理解和减轻恶意软件分类中的标签偏差:一项实证研究

Jia Yan, Xiangkun Jia, Lingyun Ying, Purui Su
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引用次数: 1

摘要

机器学习技术在恶意软件分类中有很好的应用前景,但在标注过程中存在被忽视的标签偏差问题,从而降低了实际应用中的性能。为了了解标签偏差问题和现有解决方案,我们基于VirusTotal中的两个便携式可执行(PE)恶意软件样本数据集(即开放源代码的BODMAS样本52,793个,新收集的MAIN数据集样本153,811个)和67个反病毒引擎进行了实证研究。我们首先展示了标签偏差问题的两种方式,包括混沌命名规则和标注不一致。然后我们给出了两种解决方案(即选择一个信誉良好的AV引擎和基于多数投票的聚合多个标签)的效果,发现它们面临着特征偏好和引擎独立性的问题。最后,我们提出了一些改进建议,使F1分数提高了7.79%(即从84.83%提高到92.62%)。数据集将是开源的,以供进一步研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Understanding and Mitigating Label Bias in Malware Classification: An Empirical Study
Machine learning techniques are promising for malware classification, but there is a neglected problem of label bias in the annotation process which decreases the performance in practice. To understand the label bias problems and existing solutions, we conduct an empirical study based on two Portable Executable (PE) malware sample datasets (i.e., open-sourced BODMAS with 52,793 samples and a new collected MAIN dataset of 153,811 samples), and 67 anti-virus engines in VirusTotal. We first show the two ways of label bias problems, including chaotic naming rules and annotation inconsistency. Then we present the effects of two solutions (i.e., electing one reputable AV engine and aggregating multiple labels based on majority voting) and find they face the problems of feature preference and engine independence. Finally, we propose some recommendations for improvements and get a 7.79% increase in the F1 score (i.e., from 84.83% to 92.62%). The dataset will be open-source for further study.
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