Mehta AI Agents for Secure and Software-Defined Networking
1. Auflage 2026
ISBN: 979-8-8688-2358-9
Verlag: APRESS
Format: PDF
Kopierschutz: 1 - PDF Watermark
Harnessing AI and SDN to Revolutionize Modern Work Environments
E-Book, Englisch, 270 Seiten
Reihe: Professional and Applied Computing (R0)
ISBN: 979-8-8688-2358-9
Verlag: APRESS
Format: PDF
Kopierschutz: 1 - PDF Watermark
This book explores how Artificial Intelligence (AI) and Software-Defined Networking (SDN) can transform the way modern networks are designed, secured, and operated. In an era shaped by cloud computing, IoT, 5G, and edge computing, traditional network management is no longer enough—this book reveals how AI agents bring autonomy, intelligence, and adaptability to meet these challenges.
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.
Zielgruppe
Professional/practitioner
Autoren/Hrsg.
Weitere Infos & Material
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.- C hapter 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.




