Main Article Content
Abstract
Ranking fraud in the mobile Application market refers to fraudulent or deceptive activities which have a purpose of bumping up the Applications in the popularity list. 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, providing a holistic view of ranking fraud and proposed a ranking fraud detection system for mobile applications. Specifically, in this proposed to accurately locate the ranking fraud by mining the active periods. Furthermore, investigating three types of evidences, i.e., ranking based evidences, rating based evidences, and review based evidences. In addition, we propose an optimization based aggregation system with real-world Application, 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.