APPLICATION OF GENERATIVE AI IN CYBERSECURITY DETECTION AND PREVENTION OF NEXT-GENERATION ATTACKS

dc.contributor.author	Aram Andreasyan
dc.date.accessioned	2026-03-02T20:33:46Z
dc.date.issued	2024-11-05
dc.description.abstract	This article examines modern approaches to applying generative artificial intelligence (Generative AI) to cybersecurity problems. Particular attention is paid to the use of generative adversarial networks (GANs), large-scale language models (LLMs), and transformer architectures to detect, model, and prevent next-generation attacks, including zero - day attacks, polymorphic malware, and phishing.
dc.format	application/pdf
dc.identifier.uri	https://americanjournal.org/index.php/ajtas/article/view/678
dc.identifier.uri	https://asianeducationindex.com/handle/123456789/117689
dc.language.iso	eng
dc.publisher	American Journals Publishing
dc.relation	https://americanjournal.org/index.php/ajtas/article/view/678/3258
dc.rights	https://creativecommons.org/licenses/by-nc/4.0
dc.source	American Journal of Technology and Applied Sciences; Vol. 30 (2024); 57-61
dc.source	2832-1766
dc.subject	Generative AI, cybersecurity, GAN, LLM, intrusion detection, adversarial attacks, zero-day.
dc.title	APPLICATION OF GENERATIVE AI IN CYBERSECURITY DETECTION AND PREVENTION OF NEXT-GENERATION ATTACKS
dc.type	info:eu-repo/semantics/article
dc.type	info:eu-repo/semantics/publishedVersion
dc.type	Peer-reviewed Article

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