Kol-4-Gen: Stacked Kolmogorov-Arnold and Generative Adversarial Networks for Malware Binary Classification Through Visual Analysis
Anurag Dutta, Satya Prakash Nayak, Ruchira Naskar, Rajat Subhra Chakraborty · IEEE Embedded Systems Letters · 2025
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 multiclass malware classifier, in this letter, 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 standardMalimg(grayscale, imbalanced, 25 classes),Malevis(RGB, balanced, 26 classes), and the miniatureVirus-MNIST(grayscale, imbalanced, 10 classes) datasets, outperform state-of-the-art (S-O-T-A) models, achieving$\approx 99.36\%$,$\approx 95.44\%$, and$\approx 92.12\%$validation accuracy, respectively.