Network Classification for Traffic Management - Anomaly detection, feature selection, clustering and classification (Hardcover)

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.

General

Imprint:	Institution Of Engineering And Technology
Country of origin:	United Kingdom
Series:	Computing and Networks
Release date:	March 2020
First published:	2020
Authors:	Zahir Tari (Full Professor) • Adil Fahad (Assistant Professor) • Abdulmohsen Almalawi (Assistant Professor) • Xun Yi (Professor)
Dimensions:	234 x 156mm (L x W)
Format:	Hardcover
Pages:	288
ISBN-13:	978-1-78561-921-2
Categories:	Books > Computing & IT > Computer hardware & operating systems > Systems management Books > Computing & IT > Computer communications & networking > General
LSN:	1-78561-921-7
Barcode:	9781785619212

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