Recognizing malware before its installation plays a crucial role in keeping an android device safe. In this paper we describe a supervised method that is able to analyse multiple information (e.g. permissions, api calls and network addresses) that can be retrieved through a broad static analysis of android applications. In particular, we propose a novel multi-view machine learning approach to malware detection, which couples knowledge extracted via both clustering and classification. In an assessment, we evaluate the effectiveness of the proposed method using benchmark Android applications and established machine learning metrics.

中文翻译：

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聚类辅助多视图分类：Android 恶意软件检测案例研究

在安装之前识别恶意软件在保证安卓设备安全方面起着至关重要的作用。在本文中，我们描述了一种能够分析多种信息（例如权限、api 调用和网络地址）的监督方法，这些信息可以通过对 Android 应用程序的广泛静态分析来检索。特别是，我们提出了一种新颖的多视图机器学习方法来检测恶意软件，它将通过聚类和分类提取的知识结合起来。在评估中，我们使用基准 Android 应用程序和已建立的机器学习指标来评估所提出方法的有效性。