Large-scale machine learning-based malware detection: confronting the "10-fold cross validation" scheme with reality

Kevin Allix, Tegawendé F. Bissyandé, Quentin Jérôme, Jacques Klein, R. State, Yves Le Traon
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引用次数: 22

Abstract

To address the issue of malware detection, researchers have recently started to investigate the capabilities of machine-learning techniques for proposing effective approaches. Several promising results were recorded in the literature, many approaches being assessed with the common "10-Fold cross validation" scheme. This paper revisits the purpose of malware detection to discuss the adequacy of the "10-Fold" scheme for validating techniques that may not perform well in reality. To this end, we have devised several Machine Learning classifiers that rely on a novel set of features built from applications' CFGs. We use a sizeable dataset of over 50,000 Android applications collected from sources where state-of-the art approaches have selected their data. We show that our approach outperforms existing machine learning-based approaches. However, this high performance on usual-size datasets does not translate in high performance in the wild.
基于大规模机器学习的恶意软件检测:面对现实的“10倍交叉验证”方案
为了解决恶意软件检测问题,研究人员最近开始研究机器学习技术的能力,以提出有效的方法。文献中记录了一些有希望的结果,许多方法正在用常见的“10倍交叉验证”方案进行评估。本文回顾了恶意软件检测的目的,讨论了“10-Fold”方案对于验证在现实中可能表现不佳的技术的充分性。为此,我们设计了几个机器学习分类器,这些分类器依赖于从应用程序的cfg构建的一组新颖的特征。我们使用了超过50,000个Android应用程序的庞大数据集,这些应用程序是从最先进的方法选择数据的来源收集的。我们表明,我们的方法优于现有的基于机器学习的方法。然而,在通常大小的数据集上的高性能并不能转化为野外的高性能。
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