Network Intrusion Detection Based on Conditional Wasserstein Generative Adversarial Network and Cost-Sensitive Stacked Autoencoder

Network Intrusion Detection Based on Conditional Wasserstein Generative Adversarial Network and Cost-Sensitive Stacked Autoencoder

In the field of intrusion detection, there is often a problem of data imbalance, and more and more unknown types of attacks make detection difficult. To resolve above issues, this article proposes a network intrusion detection model called CWGAN-CSSAE, which combines improved conditional Wasserstein...

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Bibliographic Details
Main Authors: Guoling Zhang, Xiaodan Wang, Rui Li, Yafei Song, Jiaxing He, Jie Lai
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Intrusion detection
conditional Wasserstein GAN
stacked autoencoder
imbalanced classification
cost-sensitive
regularization
Online Access:https://ieeexplore.ieee.org/document/9229088/

Internet

https://ieeexplore.ieee.org/document/9229088/

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