MalDataGen: A Modular Framework for Synthetic Tabular Data Generation in Malware Detection
Cryptography and Security
2025-11-04 v1 Artificial Intelligence
Machine Learning
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.
Keywords
Cite
@article{arxiv.2511.00361,
title = {MalDataGen: A Modular Framework for Synthetic Tabular Data Generation in Malware Detection},
author = {Kayua Oleques Paim and Angelo Gaspar Diniz Nogueira and Diego Kreutz and Weverton Cordeiro and Rodrigo Brandao Mansilha},
journal= {arXiv preprint arXiv:2511.00361},
year = {2025}
}
Comments
10 pages, 6 figures, 2 tables. Published at the Brazilian Symposium on Cybersecurity (SBSeg 2025)