Discovery of Ranking Fraud for Mobile Apps

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.

AIM

The aim of this paper is provide a holistic view of ranking fraud and propose a ranking fraud detection system for mobile Apps.

SCOPE

The scope of this paper is 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.

EXISTING SYSTEM

In the literature, while there are some related works, 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 underexplored. To fill this crucial void, in this paper, we propose to develop a ranking fraud detection system for mobile Apps. Along this line, we identify several important challenges. First, ranking fraud does not always happen in the whole life cycle of an App, so we need to detect the time when fraud happens. Such challenge can be regarded as detecting the local anomaly instead of global anomaly of mobile Apps. Second, due to the huge number of mobile Apps, it is difficult to manually label ranking fraud for each App, so it is important to have a scalable way to automatically detect ranking fraud without using any benchmark information. Finally, due to the dynamic nature of chart rankings, it is not easy to identify and confirm the evidences linked to ranking fraud, which motivates us to discover some implicit fraud patterns of mobile Apps as evidences.

DISADVANTAGES

·      The problem of detecting ranking fraud for mobile Apps.

·      Huge number of mobile apps, it is difficult to manually label ranking fraud for each app.

PROPOSED SYSTEM

In this project, 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. Nonetheless, the ranking based evidences can be affected by App developers’ reputation and some legitimate marketing campaigns, such as “limited-time discount”. As a result, it is not sufficient to only use ranking based evidences. Therefore, 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. In addition, we develop an unsupervised evidence-aggregation method to integrate these three types of evidences for evaluating the credibility of leading sessions from mobile Apps. It shows the framework of our ranking fraud detection system for mobile Apps. It is worth noting that all the evidences are extracted by modeling Apps’ ranking, rating and review behaviors through statistical hypotheses tests. The proposed framework is scalable and can be extended with other domaingenerated evidences for ranking 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, i.e., more than two years. Experimental results show the effectiveness of the proposed system, the scalability of the detection algorithm as well as some regularity of ranking fraud activities.

ADVANTAGES

• An unique perspective of this approach is that all the evidences can be modeled by statistical hypothesis tests, thus it is easy to be extended with other evidences from domain knowledge to detect ranking fraud.
• Identified ranking based evidences, rating based evidences and review based evidences for detecting ranking fraud.

SYSTEM ARCHITECTURE

SYSTEM CONFIGURATION

HARDWARE REQUIREMENTS:-

·                Processor          -   Pentium –III

·                Speed                -    1.1 Ghz

·                RAM                 -    256 MB(min)

·                Hard Disk         -   20 GB

·                Floppy Drive    -    1.44 MB

·                Key Board                 -    Standard Windows Keyboard

·                Mouse               -    Two or Three Button Mouse

·                Monitor             -    SVGA

SOFTWARE REQUIREMENTS:-

·                Operating System      :Android OS             

·                Front End                  : JAVA

·                Database                  : SqLite

·                Tool                           :Eclipse

REFERENCES

Hengshu Zhu, Hui Xiong , Yong Ge , Enhong Chen “Discovery of Ranking Fraud for Mobile Apps” IEEE Transactions on Knowledge and Data Engineering, Volume27,  Issue 1 April  2014.