AI Agents for Secure and Software-Defined Networking
Apress (Verlag)
979-8-8688-2357-2 (ISBN)
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The book starts with the foundational concepts in AI and SDN, guiding readers toward advanced architectures and real-world applications. It examines urgent needs such as scalable, self-healing networks and proactive cybersecurity, showing how AI techniques—including reinforcement learning, graph neural networks, and explainable AI—can achieve intent-based networking, cognitive healing, federated learning, and intelligent automation. Each chapter combines conceptual overviews with detailed discussions, case studies, and actionable insights, making it accessible to students, researchers, engineers, and decision-makers alike. It bridges technical depth with broader considerations such as ethics, governance, energy efficiency, and disaster recovery. It unifies AI and networking into a single, practical framework rather than treating them as separate fields. The inclusion of curated resources, from books and blogs to courses and glossaries, supports ongoing learning beyond the text itself.
This book serves as a roadmap, guiding readers in designing intelligent, secure, and adaptive network ecosystems—essential for those aiming to lead the next generation of decentralized, resilient, and AI-driven digital infrastructure.
What you will learn:
Understand core AI-agent architectures and their integration with Software-Defined Networking for scalable, adaptive environments.
How to use ML, deep learning, reinforcement learning, and graph neural networks to optimize, automate, and secure networks.
How to develop AI-enabled networks with real-world case studies from telecom, smart cities, and enterprise IT.
Explore trends like federated learning, edge AI, programmable optical networks, and AI-driven disaster recovery
Who this book is for:
This book serves network architects and engineers using AI-driven automation to solve scalability and complexity challenges. It guides AI researchers and data scientists applying advanced methods for smarter, more efficient networks. Security professionals will also find value in AI-driven threat detection, incident response, and collaborative defense.
Het Mehta is a seasoned software engineer with nearly seven years at Cisco, specializing in AI-driven networking and SD-WAN innovation. Currently serving as a Software Engineer IV in San Jose, he has led impactful projects enhancing network reliability, scalability, and security. Het redesigned serial list synchronization for network authentication, integrating REST APIs with Neo4j for seamless and secure controller updates. He played a key role in improving network stability by decoupling control and data planes, ensuring uninterrupted MPLS and private connections. His creation of vDiagnose, a proactive SD-WAN health assessment tool, has boosted uptime and operational efficiency across deployments. Het has also developed selective logging features that streamline troubleshooting, cut noise, and speed resolution times. Earlier roles saw him engineering highly scalable, distributed control plane overlays and optimizing kernel footprints for diverse devices. With deep expertise in distributed systems, diagnostics, and enterprise networking, Het combines technical precision with a strong focus on user experience. His work consistently bridges innovation and practical deployment, making modern networks more resilient, adaptive, and intelligent. He is based in the US.
Chapter 1: AI-Agent Architectures for Decentralized Network Management.- Chapter 2: Adaptive AI Agents for Intent-Based Networking (IBN).- Chapter 3: Federated Learning for Collaborative Network Optimization.- Chapter 4: AI Agents for Cross-Layer Network Orchestration.- Chapter 5: Digital Twins for AI-Driven Network Simulations.- Chapter 6: Cognitive AI Agents for Proactive Network Healing.- Chapter 7: AI-Powered Dynamic Policy Management in SDN.- Chapter 8: Reinforcement Learning for Autonomous Traffic Engineering.- Chapter 9: Ethical AI Agents for Responsible Network Management.- Chapter 10: AI Agents for Network Threat Intelligence and Response.- Chapter 11: AI-Driven Resource Allocation in Multi-Tenant SDN Environments.- Chapter 12: AI Agents for Edge-Centric SDN Management.- Chapter 13: AI-Augmented Network Function Chaining in SDN.- Chapter 14: AI Agents for Zero-Touch Provisioning in SDN.- Chapter 15: Leveraging Graph Neural Networks for SDN Management.- Chapter 16: AI-Enhanced Security for Programmable Data Planes.- Chapter 17: Personalized Network Management with AI Agents.- Chapter 18: AI-Driven Energy Efficiency in SDN.- Chapter 19: AI-Powered Collaborative Security for Multi-Domain SDN.- Chapter 20: AI in Programmable Optical Networks with SDN.- Chapter 21: AI-Driven Predictive Analytics for SDN Scalability.- Chapter 22: Democratizing AI-Driven SDN Management with Open Source.- Chapter 23: AI Agents for SD-WAN Optimization.- Chapter 24: AI-Driven Security for SD-WAN.- Chapter 25: Ensuring Business Continuity with AI-Driven Disaster Recovery. Chapter 26: Additional Resources and Glossary.
| Erscheint lt. Verlag | 29.6.2026 |
|---|---|
| Zusatzinfo | Approx. 300 p. |
| Verlagsort | Berkley |
| Sprache | englisch |
| Maße | 178 x 254 mm |
| Themenwelt | Mathematik / Informatik ► Informatik ► Software Entwicklung |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Schlagworte | AI Agents for Network Automation • AI in Networking • Cognitive Network Management • Edge AI and SDN • Federated learning in networks • Intent-based Networking • Secure SDN architectures • Software-Defined Networking (SDN) |
| ISBN-13 | 979-8-8688-2357-2 / 9798868823572 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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