Deep Defense Against Mal-Doc: Utilizing Transformer and SeqGAN for Detecting and Classifying Document Type Malware

Deep Defense Against Mal-Doc: Utilizing Transformer and SeqGAN for Detecting and Classifying Document Type Malware

The prevalence of non-executable malware is on the rise, presenting a major threat to users, including major public institutions and corporations. While extensive research has been conducted on detecting malware threats, there is a noticeable gap in studying document-type malware compared with execu...

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Bibliographic Details
Main Authors:	Gati Lother Martin, Sang-Min Lee, Jong-Hyun Kim, Young-Seob Jeong, Ah Reum Kang, Jiyoung Woo
Format:	Article
Language:	English
Published:	MDPI AG 2025-03-01
Series:	Applied Sciences
Subjects:	document-type malware Transformer model generative adversarial network deep learning script codes
Online Access:	https://www.mdpi.com/2076-3417/15/6/2978
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