NDLI: Early Detection of Spam Mobile Apps

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Early Detection of Spam Mobile Apps

Content Provider	ACM Digital Library
Author	Kaafar, Mohamed Ali Mahanti, Anirban Mohapatra, Prasant Seneviratne, Suranga Seneviratne, Aruna
Abstract	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.
Starting Page	949
Ending Page	959
Page Count	11
File Format	PDF
ISBN	9781450334693
DOI	10.1145/2736277.2741084
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-05-18
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Spam apps Mobile apps Spam Android
Content Type	Text
Resource Type	Article

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