{"ID":2839090,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.16373","arxiv_id":"2511.16373","title":"Reducing Instability in Synthetic Data Evaluation with a Super-Metric in MalDataGen","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.","short_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...","url_abs":"https://arxiv.org/abs/2511.16373","url_pdf":"https://arxiv.org/pdf/2511.16373v1","authors":"[\"Anna Luiza Gomes da Silva\",\"Diego Kreutz\",\"Angelo Diniz\",\"Rodrigo Mansilha\",\"Celso Nobre da Fonseca\"]","published":"2025-11-20T13:55:39Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"cs.LG\"]","methods":"[]","has_code":false}