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

arXiv: https://arxiv.org/abs/2511.16373 · HTML: https://mlsanity.com/paper/2511.16373/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 15 references that are in the map.
- First posted on arXiv 20 Nov 2025.

## Builds on (most cited first, 3 of 3)

- [TabSynDex: A Universal Metric for Robust Evaluation of Synthetic Tabular Data](https://mlsanity.com/paper/2207.05295/) (2022)
- [Synthetic Data in AI: Challenges, Applications, and Ethical Implications](https://mlsanity.com/paper/2401.01629/) (2024)
- [MalDataGen: A Modular Framework for Synthetic Tabular Data Generation in Malware Detection](https://mlsanity.com/paper/2511.00361/) (2025)

## Abstract (arXiv)

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.