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

Zahir Tari, Adil Fahad, Abdulmohsen Almalawi, Xun Yi

Informasi Dasar

48 kali
23.21.224
004.6
Buku - Elektronik (E-Book)
1

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.

Subjek

NETWORK COMMUNICATION
TRAFIC-TELECOMMUNICATION,

Katalog

Network Classification for Traffic Management: Anomaly detection, feature selection, clustering and classification
9781785619229
288p.: pdf file.; 5 MB
English

Sirkulasi

Rp. 0
Rp. 0
Tidak

Pengarang

Zahir Tari, Adil Fahad, Abdulmohsen Almalawi, Xun Yi
Perorangan
 
 

Penerbit

IET
ne
2020

Koleksi

Kompetensi

 

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