• Arshia Tabassum
• N.Manisha
• A.Sai Kumar
• K.Sai Krishna

Ranking fraud the Apps in the popularity list Indeed, it becomes more and more frequent for App developers to use shady means, such a s 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.

Apps, Ranking Fraud Apprehension, Evidence Reckoning, Historical Records, Rating and Review

"FRAUD DETECTION ON APP USAGE WITH RANKING STATISTICS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 4, page no.359-361, April-2018, Available :http://www.jetir.org/papers/JETIR1804068.pdf

"FRAUD DETECTION ON APP USAGE WITH RANKING STATISTICS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 4, page no. pp359-361, April-2018, Available at : http://www.jetir.org/papers/JETIR1804068.pdf