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arXiv · 2511.00361

MalDataGen: A Modular Framework for Synthetic Tabular Data Generation in Malware Detection

Kayua Oleques Paim·Angelo Gaspar Diniz Nogueira·Diego Kreutz·Weverton Cordeiro·Rodrigo Brandao Mansilha
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Abstract

High-quality data scarcity hinders malware detection, limiting ML performance. We introduce MalDataGen, an open-source modular framework for generating high-fidelity synthetic tabular data using modular deep learning models (e.g., WGAN-GP, VQ-VAE). Evaluated via dual validation (TR-TS/TS-TR), seven classifiers, and utility metrics, MalDataGen outperforms benchmarks like SDV while preserving data utility. Its flexible design enables seamless integration into detection pipelines, offering a practical solution for cybersecurity applications.

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Kayua Oleques Paim, Angelo Gaspar Diniz Nogueira, Diego Kreutz, Weverton Cordeiro, Rodrigo Brandao Mansilha. 2025-11-01. MalDataGen: A Modular Framework for Synthetic Tabular Data Generation in Malware Detection. https://doi.org/10.5753/sbseg_estendido.2025.12113

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