Troubleshooting for Network Operators : The Road to a New Paradigm with Encrypted Traffic

Troubleshooting for Network Operators The Road to a New Paradigm with Encrypted Traffic

Nowadays, the Internet is becoming more and more complex due to an everincreasing number of network devices, various multimedia services and a prevalence of encrypted traffic. Therefore, in this context, this book presents a novel efficient multi modular troubleshooting architecture to overcome limi...

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Detalles Bibliográficos
Otros Autores: Tong, Van Van, author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: London, England ; Hoboken, NJ : ISTE Ltd [2023]
Edición:First edition
Colección:Networks & telecommunications series. New generation networks set ; volume 3
Materias:
Computer networks > Management.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009811326006719
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright Page
  • Contents
  • Preface
  • Introduction
  • Chapter 1. State of the Art on Network Troubleshooting
  • 1.1. Network troubleshooting
  • 1.1.1. State of the art
  • 1.1.2. Traditional troubleshooting architecture
  • 1.2. Background on encryption protocols
  • 1.2.1. QUIC
  • 1.2.2. Other protocols
  • 1.3. Drawbacks of troubleshooting with encrypted traffic
  • 1.3.1. Network performance monitoring
  • 1.3.2. Intrusion detection system
  • 1.4. Conclusion
  • Chapter 2. Novel Global Troubleshooting Framework for Encrypted Traffic
  • 2.1. Novel network troubleshooting architecture for encrypted traffic
  • 2.2. Proof of concept of novel troubleshooting architecture in SDN
  • 2.3. Data collection
  • 2.3.1. Data classification
  • 2.3.2. Monitoring tools
  • 2.3.3. Parameter measurement
  • 2.4. Troubleshooting dataset
  • 2.4.1. Datasets for root cause analysis
  • 2.4.2. Dataset for traffic classification
  • 2.5. Conclusion
  • Chapter 3. Traffic Classification: Novel QUIC Traffic Classifier Based on Convolutional Neural Network
  • 3.1. Introduction
  • 3.2. Background
  • 3.2.1. Convolutional network
  • 3.2.2. Characteristics of QUIC-based applications
  • 3.3. Traffic classification approaches
  • 3.3.1. Port-based approaches
  • 3.3.2. Payload-based approaches
  • 3.3.3. Statistic-based approaches
  • 3.3.4. DL-based approaches
  • 3.4. Novel traffic classification method for QUIC traffic
  • 3.4.1. Traffic collection
  • 3.4.2. Flow-based features
  • 3.4.3. Preprocessing
  • 3.4.4. Novel traffic classification method
  • 3.5. Experimental results
  • 3.5.1. Dataset specification
  • 3.5.2. Performance metrics
  • 3.5.3. Performance analysis
  • 3.6. Conclusion
  • Chapter 4. Anomaly Detection
  • 4.1. Introduction
  • 4.2. Anomaly detection approaches
  • 4.2.1. Knowledge-based mechanisms
  • 4.2.2. Rule inductions
  • 4.2.3. Information theory.
  • 4.2.4. ML-based mechanisms
  • 4.3. Anomaly detection approach using machine learning
  • 4.3.1. ML-based anomaly detection method
  • 4.3.2. Data collection and processing
  • 4.4. Experimental results
  • 4.4.1. Experimental setup
  • 4.4.2. Performance analysis
  • 4.5. Conclusion
  • Chapter 5. Temporary Remediation: SDN-based Application-awareSegmentRoutingforLarge-scaleNetworks
  • 5.1. Introduction
  • 5.2. Application-aware routing mechanisms
  • 5.2.1. Application-aware routing
  • 5.2.2. Application-aware MPLS
  • 5.2.3. Application-aware SR
  • 5.3. Adaptive segment routing mechanism for encrypted traffic
  • 5.3.1. Overview of the SDN-based adaptive segment routing framework
  • 5.3.2. Network monitoring
  • 5.3.3. Anomaly detection
  • 5.3.4. Application-aware remediation
  • 5.4. Experimental results
  • 5.4.1. Experiment setup
  • 5.4.2. Benchmark
  • 5.4.3. Performance analysis
  • 5.5. Conclusion
  • Chapter 6. Root Cause Analysis and Definitive Remediation
  • 6.1. Root cause analysis: machine learning based root cause analysis for SDN network
  • 6.1.1. Introduction
  • 6.1.2. Root cause analysis mechanisms
  • 6.1.3. ML-based RCA mechanism
  • 6.1.4. Experimental results
  • 6.1.5. Conclusion
  • 6.2. Definitive remediation: adaptive QUIC BBR algorithm using reinforcement learning for dynamic networks
  • 6.2.1. Introduction
  • 6.2.2. Congestion control mechanisms
  • 6.2.3. Adaptive BBR algorithm
  • 6.2.4. Experimental results
  • 6.2.5. Conclusion
  • Conclusions and Prospects
  • References
  • Index
  • EULA.

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