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KOL-4-GEN: Stacked Kolmogorov-Arnold and Generative Adversarial Networks for Malware Binary Classification through Visual Analysis | IEEE Journals & Magazine | IEEE Xplore

KOL-4-GEN: Stacked Kolmogorov-Arnold and Generative Adversarial Networks for Malware Binary Classification through Visual Analysis


Abstract:

Malware identification and classification is an active field of research. A popular approach is to classify malware binaries using visual analysis, by converting malware ...Show More

Abstract:

Malware identification and classification is an active field of research. A popular approach is to classify malware binaries using visual analysis, by converting malware binaries into images, which reveal different class-specific patterns. To develop a highly accurate multi-class malware classifier, in this paper, we propose KOL-4-GEN, a set of four novel deep learning models based on the Kolmogorov-Arnold Network (KAN) with trainable activation functions, and a Generative Adversarial Network (GAN) to address data imbalance (if applicable) during training. Our models, tested on the standard Malimg (grayscale, imbalanced, 25 classes), Malevis (RGB, balanced, 26 classes), and the miniature Virus-MNIST (grayscale, imbalanced, 10 classes) datasets, outperform state-of-the-art (S-O-T-A) models, achieving ≈ 99.36%, ≈ 95.44%, and ≈ 92.12% validation accuracy, respectively.
Published in: IEEE Embedded Systems Letters ( Early Access )
Page(s): 1 - 1
Date of Publication: 23 January 2025

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