Exploiting Vision Transformer and Ensemble Learning for Advanced Malware Classification

ABSTRACT Malware remains a significant concern for modern digital systems, increasing the need for reliable and scalable detection methods. This work proposes an ensemble method that combines a random forest (RF) with a vision transformer (ViT). The approach exploits complementary feature spaces, in...

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Autori principali:	Fadi Makarem, Rida El Chall, Abed Ellatif Samhat, Khouloud Samrouth, Nader Bakir
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	Wiley 2026-01-01
Serie:	Engineering Reports
Soggetti:	ensemble learning multi‐class malware classification random forest ResNet50 vision transformer (ViT)
Accesso online:	https://doi.org/10.1002/eng2.70558
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Exploiting Vision Transformer and Ensemble Learning for Advanced Malware Classification

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