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 MoreMetadata
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 )