AI and Machine Learning for Network and Security Management

Yulei Wu, Jingguo Ge, Tong Li

• Publisher: Wiley-IEEE Press
• Year: 2022
• Print ISBN: 9781119835875
• eBook ISBN: 9781119835899
• Edition: 1
• Topic: Computer Science
• Subtopic: Cyber Security
• Index: Computer Science

AI AND MACHINE LEARNING FOR NETWORK AND SECURITY MANAGEMENT

Extensive Resource for Understanding Key Tasks of Network and Security Management

AI and Machine Learning for Network and Security Management covers a range of key topics of network automation for network and security management, including resource allocation and scheduling, network planning and routing, encrypted traffic classification, anomaly detection, and security operations. In addition, the authors introduce their large-scale intelligent network management and operation system and elaborate on how the aforementioned areas can be integrated into this system, plus how the network service can benefit.

Sample ideas covered in this thought-provoking work include:

• How cognitive means, e.g., knowledge transfer, can help with network and security management
• How different advanced AI and machine learning techniques can be useful and helpful to facilitate network automation
• How the introduced techniques can be applied to many other related network and security management tasks

Network engineers, content service providers, and cybersecurity service providers can use AI and Machine Learning for Network and Security Management to make better and more informed decisions in their areas of specialization. Students in a variety of related study programs will also derive value from the work by gaining a base understanding of historical foundational knowledge and seeing the key recent developments that have been made in the field.

Table of contents

• Cover
• Table of Contents
• Title Page
• Copyright
• Author Biographies
• Preface
• Acknowledgments
• Acronyms
• 1 Introduction
• 2 When Network and Security Management Meets AI and Machine Learning
• 3 Learning Network Intents for Autonomous Network Management*
• 4 Virtual Network Embedding via Hierarchical Reinforcement Learning1
• 5 Concept Drift Detection for Network Traffic Classification
• 6 Online Encrypted Traffic Classification Based on Lightweight Neural Networks*
• 7 Context‐Aware Learning for Robust Anomaly Detection*
• 8 Anomaly Classification with Unknown, Imbalanced and Few Labeled Log Data
• 9 Zero Trust Networks
• 10 Intelligent Network Management and Operation Systems
• 11 Conclusions, and Research Challenges and Open Issues
• Index
• End User License Agreement