MadDroid: characterizing and detecting devious ad contents for Android apps

Tianming Liu, Haoyu Wang, Li Li, Xiapu Luo, Feng Dong, Yao Guo, Liu Wang, Tegawendé F. Bissyandé, Jacques Klein

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

13 Citations (Scopus)


Advertisement drives the economy of the mobile app ecosystem. As a key component in the mobile ad business model, mobile ad content has been overlooked by the research community, which poses a number of threats, e.g., propagating malware and undesirable contents. To understand the practice of these devious ad behaviors, we perform a large-scale study on the app contents harvested through automated app testing. In this work, we first provide a comprehensive categorization of devious ad contents, including five kinds of behaviors belonging to two categories: ad loading content and ad clicking content. Then, we propose MadDroid, a framework for automated detection of devious ad contents. MadDroid leverages an automated app testing framework with a sophisticated ad view exploration strategy for effectively collecting ad-related network traffic and subsequently extracting ad contents. We then integrate dedicated approaches into the framework to identify devious ad contents. We have applied MadDroid to 40,000 Android apps and found that roughly 6% of apps deliver devious ad contents, e.g., distributing malicious apps that cannot be downloaded via traditional app markets. Experiment results indicate that devious ad contents are prevalent, suggesting that our community should invest more effort into the detection and mitigation of devious ads towards building a trustworthy mobile advertising ecosystem.

Original languageEnglish
Title of host publicationProceedings of the World Wide Web Conference WWW 2020
EditorsTie-Yan Liu, Maarten van Steen
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages12
ISBN (Electronic)9781450370233
Publication statusPublished - 2020
EventInternational World Wide Web Conference 2020 - Taipei, Taiwan
Duration: 20 Apr 202024 Apr 2020
Conference number: 29th (Proceedings) (Website)


ConferenceInternational World Wide Web Conference 2020
Abbreviated titleWWW 2020
Internet address


  • ad fraud
  • Android app
  • malware
  • mobile advertising

Cite this