Abstract: Now days, Due to faster development in the mobile technology and mobile devices, the applications that is mobile apps are being very interesting and well-known concept in this field. As there is large number of mobile Apps, ranking fraud is the key challenge in front of the mobile App market. Ranking fraud is the term used for referring to fraudulent or suspicious activities which have intention of boosting up the Apps in the popularity list. In fact, App developers are using tricky means more and more frequently for increasing their Apps’ sales or posting fake App ratings. Thus the need for preventing ranking fraud has been widely realized. This paper proposes a system for mobile apps in order to ranking fraud detection. The proposed system mines the leading sessions of mobile apps to precisely locate the ranking fraud. Additionally, system finds ranking, rating and review behaviors and investigation of three types of evidences, they are ranking based evidences, rating based evidences and review based evidences is done. Then, we propose an aggregation method based on optimization to combine all the evidences for fraud detection. Finally, the proposed system will be measured with App data collected from the App Store for a long time period.

Keywords: Mobile Apps, ranking fraud detection, evidence aggregation, historical ranking records, rating and review.