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

TL;DR

MalDataGen is a flexible, open-source framework that uses advanced deep learning models to generate high-quality synthetic tabular data, improving malware detection performance and utility.

Contribution

It introduces a modular deep learning-based framework for synthetic data generation tailored for malware detection, outperforming existing benchmarks.

Findings

MalDataGen outperforms SDV in utility metrics.

Dual validation confirms data fidelity.

Framework integrates seamlessly into detection pipelines.

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.