Название: AI and Machine Learning for Network and Security Management
Автор: Jingguo Ge, Tong Li
Издательство: Wiley-IEEE
Год: 2023
Страниц: 283
Язык: английский
Формат: epub (true)
Размер: 10.1 MB

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.

With the fast development of networking technologies, the communication network has gone through four generations and is in the process of deploying the fifth‐generation system (5G) worldwide. 5G has its unique feature of accommodating diversified services on top of a shared infrastructure. These services not only include the telecommunication service that we use every day for our daily lives, but also encompass a wide variety of services in support of many important vertical industries including energy, health, water, manufacturing, environment, to name a few. These services are mainly classified into three broad categories: enhanced Mobile Broadband (eMBB), Ultra Reliable Low Latency Communications (URLLC), and massive Machine Type Communications (mMTC). The deployment of 5G to support eMBB services has already started in the globe, and that of supporting URLLC and mMTC will start in the foreseeable future. Meanwhile, research of next‐generation communication systems, i.e. beyond 5G (B5G) or 6G, has already started with many research centers and groups established globally.

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.

Contents:
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 Learning
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