Improved Feature Selection and Stream Traffic Classification Based on Machine Learning in Software-Defined Networks

Improved Feature Selection and Stream Traffic Classification Based on Machine Learning in Software-Defined Networks

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Traffic classification (TC) in software-defined networks (SDN) using machine learning (ML) appears to be a viable option for improving network management. TC improves SDN operability, while SDN speeds up the feature selection (FS) process, especially when ML is used as a classification mechanism to...

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Bibliographic Details
Main Authors: Arwa M. Eldhai, Mosab Hamdan, Ahmed Abdelaziz, Ibrahim Abaker Targio Hashem, Sharief F. Babiker, M. N. Marsono, Muzaffar Hamzah, Noor Zaman Jhanjhi
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Adaptive random forest
features selection
Hoeffding adaptive trees
k-nearest neighbor with adaptive sliding window detector
machine learning
software-defined networks
Online Access:https://ieeexplore.ieee.org/document/10445219/

Internet

https://ieeexplore.ieee.org/document/10445219/

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