Embedding-Driven Synthetic Malware Generation with Autoencoders and Cluster-Tangent Diffusion

Malware has become increasingly sophisticated over the years, with zero-day attacks emerging at an alarming pace. Effective detection and analysis demand real malware samples, which are expensive and skill-dependent to extract. As a result, generating high quality synthetic samples from scarce data...

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Hlavní autoři:	Gunnika Kapoor, Sathvika Nadipalli, Fabio Di Troia
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	MDPI AG 2025-11-01
Edice:	Applied Sciences
Témata:	synthetic malware generation diffusion manifold diffusion Natural Language Processing graph embeddings autoencoder
On-line přístup:	https://www.mdpi.com/2076-3417/15/21/11791
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