A Comprehensive Survey of Generative Adversarial Networks (GANs) in Cybersecurity Intrusion Detection-Reference-Cited by-同舟云学术

A Comprehensive Survey of Generative Adversarial Networks (GANs) in Cybersecurity Intrusion Detection

Author:

Dunmore Aeryn¹^ORCID,Jang-Jaccard Julian¹^ORCID,Sabrina Fariza²^ORCID,Kwak Jin³

Affiliation:

1. Department of Computer Science, Cybersecurity Laboratory, Massey University, Auckland, New Zealand

2. School of Engineering and Technology, Central Queensland University, Sydney, NSW, Australia

3. Department of Cyber Security, Ajou University, Suwon, South Korea

Funder

Ministry of Business, Innovation, and Employment (MBIE) from the New Zealand Government

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

Reference147 articles.

1. Combating mode collapse in GAN training: An empirical analysis using Hessian eigenvalues;durall;arXiv 2012 09673,2020

3. Continual learning in generative adversarial nets;seff;arXiv 1705 08395,2017

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