMs), in enabling dynamic threat response, autonomous network control, and multi-agent coordination under uncertainty.