Ranking Fraud Detection System for Mobile Apps

Computer Engineering

It became more and more frequent for App developers to use shameful means, such as inflating their Apps’ sales or posting phony App ratings, to commit ranking fraud. In the mobile App market, ranking fraud refers to fraudulent or deceptive activities which have a purpose of bumping up the Apps in the popularity list. While the importance of preventing ranking fraud has been widely recognized, there is limited understanding and research in this area. In this paper, we provide a comprehensive way of ranking fraud and propose a ranking fraud detection system for mobile Apps. Specifically, we first propose to accurately locate the ranking fraud by mining the active periods, namely leading sessions, of mobile Apps. 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. We also propose 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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