Mobile App Fraud Detection

Rating fraud in the mobile app industry refers to illegal or dishonest practises that are meant to bump up the mobile app market. The applications on the popularity chart. In fact, it is becoming more and more frequent for software developers to use dubious ways, such as inflating them. Sales or uploading of phone app scores, to commit ranking fraud. While the value of preventing rating fraud has been widespread, it is recognised that there is minimal awareness and study in this field. To this end, we have a systemic view of ranking in this article. Fraud and suggest a rating method for the detection of fraud in smartphone apps. Specifically, first of all, we propose to specifically identify the rating scam the mining of active times, including leading sessions, of mobile applications. These leading sessions can be leveraged for local identification. Anomaly instead of global app anomaly rankings. In addition, we analyse three forms of proof, i.e. a rating based on Proof, evidence-based rating and evidence-based analysis through modelling App ranking, rating and review behaviours by tests of mathematical theories. In addition, we suggest an aggregation-based optimization approach to incorporate all proof of fraud detection. Finally, we test the suggested framework for real-world software data obtained from the iOS App Store over a long period of time. In the tests verify the feasibility of the proposed method and demonstrate the scalability of the detection algorithm as well as the scalability of the system.