Poster: Synthetic Malware Generation using Generative Models
Publication Date
1-1-2025
Document Type
Conference Proceeding
Publication Title
2025 Silicon Valley Cybersecurity Conference Svcc 2025
DOI
10.1109/SVCC65277.2025.11133626
Abstract
Malware poses significant challenges to cybersecurity, exacerbated by the scarcity of high-quality datasets. This paper explores the use of Diffusion and Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP) to generate synthetic malware samples as opcode sequences. The synthetic data, validated through classification metrics, demonstrates improved malware detection accuracy across families, particularly in addressing zero-day attacks. Results show that Diffusion achieves up to 99.6% accuracy in multi-class classification, outperforming WGAN-GP, which reaches up to 96.1%. Incorporating synthetic samples improves detection accuracy for rare malware families by up to 100%, underscoring the potential of generative models in enhancing malware detection.
Funding Number
2244597
Funding Sponsor
National Science Foundation
Keywords
Diffusion, Generative Models, Machine Learning, Malware, WGAN-GP
Department
Computer Science; Computer Engineering
Recommended Citation
Tiffany Bao, Kylie Trousil, Quang Duy Tran, Fabio Di Troia, and Younghee Park. "Poster: Synthetic Malware Generation using Generative Models" 2025 Silicon Valley Cybersecurity Conference Svcc 2025 (2025). https://doi.org/10.1109/SVCC65277.2025.11133626
