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

2025-12-27

AI Summary

Emerging composite cybersecurity threats arising from the convergence of SDN, NFV, O-RAN, and cloud-native architectures necessitate a redefinition of network resilience theory. Method: This work proposes a unified resilience paradigm integrating adaptability, proactivity, and retroactivity, achieved by systematically unifying zero-trust architecture, game-theoretic threat modeling, and self-healing mechanisms—augmented innovatively with reinforcement learning and large language models (LLMs) to enable dynamic response and multi-agent collaborative defense. Contributions: (1) A quantifiable, multi-dimensional resilience evaluation framework; (2) Empirical validation in simulated attack scenarios demonstrating >40% improvement in AI-driven autonomous recovery latency; (3) A comprehensive resilience engineering methodology spanning design, deployment, and continuous evolution. The approach advances foundational resilience theory while delivering practical, deployable safeguards for next-generation programmable networks.

Technology Category

• Game Theory and Economic Paradigms: Adversarial Learning
• Machine Learning: Deep Neural Architectures and Foundation Models
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

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.

Problem

• Addresses cyber resilience challenges in next-generation networks
• Examines AI-driven threats and adaptive security mechanisms
• Proposes zero trust and self-healing strategies for network defense

Innovation

• Zero trust architectures for adaptive security
• Game-theoretic modeling for threat anticipation
• AI-driven self-healing and autonomous network control