ABSTRACT

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 developers 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 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. 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 regularity of ranking fraud activities.

EXISTING SYSTEM:

• In the literature, while there are some related work, such as web ranking spam detection, online review spam detection and mobile App recommendation, the problem of detecting ranking fraud for mobile Apps is still under-explored.
• Generally speaking, the related works of this study can be grouped into three categories.

DISADVANTAGES OF EXISTING SYSTEM:

• Although some of the existing approaches can be used for anomaly detection from historical rating and review records, they are not able to extract fraud evidences for a given time period (i.e., leading session).
• Cannot able to  detect ranking fraud happened in Apps’ historical leading sessions.
• There is no existing benchmark to decide which leading sessions or Apps really contain ranking fraud.

PROPOSED SYSTEM:

• We first propose a simple yet effective algorithm to identify the leading sessions of each App based on its historical ranking records. Then, with the analysis of Apps’ ranking behaviors, we find that the fraudulent Apps often have different ranking patterns in each leading session compared with normal Apps. Thus, we characterize some fraud evidences from Apps’ historical ranking records, and develop three functions to extract such ranking based fraud evidences.
• We further propose two types of fraud evidences based on Apps’ rating and review history, which reflect some anomaly patterns from Apps’ historical rating and review records.

ADVANTAGES OF PROPOSED SYSTEM:

• The proposed framework is scalable and can be extended with other domain generated evidences for ranking fraud detection.
• Experimental results show the effectiveness of the proposed system, the scalability of the detection algorithm as well as some regularity of ranking fraud activities.

SYSTEM REQUIREMENTS

SOFTWARE REQUIREMENTS:

•           Web Technologies                               :           HTML, CSS, JS. JSP

•           Programming Language                      :           Java and J2EE

•           Database Connectivity                        :           JDBC

•           Backend Database                              :           MySQL

•           Operating System                               :           Windows 08/10

HARDWARE REQUIREMENTS:

• Processor                     :           Core I3
• RAM Capacity            :           2 GB
• Hard Disk                   :           250 GB
• Monitor                       :           15″ Color
• Mouse                         :           Two or Three Button Mouse
• Key Board                  :           Windows 08/10