MalDataGen: A Modular Framework for Synthetic Tabular Data Generation in Malware Detection
By: Kayua Oleques Paim , Angelo Gaspar Diniz Nogueira , Diego Kreutz and more
Potential Business Impact:
Creates fake computer virus data to train defenses.
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.
Similar Papers
Synthetic Data: AI's New Weapon Against Android Malware
Cryptography and Security
Creates fake malware to train phone security.
MalGEN: A Generative Agent Framework for Modeling Malicious Software in Cybersecurity
Cryptography and Security
Creates fake computer viruses to test defenses.
Reducing Instability in Synthetic Data Evaluation with a Super-Metric in MalDataGen
Artificial Intelligence
Makes fake virus data better for training phone security.