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

Comments

© 2025 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Department

Computer Engineering; Computer Science

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