Early Detection of Spam Mobile Apps

S. Seneviratne, A. Seneviratne, M. A. Kaafar, A. MahantiPMohapatraEarly Detection of Spam Mobile Apps”In Proceedings of the 24th International Conference on World Wide Web’15 (WWW) p949-959, Florence, Italy. May, 2015.

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 in- crease the workload of app market operators. Apps can be “spammy” in multiple ways including not having a specific functionality, unrelated app description or unrelated key- words 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 pro- pose a method to detect spam apps solely using app meta- data 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.

Dataset: The datasets used in our paper are made available to stem further research in the area of mobile spam detection (download). Please refer to Section 3 and 4 of our paper for a description of the data collection methodology and a summary of the datasets.

Note: If you use our datasets in your research, please include a reference to our WWW 2015 paper (pdf) in your work.

Contact: Please send your queries to Suranga.Seneviratne@data61.csiro.au