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Bibliographic Details
Main Authors: Paim, Kayua Oleques, Nogueira, Angelo Gaspar Diniz, Kreutz, Diego, Cordeiro, Weverton, Mansilha, Rodrigo Brandao
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2511.00361
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author Paim, Kayua Oleques
Nogueira, Angelo Gaspar Diniz
Kreutz, Diego
Cordeiro, Weverton
Mansilha, Rodrigo Brandao
author_facet Paim, Kayua Oleques
Nogueira, Angelo Gaspar Diniz
Kreutz, Diego
Cordeiro, Weverton
Mansilha, Rodrigo Brandao
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2511_00361
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle MalDataGen: A Modular Framework for Synthetic Tabular Data Generation in Malware Detection
Paim, Kayua Oleques
Nogueira, Angelo Gaspar Diniz
Kreutz, Diego
Cordeiro, Weverton
Mansilha, Rodrigo Brandao
Cryptography and Security
Artificial Intelligence
Machine Learning
I.2
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.
title MalDataGen: A Modular Framework for Synthetic Tabular Data Generation in Malware Detection
topic Cryptography and Security
Artificial Intelligence
Machine Learning
I.2
url https://arxiv.org/abs/2511.00361