Read more
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.
List of contents
Author Biographies xiii
Preface xv
Acknowledgments xvii
Acronyms xix
1 Introduction 1
1.1 Introduction 1
1.2 Organization of the Book 3
1.3 Conclusion 6
References 6
2 When Network and Security Management Meets AI and Machine Learning 9
2.1 Introduction 9
2.2 Architecture of Machine Learning-Empowered Network and Security Management 10
2.3 Supervised Learning 12
2.3.1 Classification 12
2.3.2 Regression 15
2.4 Semisupervised and Unsupervised Learning 15
2.4.1 Clustering 17
2.4.2 Dimension Reduction 17
2.4.3 Semisupervised Learning 18
2.5 Reinforcement Learning 18
2.5.1 Policy-Based 21
2.5.2 Value-Based 22
2.6 Industry Products on Network and Security Management 24
2.6.1 Network Management 24
2.6.1.1 Cisco DNA Center 24
2.6.1.2 Sophie 25
2.6.1.3 Juniper EX4400 Switch 25
2.6.1.4 Juniper SRX Series Services Gateway 25
2.6.1.5 H3C SeerAnalyzer 26
2.6.2 Security Management 27
2.6.2.1 SIEM, IBM QRadar Advisor with Watson 27
2.6.2.2 FortiSandbox 27
2.6.2.3 FortiSIEM 28
2.6.2.4 FortiEDR 28
2.6.2.5 FortiClient 29
2.6.2.6 H3C SecCenter CSAP 29
2.7 Standards on Network and Security Management 29
2.7.1 Network Management 29
2.7.1.1 Cognitive Network Management 30
2.7.1.2 End-to-End 5G and Beyond 30
2.7.1.3 Software-Defined Radio Access Network 32
2.7.1.4 Architectural Framework for ML in Future Networks 32
2.7.2 Security Management 33
2.7.2.1 Securing AI 33
2.8 Projects on Network and Security Management 34
2.8.1 Poseidon 34
2.8.2 NetworkML 35
2.8.3 Credential-Digger 36
2.8.4 Adversarial Robustness Toolbox 37
2.9 Proof-of-Concepts on Network and Security Management 38
2.9.1 Classification 38
2.9.1.1 Phishing URL Classification 38
2.9.1.2 Intrusion Detection 39
2.9.2 Active Learning 39
2.9.3 Concept Drift Detection 40
2.10 Conclusion 41
References 42
3 Learning Network Intents for Autonomous Network Management 49
3.1 Introduction 49
3.2 Motivation 52
3.3 The Hierarchical Representation and Learning Framework for Intention Symbols Inference 53
3.3.1 Symbolic Semantic Learning (SSL) 53
3.3.1.1 Connectivity Intention 55
3.3.1.2 Deadlock Free Intention 56
3.3.1.3 Performance Intention 57
3.3.1.4 Discussion 57
3.3.2 Symbolic Structure Inferring (SSI) 57
3.4 Experiments 59
3.4.1 Datasets 59
3.4.2 Experiments on Symbolic Semantic Learning 60
3.4.3 Experiments on Symbolic Structure Inferring 62
3.4.4 Experiments on Symbolic Structure Transferring 64
3.5 Conclusion 66
References 66
4 Virtual Network Embedding via Hierarchical Reinforcement Learning 69
4.1 Introduction 69
4.2 Motivation 70
4.3 Preliminaries and Notations 72
4.3.1 Virtual Network Embedding 72
4.3.1.1 Substrate Network and Virtual Network 72
4.3.1.2 The VNE Problem 72
4.3.1.3 Evaluation Metrics 73
4.3.2 Reinforcement Learning 74
4.3.3 Hierarchical Reinforcement Learning 75
4.4 The Framework of VNE-HRL 75
4.4.1 Overview 75
4.4.2 The High-level Agent 77
4.4.2.1 State Encoder for
About the author
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.
