AI and Machine Learning for Network and Security Management (IEEE Press Series on Network and Service Management) - Hardcover

Über die Autorin bzw. den Autor

Yulei Wu, is a Senior Lecturer with the Department of Computer Science, Faculty of Environment, Science and Economy, University of Exeter, UK. His research focuses on networking, Internet of Things, edge intelligence, information security, and ethical AI. He serves as an Associate Editor for IEEE Transactions on Network and Service Management, and IEEE Transactions on Network Science and Engineering, as well as an Editorial Board Member of Computer Networks, Future Generation Computer Systems, and Nature Scientific Reports at Nature Portfolio. He is a Senior Member of the IEEE and the ACM, and a Fellow of the HEA (Higher Education Academy).

Jingguo Ge, is currently a Professor of the Institute of Information Engineering, Chinese Academy of Sciences (CAS), and also a Professor of School of Cyber Security, University of Chinese Academy of Sciences. His research focuses on Future Network Architecture, 5G/6G, Software-defined networking (SDN), Cloud Native networking, Zero Trust Architecture. He has published more than 60 research papers and is the holder of 28 patents. He participated in the formulation of 3 ITU standards on IMT2020.

Tong Li, is currently a Senior Engineer of Institute of Information Engineering at the Chinese Academy of Sciences (CAS). His research and engineering focus on Computer Networks, Cloud Computing, Software-Defined Networking (SDN), and Distributed Network and Security Management. He participated 2 ITU standards on IMT2020 and developed many large-scale software systems on SDN, network management and orchestration.

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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.