DECAF: Detecting and Characterizing Ad Fraud in Mobile Apps
DECAF: Detecting and Characterizing Ad Fraud in Mobile Apps
复制标题
DOI：
--
复制
发表时间：
2014-04
影响因子：
4.3
通讯作者：
B. Liu;Suman Nath;R. Govindan;Jie Liu
中科院分区：
文献类型：
--
作者：
B. Liu;Suman Nath;R. Govindan;Jie Liu
移动的应用程序的广告网络需要检查其广告的视觉布局，以检测某些类型的位置欺诈。手动执行此操作容易出错，并且无法扩展到当今应用程序商店的大小。在本文中，我们设计了一个系统称为DECAF自动发现各种配售欺诈可扩展和有效的。DECAF使用自动化应用导航，并优化在有限的时间内扫描大量的视觉元素。它还包括一个框架，用于有效地检测应用程序中的广告是否违反了管理广告放置和显示的可扩展规则集。我们已经为基于Windows的移动的平台实施了DECAF，并将其应用于1，150个平板电脑应用程序和50，000个手机应用程序，以描述广告欺诈的流行程度。微软的广告欺诈团队已经使用了DECAF，并帮助发现了许多广告欺诈的案例。
Ad networks for mobile apps require inspection of the visual layout of their ads to detect certain types of placement frauds. Doing this manually is error prone, and does not scale to the sizes of today's app stores. In this paper, we design a system called DECAF to automatically discover various placement frauds scalably and effectively. DECAF uses automated app navigation, together with optimizations to scan through a large number of visual elements within a limited time. It also includes a framework for efficiently detecting whether ads within an app violate an extensible set of rules that govern ad placement and display. We have implemented DECAF for Windows-based mobile platforms, and applied it to 1,150 tablet apps and 50,000 phone apps in order to characterize the prevalence of ad frauds. DECAF has been used by the ad fraud team in Microsoft and has helped find many instances of ad frauds.