Publication Date
1-1-2025
Document Type
Article
Publication Title
IEEE Access
Volume
13
DOI
10.1109/ACCESS.2025.3556704
First Page
59725
Last Page
59736
Abstract
Malware attacks have a significant negative impact on organizations of varied scales in the field of cybersecurity. Recently, malware researchers have increasingly turned to machine learning techniques to combat sophisticated obfuscation methods used in malware. However, collecting a diverse set of malware samples with various obfuscation techniques is challenging and often takes years, especially for newly developed malware. This issue is further compounded by a well-known limitation of machine learning models: their poor performance when training data is scarce. In this paper, we propose a new system for generating synthetic malware samples to augment imbalanced malware dataset. Our approach decomposes malware binary samples into mnemonic opcode sequences, leveraging natural language processing to extract contextual meaning behind malware opcode features to aid the learning of generative AI (GenAI) employed in this paper, Generative Adversarial Networks (GAN), Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP), and a modified Diffusion model. The experiment results show that augmenting training data with Diffusion-based synthetic data significantly improves classification performance for minor classes by up to 60% on average. This enhancement ultimately leads to an overall malware classification performance of 96%, an 8% improvement. These findings demonstrate the high quality and fidelity of the synthetic data, its robustness, and its potential applications in malware analysis. Specifically, synthetic malware data proves effective in improving the classification of minor malware classes and detection rates, even though the size of known malware data is significantly small.
Keywords
data augmentation, Diffusion, GAN, generative AI, imbalanced datasets, machine learning, malware, natural language processing
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Department
Computer Science
Recommended Citation
Tiffany Bao, Kylie Trousil, Quang Duy Tran, Fabio Di Troia, and Younghee Park. "Generating Synthetic Malware Samples Using Generative AI" IEEE Access (2025): 59725-59736. https://doi.org/10.1109/ACCESS.2025.3556704
