3 papers
cs.CR2025
Synthetic Data: AI's New Weapon Against Android Malware
Angelo Gaspar Diniz Nogueira, Kayua Oleques Paim, Hendrio Bragança +2
The ever-increasing number of Android devices and the accelerated evolution of malware, reaching over 35 million samples by 2024, highlight the critical importance of effective det…
cs.CR2025
MalDataGen: A Modular Framework for Synthetic Tabular Data Generation in Malware Detection
Kayua Oleques Paim, Angelo Gaspar Diniz Nogueira, Diego Kreutz +2
High-quality data scarcity hinders malware detection, limiting ML performance. We introduce MalDataGen, an open-source modular framework for generating high-fidelity synthetic tabu…
cs.CR2025
Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance
Joner Assolin, Gabriel Canto, Diego Kreutz +4
Malware detection in Android systems requires both cybersecurity expertise and machine learning (ML) techniques. Automated Machine Learning (AutoML) has emerged as an approach to s…