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A_Comprehensive_Survey_of_Generative_Adversarial_Networks_GANs_in_Cybersecurity_Intrusion_Detection.pdf (1.59 MB)

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

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posted on 2024-04-26, 02:01 authored by Aeryn Dunmore, Julian Jang-Jaccard, Fariza SabrinaFariza Sabrina, Jin Kwak
Generative Adversarial Networks (GANs) have seen significant interest since their introduction in 2014. While originally focused primarily on image-based tasks, their capacity for generating new, synthetic data has brought them into many different fields of Machine Learning research. Their use in cybersecurity has grown swiftly, especially in tasks which require training on unbalanced datasets of attack classes. In this paper we examine the use of GANs in Intrusion Detection Systems (IDS) and how they are currently being employed in this area of research. GANs are currently in use for the creation of adversarial examples, editing the semantic information of data, creating polymorphic samples of malware, augmenting data for rare classes, and much more. We have endeavored to create a paper that may act as a primer for cybersecurity specialists and machine learning researchers alike. This paper details what GANs are and how they work, the current types of GAN in use in the area, datasets used in this research, metrics for evaluation, current areas of use in intrusion detection, and when and how they are best used.

Funding

Cyber Security Research Programme - Artificial Intelligence for Automating Response to Threats

Ministry of Business, Innovation and Employment

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History

Volume

11

Start Page

76071

End Page

76094

Number of Pages

24

eISSN

2169-3536

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Additional Rights

CC BY

Peer Reviewed

  • Yes

Open Access

  • Yes

Acceptance Date

2023-07-14

External Author Affiliations

Massey University, NZ

Era Eligible

  • Yes

Journal

IEEE Access