Google Play上的灰色软件研究

Benjamin Andow, Adwait Nadkarni, Blake Bassett, W. Enck, Tao Xie
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引用次数: 31

摘要

虽然已经有各种各样的研究对Android恶意软件进行识别和分类,但对处于灰色地带的更广泛类别的应用程序的讨论却很有限。由于操作系统属性的不同,移动灰色软件不同于PC灰色软件。由于移动灰软件的主观性,仅通过程序分析难以识别移动灰软件。相反,我们假设用文本分析增强分析可以有效地减少人工在分类灰色软件时的工作量。在本文中,我们设计并实现了七种主要类型的灰色软件的启发式算法。然后,我们使用这些启发式方法在谷歌Play的大量应用程序上模拟灰色软件分类。然后,我们提出了我们的实证研究结果,证明了一个明确的灰色软件问题。在此过程中,我们展示了即使是相对简单的启发式方法也可以快速识别出以不良方式利用用户的应用程序。
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A Study of Grayware on Google Play
While there have been various studies identifying and classifying Android malware, there is limited discussion of the broader class of apps that fall in a gray area. Mobile grayware is distinct from PC grayware due to differences in operating system properties. Due to mobile grayware's subjective nature, it is difficult to identify mobile grayware via program analysis alone. Instead, we hypothesize enhancing analysis with text analytics can effectively reduce human effort when triaging grayware. In this paper, we design and implement heuristics for seven main categories of grayware. We then use these heuristics to simulate grayware triage on a large set of apps from Google Play. We then present the results of our empirical study, demonstrating a clear problem of grayware. In doing so, we show how even relatively simple heuristics can quickly triage apps that take advantage of users in an undesirable way.
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