TY - GEN
T1 - Ranking fraud detection for mobile apps
T2 - 22nd ACM International Conference on Information and Knowledge Management, CIKM 2013
AU - Zhu, Hengshu
AU - Xiong, Hui
AU - Ge, Yong
AU - Chen, Enhong
PY - 2013
Y1 - 2013
N2 - 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 develops 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 investigate two types of evidences, ranking based evidences and rating based evidences, by modeling Apps' ranking and rating 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 Apple's 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.
AB - 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 develops 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 investigate two types of evidences, ranking based evidences and rating based evidences, by modeling Apps' ranking and rating 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 Apple's 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.
KW - Mobile apps
KW - Ranking fraud detection
UR - http://www.scopus.com/inward/record.url?scp=84889587593&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84889587593&partnerID=8YFLogxK
U2 - 10.1145/2505515.2505547
DO - 10.1145/2505515.2505547
M3 - Conference contribution
AN - SCOPUS:84889587593
SN - 9781450322638
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 619
EP - 628
BT - CIKM 2013 - Proceedings of the 22nd ACM International Conference on Information and Knowledge Management
Y2 - 27 October 2013 through 1 November 2013
ER -