scholarly journals A tutorial on generative adversarial networks with application to classification of imbalanced data

2021 ◽  
Author(s):  
Yuxiao Huang ◽  
Kara G. Fields ◽  
Yan Ma
Keyword(s):  
Imbalanced Data ◽  
Generative Adversarial Networks ◽  
Adversarial Networks

scholarly journals Deep learning approach to classification of lung cytological images: Two-step training using actual and synthesized images by progressive growing of generative adversarial networks

PLoS ONE ◽  
2020 ◽  
Vol 15 (3) ◽  
pp. e0229951 ◽  
Author(s):  
Atsushi Teramoto ◽  
Tetsuya Tsukamoto ◽  
Ayumi Yamada ◽  
Yuka Kiriyama ◽  
Kazuyoshi Imaizumi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Generative Adversarial Networks ◽  
Learning Approach ◽  
Adversarial Networks

scholarly journals Generalization of Deep Neural Networks for Imbalanced Fault Classification of Machinery Using Generative Adversarial Networks

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 111168-111180 ◽  
Author(s):  
Jinrui Wang ◽  
Shunming Li ◽  
Baokun Han ◽  
Zenghui An ◽  
Huaiqian Bao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Fault Classification ◽  
Generative Adversarial Networks ◽  
Adversarial Networks

scholarly journals Handling Imbalanced Data in Intrusion Detection Systems using Generative Adversarial Networks

2020 ◽  
Vol 2020 (1) ◽  
pp. 1-13
Author(s):  
Ly Vu ◽  
Quang Uy Nguyen
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Detection System ◽  
Imbalanced Data ◽  
Original Data ◽  
Machine Learning Techniques ◽  
Generative Adversarial Networks ◽  
Detection Systems ◽  
Adversarial Networks ◽  
Attack Data

Machine learning-based intrusion detection hasbecome more popular in the research community thanks to itscapability in discovering unknown attacks. To develop a gooddetection model for an intrusion detection system (IDS) usingmachine learning, a great number of attack and normal datasamples are required in the learning process. While normaldata can be relatively easy to collect, attack data is muchrarer and harder to gather. Subsequently, IDS datasets areoften dominated by normal data and machine learning modelstrained on those imbalanced datasets are ineffective in detect-ing attacks. In this paper, we propose a novel solution to thisproblem by using generative adversarial networks to generatesynthesized attack data for IDS. The synthesized attacks aremerged with the original data to form the augmented dataset.Three popular machine learning techniques are trained on theaugmented dataset. The experiments conducted on the threecommon IDS datasets and one our own dataset show thatmachine learning algorithms achieve better performance whentrained on the augmented dataset of the generative adversarialnetworks compared to those trained on the original datasetand other sampling techniques. The visualization techniquewas also used to analyze the properties of the synthesizeddata of the generative adversarial networks and the others.

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