RacketStore: measurements of ASO deception in Google play via mobile and app usage
RacketStore: measurements of ASO deception in Google play via mobile and app usage
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DOI：
10.1145/3487552.3487837
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发表时间：
2021-11
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影响因子：
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通讯作者：
Nestor Hernandez;Ruben Recabarren;Bogdan Carbunar;Syed Ishtiaque Ahmed
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作者：
Nestor Hernandez;Ruben Recabarren;Bogdan Carbunar;Syed Ishtiaque Ahmed
在线应用搜索优化(ASO)平台为付费应用开发商提供批量安装和虚假评论，以欺诈性地提高他们在应用商店的搜索排名，这些平台被证明使用了多样化和复杂的策略，成功地避开了最先进的检测方法。在本文中，我们介绍了RacketStore，这是一个从参与ASO提供商和普通用户的Android设备收集数据的平台，内容是他们与他们从Google Play Store安装的应用程序的互动。我们提供了对RacketStore在由ASO提供商和普通用户控制的803台独特设备上安装的943次研究的测量结果，其中包括从这些设备收集的58,362,249个数据快照，安装在这些设备上的12,341个应用程序，以及他们的110,511,637条Google Play评论。我们发现，ASO提供商和普通用户在他们的设备上注册的用户账户的数量和类型、他们审查的应用程序的数量以及应用程序的安装时间和他们的审查时间之间的间隔方面存在显著差异。我们利用这些洞察力引入了对应用程序和设备的使用进行建模的功能，并展示了它们可以训练监督学习算法来检测付费应用程序安装和虚假评论，F1度量为99.72%(AUC高于0.99)，并检测由ASO提供商控制的设备，F1度量为95.29%(AUC=0.95)。我们讨论了与我们的分类器逃避检测相关的成本，以及应用商店使用我们的方法检测ASO与隐私合作的可能性。
Online app search optimization (ASO) platforms that provide bulk installs and fake reviews for paying app developers in order to fraudulently boost their search rank in app stores, were shown to employ diverse and complex strategies that successfully evade state-of-the-art detection methods. In this paper we introduce RacketStore, a platform to collect data from Android devices of participating ASO providers and regular users, on their interactions with apps which they install from the Google Play Store. We present measurements from a study of 943 installs of RacketStore on 803 unique devices controlled by ASO providers and regular users, that consists of 58,362,249 data snapshots collected from these devices, the 12,341 apps installed on them and their 110,511,637 Google Play reviews. We reveal significant differences between ASO providers and regular users in terms of the number and types of user accounts registered on their devices, the number of apps they review, and the intervals between the installation times of apps and their review times. We leverage these insights to introduce features that model the usage of apps and devices, and show that they can train supervised learning algorithms to detect paid app installs and fake reviews with an F1-measure of 99.72% (AUC above 0.99), and detect devices controlled by ASO providers with an F1-measure of 95.29% (AUC = 0.95). We discuss the costs associated with evading detection by our classifiers and also the potential for app stores to use our approach to detect ASO work with privacy.