Synthetic Malware Using Deep Variational Autoencoders and Generative Adversarial Networks

Choi, Aaron; Giang, Albert; Jumani, Sajit; Luong, David; Troia, Fabio Di

Synthetic Malware Using Deep Variational Autoencoders and Generative Adversarial Networks

Choi, Aaron and Giang, Albert and Jumani, Sajit and Luong, David and Troia, Fabio Di (2024) Synthetic Malware Using Deep Variational Autoencoders and Generative Adversarial Networks. EAI Endorsed Transactions on Internet of Things.

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Abstract

The effectiveness of detecting malicious files heavily relies on the quality of the training dataset, particularly its size and authenticity. However, the lack of high-quality training data remains one of the biggest challenges in achieving widespread adoption of malware detection by trained machine

Item Type:	Article
Date Deposited:	04 Mar 2026 18:15
Last Modified:	11 Apr 2026 00:09
URI:	http://eprints.eai.eu/id/eprint/51793

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