报告题目:Ranking Fraud Detection for Mobile Apps: A Holistic View
报告人:Professor Hui Xiong
 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 App’s 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. In this talk, we provide a holistic view of ranking fraud and introduce 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 iOS 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 patterns of ranking fraud activities.
Short Biography
Dr. Hui Xiong is currently an Associate Professor and the Vice Chair of the Management Science and Information Systems Department, and the Director of Rutgers Center for Information Assurance at Rutgers, the State University of New Jersey, where he received a two-year early promotion/tenure (2009), the Rutgers University Board of Trustees Research Fellowship for Scholarly Excellence (2009), and the ICDM-2011 Best Research Paper Award (2011). Dr. Xiong received his Ph.D. in Computer Science from the University of Minnesota (UMN), USA, in 2005, the B.E. degree in Automation from the University of Science and Technology of China (USTC), China, and the M.S. degree in Computer Science from the National University of Singapore (NUS), Singapore. His general area of research is data and knowledge engineering, with a focus on developing effective and efficient data analysis techniques for emerging data intensive applications. He has published prolifically in refereed journals and conference proceedings (3 books, 50+ journal papers, and 60+ conference papers). He is the co-Editor-in-Chief of Encyclopedia of GIS by Springer, and an Associate Editor of IEEE Transactions on Knowledge and Data Engineering (TKDE) as well as the Knowledge and Information Systems (KAIS) journal. He has served regularly on the organization and program committees of numerous conferences, including as a Program Co-Chair of the Industrial and Government Track for the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), a Program Co-Chair for the IEEE 2013 International Conference on Data Mining (ICDM-2013), and a General Chair for the IEEE 2015 International Conference on Data Mining (ICDM-2015). He is a senior member of the ACM and the IEEE.
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