N-opcode analysis for android malware classification and categorization

Boojoong Kang, S. Yerima, K. Mclaughlin, S. Sezer
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引用次数: 72

Abstract

Malware detection is a growing problem particularly on the Android mobile platform due to its increasing popularity and accessibility to numerous third party app markets. This has also been made worse by the increasingly sophisticated detection avoidance techniques employed by emerging malware families. This calls for more effective techniques for detection and classification of Android malware. Hence, in this paper we present an n-opcode analysis based approach that utilizes machine learning to classify and categorize Android malware. This approach enables automated feature discovery that eliminates the need for applying expert or domain knowledge to define the needed features. Our experiments on 2520 samples that were performed using up to 10-gram opcode features showed that an f-measure of 98% is achievable using this approach.
android恶意软件分类与分类的n -操作码分析
恶意软件检测是一个日益严重的问题,特别是在Android移动平台上,因为它越来越受欢迎,并可访问众多第三方应用程序市场。新兴恶意软件家族采用的越来越复杂的检测避免技术也使情况变得更糟。这就需要更有效的检测和分类Android恶意软件的技术。因此,在本文中,我们提出了一种基于n操作码分析的方法,该方法利用机器学习对Android恶意软件进行分类和分类。这种方法能够自动发现特性,从而消除了应用专家或领域知识来定义所需特性的需要。我们使用高达10克的操作码特征对2520个样本进行的实验表明,使用这种方法可以实现98%的f测量。
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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