Network Classification for Traffic Management: Anomaly detection, feature selection, clustering and classification
With the massive increase of data and traffic on the Internet within the 5G, IoT and smart cities frameworks, current network classification and analysis techniques are falling short. Novel approaches using machine learning algorithms are needed to cope with and manage real-world network traffic, including supervised, semi-supervised, and unsupervised classification techniques. Accurate and effective classification of network traffic will lead to better quality of service and more secure and manageable networks.

This authored book investigates network traffic classification solutions by proposing transport-layer methods to achieve better run and operated enterprise-scale networks. The authors explore novel methods for enhancing network statistics at the transport layer, helping to identify optimal feature selection through a global optimization approach and providing automatic labelling for raw traffic through a SemTra framework to maintain provable privacy on information disclosure properties.

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Network Classification for Traffic Management: Anomaly detection, feature selection, clustering and classification
With the massive increase of data and traffic on the Internet within the 5G, IoT and smart cities frameworks, current network classification and analysis techniques are falling short. Novel approaches using machine learning algorithms are needed to cope with and manage real-world network traffic, including supervised, semi-supervised, and unsupervised classification techniques. Accurate and effective classification of network traffic will lead to better quality of service and more secure and manageable networks.

This authored book investigates network traffic classification solutions by proposing transport-layer methods to achieve better run and operated enterprise-scale networks. The authors explore novel methods for enhancing network statistics at the transport layer, helping to identify optimal feature selection through a global optimization approach and providing automatic labelling for raw traffic through a SemTra framework to maintain provable privacy on information disclosure properties.

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Network Classification for Traffic Management: Anomaly detection, feature selection, clustering and classification

Network Classification for Traffic Management: Anomaly detection, feature selection, clustering and classification

Network Classification for Traffic Management: Anomaly detection, feature selection, clustering and classification

Network Classification for Traffic Management: Anomaly detection, feature selection, clustering and classification

Overview

With the massive increase of data and traffic on the Internet within the 5G, IoT and smart cities frameworks, current network classification and analysis techniques are falling short. Novel approaches using machine learning algorithms are needed to cope with and manage real-world network traffic, including supervised, semi-supervised, and unsupervised classification techniques. Accurate and effective classification of network traffic will lead to better quality of service and more secure and manageable networks.

This authored book investigates network traffic classification solutions by proposing transport-layer methods to achieve better run and operated enterprise-scale networks. The authors explore novel methods for enhancing network statistics at the transport layer, helping to identify optimal feature selection through a global optimization approach and providing automatic labelling for raw traffic through a SemTra framework to maintain provable privacy on information disclosure properties.

Product Details

ISBN-13: 9781785619212
Publisher: The Institution of Engineering and Technology
Publication date: 03/23/2020
Series: Computing and Networks
Pages: 288
Product dimensions: 6.14(w) x 9.21(h) x (d)

About the Author

Zahir Tari is a full professor and discipline head of the School of Computer Science, RMIT University, Australia. His expertise is in the areas of system performance (e.g., cloud, IoT) as well as system security (e.g., SCADA, cloud).


Adil Fahad is an assistant professor and head of the department of Computer Information Systems, Universityof Al Baha, Saudi Arabia. His research interests cover wireless sensor networks, mobile networks, SCADA security, ad-hoc networks, data mining, statistical analysis/modelling and machine learning.


Abdulmohsen Almalawi is an assistant professor in the Department of Computer Science at the Universityof King Abdulaziz, Saudi Arabia. His research interests are in the areas of machine learning.


Xun Yi is a professor at the School of Computer Science, RMIT University, Australia. His research interests include data privacy, cloud security, privacy-preserving data mining, network security protocols, applied cryptography, e-commerce security and mobile agent security.

Table of Contents

  • Chapter 1: Introduction
  • Chapter 2: Background
  • Chapter 3: Related work
  • Chapter 4: A taxonomy and empirical analysis of clustering algorithms for traffic classification
  • Chapter 5: Toward an efficient and accurate unsupervised feature selection
  • Chapter 6: Optimizing feature selection to improve transport layer statistics quality
  • Chapter 7: Optimality and stability of feature set for traffic classification
  • Chapter 8: A privacy-preserving framework for traffic data publishing
  • Chapter 9: A semi-supervised approach for network traffic labeling
  • Chapter 10: A hybrid clustering-classification for accurate and efficient network classification
  • Chapter 11: Conclusion
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