MalDataGen: A Modular Framework for Synthetic Tabular Data Generation in Malware Detection - Paim, Kayuã Oleques; Nogueira, Angelo Gaspar Diniz; Kreutz, Diego; Cordeiro, Weverton; Mansilha, Rodrigo Brandão (Anais Estendidos do Simpósio Brasileiro de Cibersegurança (SBSeg), 2025)
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.