MOBILE APP SECURITY & FEATURES LEVEL ASSESSMENT

Computer Engineering

Fraud Ranking in the mobile App market refers to fraudulent or deceptive activities which have a purpose of bumping up the Applications in the popularity list. Indeed, it becomes more and more frequent for Application developers to use shady means, such as initiating their Applications sales or posting phony Application ratings, to commit ranking fraud. While the importance of preventing ranking fraud has been widely recognized, there is limited understanding and research in this area. To this end, we provide a holistic view of ranking fraud and propose a ranking fraud detection system for mobile Apps. Specially, we propose to accurately locate the ranking fraud by mining the active periods, namely leading sessions, of mobile Applications. Such leading sessions can be leveraged for detecting the local anomaly instead of global anomaly of App rankings. Furthermore, we investigate three types of evidences, i.e., ranking based evidences, rating based evidences and review based evidences, by modeling Apps ranking, rating and review behaviors through statistical hypotheses tests. In addition, we introduced an optimization based aggregation method to integrate all the evidences for fraud detection. Finally, we evaluate the proposed system with real-world App data collected from the IOS App Store for a long time period. In the experiments, we validate the effectiveness of the proposed system, and show the scalability of the detection algorithm as well as some regularity of ranking fraud activities.

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