Name: Network Classification for Traffic Management: Anomaly Detection, Feature Selection, Clustering and Classification (Computing and Networks) (en Inglés)
SKU: 52419220
Price: 143.41 USD
Availability: InStock
Author: Tari, Zahir ; Fahad, Adil ; Almalawi, Abdulmohsen
ISBN: 9781785619212

portada Network Classification for Traffic Management: Anomaly Detection, Feature Selection, Clustering and Classification (Computing and Networks) (en Inglés)

Network Classification for Traffic Management: Anomaly Detection, Feature Selection, Clustering and Classification (Computing and Networks) (en Inglés)

Formato

Libro Físico

Autor

Zahir Tari

Adil Fahad

Abdulmohsen Almalawi

Editorial

Institution of Engineering & Technology

Año

2020

Idioma

Inglés

N° páginas

288

Encuadernación

Tapa Dura

Dimensiones

23.4x16.3x2.3 cm

Peso

0.59 kg.

ISBN13

9781785619212

Categorías

Redes y comunicaciones informáticas

Network Classification for Traffic Management: Anomaly Detection, Feature Selection, Clustering and Classification (Computing and Networks) (en Inglés)

Zahir Tari (Autor) · Adil Fahad (Autor) · Abdulmohsen Almalawi (Autor) · Institution of Engineering & Technology · Tapa Dura

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Reseña del libro "Network Classification for Traffic Management: Anomaly Detection, Feature Selection, Clustering and Classification (Computing and Networks) (en Inglés)"

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.