Ranking Detection and Avoidance Frauds in Mobile Apps StoreABHIILASH

There are millions of apps are available in market for the application of mobile users. However, all the mobile users first prefer high ranked apps when downloading it. But we cannot guarantee the reliability for the downloaded application since there is increasing number of ranking frauds. Ranking fraud in the mobile App market refers to fraudulent or deceptive activities which have a purpose of bumping up the Apps in the popularity list. Indeed, it becomes more and more frequent for App developers to use shady means, such as inflating their Apps‘ sales or posting phony App 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, in this paper, we provide a holistic view 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. 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 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 OS 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. There are in huge number of official and unofficial markets are available for mobile users to get variety of application. However, we cannot guarantee that the applications available in the market are trust worthy. Therefore, the application needs to be validated. In this paper we are introducing new protocol for detecting malicious apps. Keywords— Rank Based evidence, Ranking Fraud, Mobile Applications.