Learning From Few Cyber-Attacks: Addressing the Class Imbalance Problem in Machine Learning-Based Intrusion Detection in Software-Defined Networking

Learning From Few Cyber-Attacks: Addressing the Class Imbalance Problem in Machine Learning-Based Intrusion Detection in Software-Defined Networking

The class imbalance problem negatively impacts learning algorithms’ performance in minority classes which may constitute more severe attacks than the majority ones. This study investigates the benefits of balancing strategies and imbalanced learning approaches on intrusion data from Softw...

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Bibliographic Details
Main Authors: Seyed Mohammad Hadi Mirsadeghi, Hayretdin Bahsi, Risto Vaarandi, Wissem Inoubli
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Class imbalance problem
machine learning
deep learning
cyber intrusion detection
software-defined networking
Online Access:https://ieeexplore.ieee.org/document/10353929/

Internet

https://ieeexplore.ieee.org/document/10353929/

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