Paper Title
Identifying Ranking Frauds in Mobile Apps

Ranking extortion in the portable App business sector alludes to fake or misleading exercises which have a motivation behind knocking up the Apps in the fame list. For sure, it turns out to be more successive for App designers to utilize shady means, for example, swelling their Apps' business or posting fake App appraisals, to submit positioning extortion. While the significance of averting positioning extortion has been broadly perceived, there is restricted comprehension and examination here. To this end, in this paper, we give an all-encompassing perspective of positioning misrepresentation and propose a positioning extortion recognition framework for portable Apps. In particular, we first propose to precisely find the mining so as to position misrepresentation the dynamic periods, to be specific driving sessions, of versatile Apps.This paper gives a whole perspective of positioning misrepresentation and describes a Ranking fraud identification framework for mobile Apps. This work is grouped into three category. First is web ranking spam detection, second isonline review spam detection and last one is mobile app recommendation. The Web ranking spam refers to any deliberate actions which bring to selected Web pages an unjustifiable favorable relevance or importance. Review spam is designed to give unfair view of someproducts so as to influence the consumers' perception of the products by directly or indirectly influating or damaging the product's reputation. Keywords— Mobile Apps, Ranking Fraud Detection, Evidence Aggregation, Historical Ranking Records, Rating and Review, Recommendation app, KNN.