Screen LibraryDeveloping networks using artificial intelligence /
This book mainly discusses the most important issues in artificial intelligence-aided future networks, such as applying different ML approaches to investigate solutions to intelligently monitor, control and optimize networking. The authors focus on four scenarios of successfully applying machine lea...
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| Main Authors: | |
|---|---|
| Group Author: | ; |
| Published: |
Springer,
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| Publisher Address: | Cham : |
| Publication Dates: | [2019] |
| Literature type: | Book |
| Language: | English |
| Series: |
Wireless networks,
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| Subjects: | |
| Summary: |
This book mainly discusses the most important issues in artificial intelligence-aided future networks, such as applying different ML approaches to investigate solutions to intelligently monitor, control and optimize networking. The authors focus on four scenarios of successfully applying machine learning in network space. It also discusses the main challenge of network traffic intelligent awareness and introduces several machine learning-based traffic awareness algorithms, such as traffic classification, anomaly traffic identification and traffic prediction. The authors introduce some ML app |
| Carrier Form: | xi, 248 pages : illustrations ; 25 cm. |
| Bibliography: | Includes bibliographical references. |
| ISBN: |
9783030150273 3030150275 9783030150297 3030150291 9783030150303 3030150305 |
| Index Number: | Q335 |
| CLC: | TP18 |
| Call Number: | TP18/Y25 |
| Contents: |
Intro; Preface; Acknowledgments; Contents; 1 Introduction; 1.1 Background; 1.2 Overview of SDN and Machine Learning; 1.2.1 Software Defined Networking (SDN); 1.2.2 Machine Learning; 1.2.2.1 Supervised Learning; 1.2.2.2 Unsupervised Learning; 1.2.2.3 Reinforcement Learning; 1.3 Related Research and Development; 1.3.1 3GPP SA2; 1.3.2 ETSI ISG ENI; 1.3.3 ITU-T FG-ML5G; 1.4 Organizations of This Book; 1.5 Summary; 2 Intelligence-Driven Networking Architecture; 2.1 Network AI: An Intelligent Network Architecture for Self-Learning Control Strategies in Software Defined Networks 2.1.1 Network Architecture2.1.1.1 Forwarding Plane; 2.1.1.2 Control Plane; 2.1.1.3 AI Plane; 2.1.2 Network Control Loop; 2.1.2.1 Action Issue; 2.1.2.2 Network State Upload; 2.1.2.3 Policy Generation; 2.1.3 Use Case; 2.1.4 Challenges and Discussions; 2.1.4.1 Communication Overhead; 2.1.4.2 Training Cost; 2.1.4.3 Testbeds; 2.2 Summary; References; 3 Intelligent Network Awareness; 3.1 Intrusion Detection System Based on Multi-Level Semi-Supervised Machine Learning; 3.1.1 Proposed Scheme (MSML); 3.1.1.1 Pure Cluster Extraction (PCE); 3.1.1.2 Pattern Discovery (PD) 3.1.1.3 Fine-Grained Classification (FC)3.1.1.4 Model Updating; 3.1.1.5 The Hyper-Parameters; 3.1.2 Evaluation; 3.1.2.1 Dataset; 3.1.2.2 Data Pre-process; 3.1.2.3 Evaluation Criteria; 3.1.2.4 Baseline Model; 3.1.2.5 MSML; 3.2 Intrusion Detection Based on Hybrid Multi-Level Data Mining; 3.2.1 The Framework of HMLD; 3.2.2 HMLD with KDDCUP99; 3.2.2.1 KDDCUP99 Dataset; 3.2.2.2 MH-DE Module; 3.2.2.3 MH-ML Module; 3.2.2.4 MEM Module; 3.2.3 Experimental Results and Discussions; 3.2.3.1 Evaluation Criteria; 3.2.3.2 Experiments and Analysis 3.3 Abnormal Network Traffic Detection Based on Big Data Analysis3.3.1 System Model; 3.3.1.1 Normal Traffic Selection Model; 3.3.1.2 Abnormal Traffic Selection Model; 3.3.1.3 Abnormal Traffic Selection Model; 3.3.2 Simulation Results and Discussions; 3.3.2.1 Data Set; 3.3.2.2 Simulation Results; 3.3.2.3 Discussing Result of No. 8 and No. 11; 3.3.2.4 Discussing Result of No. 5 and No. 7; 3.3.2.5 Discussing Result of No. 3 and No. 4; 3.4 Summary; References; 4 Intelligent Network Control; 4.1 Multi-Controller Optimization in SDN; 4.1.1 System Model; 4.1.1.1 Network Model 4.1.1.2 Communication Model4.1.1.3 Computation Model; 4.1.1.4 Problem Formulation; 4.1.2 Methodology; 4.1.2.1 PSO Aided Near-Optimal Multi-Controller Placement; 4.1.2.2 Resource Management Relying on Deep Q-Learning; 4.1.3 Simulation Results; 4.2 QoS-Enabled Load Scheduling Based on ReinforcementLearning; 4.2.1 System Description; 4.2.1.1 Energy Internet; 4.2.1.2 Software-Defined Energy Internet; 4.2.1.3 Controller Mind framework; 4.2.1.4 Re-Queuing Module; 4.2.1.5 Info-Table Module; 4.2.1.6 Learning Module; 4.2.2 System Model; 4.2.2.1 Re-Queuing Model; 4.2.2.2 Workload Model |