Zahir Tari, Adil Fahad, Abdulmohsen Almalawi, Xun Yi

Network Classification for Traffic Management

Anomaly Detection, Feature Selection, Clustering and Classification

• Gebundenes Buch

Produktbeschreibung

This authored book investigates network traffic classification solutions by proposing transport-layer methods to achieve better run and operated enterprise-scale networks.

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Produktdetails

• Verlag: Institution of Engineering & Technology
• Seitenzahl: 288
• Erscheinungstermin: 23. März 2020
• Englisch
• Abmessung: 234mm x 163mm x 23mm
• Gewicht: 590g
• ISBN-13: 9781785619212
• ISBN-10: 1785619217
• Artikelnr.: 57699223

Autorenporträt

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).

Inhaltsangabe

* 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