Oversampling Imbalanced Data Based on Convergent WGAN for Network Threat Detection
Class imbalance is a common problem in network threat detection. Oversampling the minority class is regarded as a popular countermeasure by generating enough new minority samples. Generative adversarial network (GAN) is a typical generative model that can generate any number of artificial minority s...
Saved in:
Main Authors: | Yanping Xu, Xiaoyu Zhang, Zhenliang Qiu, Xia Zhang, Jian Qiu, Hua Zhang |
---|---|
Format: | article |
Language: | EN |
Published: |
Hindawi-Wiley
2021
|
Subjects: | |
Online Access: | https://doaj.org/article/7a3475a20f2243a9be0d3e6b5809238a |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
An oversampling method for multi-class imbalanced data based on composite weights.
by: Mingyang Deng, et al.
Published: (2021) -
An oversampling method for multi-class imbalanced data based on composite weights
by: Mingyang Deng, et al.
Published: (2021) -
A Novel Oversampling Method for Imbalanced Datasets Based on Density Peaks Clustering
by: Jie Cao*, et al.
Published: (2021) -
Economic Growth Prediction Algorithm of Coastal Area Based on Impulse Response Function
by: Qiu Rong-Shan, et al.
Published: (2021) -
Improving the Accuracy of Network Intrusion Detection with Causal Machine Learning
by: Zengri Zeng, et al.
Published: (2021)