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.

翻译：由软件定义网络（SDN）、网络功能虚拟化（NFV）、开放无线接入网（O-RAN）以及云原生架构等创新所驱动的网络系统演进，正在重新定义关键基础设施的运营格局与威胁面。本书通过深入的跨学科审视，探讨了应如何重新概念化与重新设计弹性机制，以应对这些变革带来的多层面挑战。全书共分六章，首先审视了当代风险格局，识别了新兴的网络、物理及人工智能驱动的威胁，并分析了它们对可扩展、异构网络环境的影响。随后，本书建立了严谨的弹性定义与评估框架，超越了传统的鲁棒性与容错性概念，旨在解决跨不同应用领域的自适应、前瞻性与追溯性机制。本书的核心深入探讨了弹性的先进范式与实践策略，包括零信任架构、博弈论威胁建模以及自愈设计原则。其中相当篇幅专门论述了人工智能，特别是强化学习与大语言模型（LLMs），在实现动态威胁响应、自主网络控制以及不确定条件下的多智能体协调方面所扮演的角色。