Automated Static Code Analysis for Classifying Android Applications Using Machine Learning

A. Shabtai, Yuval Fledel, Y. Elovici
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引用次数: 179

Abstract

In this paper we apply Machine Learning (ML) techniques on static features that are extracted from Android's application files for the classification of the files. Features are extracted from Android’s Java byte-code (i.e.,.dex files) and other file types such as XML-files. Our evaluation focused on classifying two types of Android applications: tools and games. Successful differentiation between games and tools is expected to provide positive indication about the ability of such methods to learn and model Android benign applications and potentially detect malware files. The results of an evaluation, performed using a test collection comprising 2,285 Android. apk files, indicate that features, extracted statically from. apk files, coupled with ML classification algorithms can provide good indication about the nature of an Android application without running the application, and may assist in detecting malicious applications. This method can be used for rapid examination of Android. apks and informing of suspicious applications.
使用机器学习分类Android应用程序的自动静态代码分析
在本文中,我们将机器学习(ML)技术应用于从Android应用程序文件中提取的静态特征上,以便对文件进行分类。功能是从Android的Java字节码(即。dex文件)和其他文件类型(如xml文件)中提取的。我们的评估主要针对两类Android应用:工具和游戏。游戏和工具之间的成功区分有望为这些方法学习和模拟Android良性应用以及潜在检测恶意文件的能力提供积极的暗示。评估结果,使用包含2,285个Android的测试集合执行。Apk文件,表示静态提取的功能。apk文件,再加上ML分类算法可以在不运行应用程序的情况下提供关于Android应用程序性质的良好指示,并且可以帮助检测恶意应用程序。该方法可用于Android的快速检测。Apks和通知可疑的应用程序。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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