MAdFraud: investigating ad fraud in android applications

Abstract

Many Android applications are distributed for free but are supported by advertisements. Ad libraries embedded in the app fetch content from the ad provider and display it on the app's user interface. The ad provider pays the developer for the ads displayed to the user and ads clicked by the user. A major threat to this ecosystem is ad fraud, where a miscreant's code fetches ads without displaying them to the user or "clicks" on ads automatically. Ad fraud has been extensively studied in the context of web advertising but has gone largely unstudied in the context of mobile advertising. We take the first step to study mobile ad fraud perpetrated by Android apps. We identify two fraudulent ad behaviors in apps: 1) requesting ads while the app is in the background, and 2) clicking on ads without user interaction. Based on these observations, we developed an analysis tool, MAdFraud, which automatically runs many apps simultaneously in emulators to trigger and expose ad fraud. Since the formats of ad impressions and clicks vary widely between different ad providers, we develop a novel approach for automatically identifying ad impressions and clicks in three steps: building HTTP request trees, identifying ad request pages using machine learning, and detecting clicks in HTTP request trees using heuristics. We apply our methodology and tool to two datasets: 1) 130,339 apps crawled from 19 Android markets including Play and many third-party markets, and 2) 35,087 apps that likely contain malware provided by a security company. From analyzing these datasets, we find that about 30% of apps with ads make ad requests while in running in the background. In addition, we find 27 apps which generate clicks without user interaction. We find that the click fraud apps attempt to remain stealthy when fabricating ad traffic by only periodically sending clicks and changing which ad provider is being targeted between installations.