Early detection of spam mobile apps

Suranga Seneviratne, Aruna Seneviratne, Mohamed Ali Kaafar, Anirban Mahanti, Prasant Mohapatra

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

22 Citations (Scopus)


Increased popularity of smartphones has attracted a large number of developers to various smartphone platforms. As a result, app markets are also populated with spam apps, which reduce the users' quality of experience and increase the workload of app market operators. Apps can be "spammy" in multiple ways including not having a specific functionality, unrelated app description or unrelated keywords and publishing similar apps several times and across diverse categories. Market operators maintain anti-spam policies and apps are removed through continuous human intervention. Through a systematic crawl of a popular app market and by identifying a set of removed apps, we propose a method to detect spam apps solely using app metadata available at the time of publication. We first propose a methodology to manually label a sample of removed apps, according to a set of checkpoint heuristics that reveal the reasons behind removal. This analysis suggests that approximately 35% of the apps being removed are very likely to be spam apps. We then map the identified heuristics to several quantifiable features and show how distinguishing these features are for spam apps. Finally, we build an Adaptive Boost classifier for early identification of spam apps using only the metadata of the apps. Our classifier achieves an accuracy over 95% with precision varying between 85%-95% and recall varying between 38%-98%. By applying the classifier on a set of apps present at the app market during our crawl, we estimate that at least 2.7% of them are spam apps.

Original languageEnglish
Title of host publicationWWW 2015
Subtitle of host publicationProceedings of the 24th International Conference on World Wide Web
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery
Number of pages11
ISBN (Electronic)9781450334693
Publication statusPublished - 18 May 2015
Externally publishedYes
Event24th International Conference on World Wide Web, WWW 2015 - Florence, Italy
Duration: 18 May 201522 May 2015


Conference24th International Conference on World Wide Web, WWW 2015


  • Android
  • Mobile Apps
  • Spam
  • Spam Apps


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