Network Classification for Traffic Management
Anomaly detection, feature selection, clustering and classification
Published on 23. March 2020
Book
Hardback
288 pages
978-1-78561-921-2 (ISBN)
Description
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.
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.
More details
Series
Language
English
Place of publication
Stevenage
United Kingdom
Target group
College/higher education
Professional and scholarly
Product notice
sewn/stitched
Cloth over boards
Dimensions
Height: 234 mm
Width: 163 mm
Thickness: 23 mm
Weight
590 gr
ISBN-13
978-1-78561-921-2 (9781785619212)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
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, University of 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 University of 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.
Adil Fahad is an assistant professor and head of the department of Computer Information Systems, University of 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 University of 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.
Author
Full ProfessorRMIT University, School of Computer Science, Australia
Assistant ProfessorUniversity of Al Baha, Department of Computer Information Systems, Saudi Arabia
Assistant ProfessorUniversity of King Abdulaziz, Department of Computer Science, Saudi Arabia
ProfessorRMIT University, School of Computer Science, Australia
Content
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
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