用综合指标提升安卓恶意软件合成数据评估稳定性
Reducing Instability in Synthetic Data Evaluation with a Super-Metric in MalDataGen
- 设计融合八项指标的超级评分体系，覆盖四类真实度维度
- 在十种生成模型、五组数据集上验证，评分更稳定且与分类器性能相关性更强
- 适合关注合成数据质量评估的研究者和安全系统开发者
安卓恶意软件领域中，合成数据质量评估长期面临不稳定和缺乏标准的问题。本文将一种超级指标整合进MalDataGen，该指标在四个真实度维度上聚合八项指标，生成单一加权得分。实验基于十种生成模型和五组平衡数据集进行，结果表明，该超级指标比传统指标更稳定、更一致，与分类器实际性能具有更强的相关性。
原文摘要 · Abstract (English)
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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