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arXiv 1804.09882cs.CRcs.LGcs.NE
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一种用于检测移动假冒应用的神经嵌入方法

A Neural Embeddings Approach for Detecting Mobile Counterfeit Apps

Data61-CSIRO（澳大利亚联邦科学与工业研究组织数据61中心）；University of Moratuwa（莫拉图瓦大学）；University of Sydney（悉尼大学）

Jathushan Rajasegaran, Suranga Seneviratne, Guillaume Jourjon

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摘要：

假冒应用程序模仿现有的流行应用程序，试图误导用户安装它们，以收集个人信息，传播恶意软件或仅仅是为了增加广告收入。许多假冒产品一旦安装就可以识别出来，但是即使是精通技术的用户也可能在安装之前很难检测到它们，因为应用程序图标和描述可能与原始应用程序非常相似。为此，本文提出使用最先进的卷积神经网络（CNN）生成的神经嵌入来测量图像之间的相似性。我们的研究结果表明，对于伪造检测问题，使用由CNN滤波器响应的Gram矩阵给出的风格嵌入的新方法优于内容嵌入和SIFT特征等基线方法。我们表明，进一步的性能提高，可以通过结合风格嵌入与内容嵌入。我们对Google Play商店中约120万个应用程序进行了分析，并确定了前1，000个应用程序的一组潜在假冒产品。在保守的假设下，我们能够在一组6，880个应用程序中找到139个包含恶意软件的应用程序，这些应用程序与Google Play商店中的前1，000个应用程序之一具有高度的视觉相似性。

英文摘要：

Counterfeit apps impersonate existing popular apps in attempts to misguide users to install them for various reasons such as collecting personal information, spreading malware, or simply to increase their advertisement revenue. Many counterfeits can be identified once installed, however even a tech-savvy user may struggle to detect them before installation as app icons and descriptions can be quite similar to the original app. To this end, this paper proposes to use neural embeddings generated by state-of-the-art convolutional neural networks (CNNs) to measure the similarity between images. Our results show that for the problem of counterfeit detection a novel approach of using style embeddings given by the Gram matrix of CNN filter responses outperforms baseline methods such as content embeddings and SIFT features. We show that further performance increases can be achieved by combining style embeddings with content embeddings. We present an analysis of approximately 1.2 million apps from Google Play Store and identify a set of potential counterfeits for top-1,000 apps. Under a conservative assumption, we were able to find 139 apps that contain malware in a set of 6,880 apps that showed high visual similarity to one of the top-1,000 apps in Google Play Store.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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