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

The book investigates network traffic classification solutions by proposing transport-layer methods to achieve better run and operated enterprise-scale networks. It deals with the following subjects: traffic management; anomaly detection; clustering algorithms; unsupervised feature selection; transp...

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Bibliographic Details
Main Author:	Tari, Zahir
Corporate Author:	Institution of Engineering and Technology
Other Authors:	Fahad, Adil, Almalawi, Abdulmohsen, Yi, Xun
Format:	eBook
Language:	English
Published:	Stevenage : IET, 2020.
Series:	Computing.
Subjects:	Computer networks > Security measures. Data protection. Machine learning. Privacy. Telecommunication. computer network management. computer network security. data privacy. feature selection. learning (artificial intelligence) pattern classification. pattern clustering. telecommunication traffic. semisupervised network traffic labelling traffic data publishing privacy preservation transport layer statistics quality unsupervised feature selection clustering algorithms hybrid clustering-classification feature selection anomaly detection traffic management network traffic classification enterprise-scale networks Electronic books.
Online Access:	Connect to the full text of this electronic book

Description
Summary:	The book investigates network traffic classification solutions by proposing transport-layer methods to achieve better run and operated enterprise-scale networks. It deals with the following subjects: traffic management; anomaly detection; clustering algorithms; unsupervised feature selection; transport layer statistics quality; feature set; privacy preserving framework for traffic data publishing; semi-supervised approach for network traffic labelling; and hybrid clustering-classification.
Physical Description:	1 online resource (288 pages)
ISBN:	9781785619229
DOI:	10.1049/PBPC032E