Effect of Balancing Data Using Synthetic Data on the Performance of Machine Learning Classifiers for Intrusion Detection in Computer Networks

Effect of Balancing Data Using Synthetic Data on the Performance of Machine Learning Classifiers for Intrusion Detection in Computer Networks

Attacks on computer networks have increased significantly in recent days, due in part to the availability of sophisticated tools for launching such attacks as well as the thriving underground cyber-crime economy to support it. Over the past several years, researchers in academia and industry used ma...

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Bibliographic Details
Main Authors: Ayesha Siddiqua Dina, A. B. Siddique, D. Manivannan
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Cyber security
conditional generative adversarial network (CTGAN)
data imbalance problem
intrusion detection
machine learning
over-sampling
Online Access:https://ieeexplore.ieee.org/document/9882118/

Internet

https://ieeexplore.ieee.org/document/9882118/

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