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arXiv · 2511.16373

Reducing Instability in Synthetic Data Evaluation with a Super-Metric in MalDataGen

Anna Luiza Gomes da Silva·Diego Kreutz·Angelo Diniz·Rodrigo Mansilha·Celso Nobre da Fonseca
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Abstract

Evaluating the quality of synthetic data remains a persistent challenge in the Android malware domain due to instability and the lack of standardization among existing metrics. This work integrates into MalDataGen a Super-Metric that aggregates eight metrics across four fidelity dimensions, producing a single weighted score. Experiments involving ten generative models and five balanced datasets demonstrate that the Super-Metric is more stable and consistent than traditional metrics, exhibiting stronger correlations with the actual performance of classifiers.

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Anna Luiza Gomes da Silva, Diego Kreutz, Angelo Diniz, Rodrigo Mansilha, Celso Nobre da Fonseca. 2025-11-20. Reducing Instability in Synthetic Data Evaluation with a Super-Metric in MalDataGen. https://arxiv.org/abs/2511.16373

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