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  • Subject area(s): Engineering
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  • Published on: 7th September 2019
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Identifying Rating and Review based Ranking fraud in Mobile App Market

K.Uthra1, R.Dhanalakshmi2

1PG Scholar, 2 Assistant Professor, Dept of Computer Science and Engineering.

1 2RMK Engineering College, Anna University, Kaverapettai, Chennai

[email protected] [email protected]

contact :  +91-8438428798

Abstract ' Nowadays ranking fraud in mobile App market became more popular in the market in order to display their apps in the popularity list and to boost their sales .Therefore, there will be increase in ranking fraud in the upcoming time, as the number of Apps developers and applications will likely grow very significantly. Many traditional methods of fraud analysis have been used to detect fraud. But these methods are complex and time consuming. However there is more need to adopt some better techniques which can ensure the ranking fraud detection efficiently by data mining analysis. This paper explores the data mining methods to identify the fraud by using Rank Aggregation algorithm. Further three types of proofs are studied they are ranking, rating and review proofs. And an aggregation method is used to aggregate all the proofs and will produce an optimized report for fraud detection.

  Keywords 'Mobile Apps market, fraud detection, aggregation method, records of the apps, rating and review proofs.

1. INTRODUCTION

For the past few years the number of mobile Apps increases day by day.  As per the statistics taken in 2012, per day 1 millions of apps been downloaded in which 91% are free apps and the remaining 9% are paid one. And in order to increase the sales, each app is ranked and maintained in the leading board. Many fraudulent activities taking place in this board only. Thus, leading sessions is used for marketing the apps. Higher the rank usually lead to a huge downloads of the app and billion of rupees in gain. And they also follow various ways to move their app to higher position and will also undergo various marketing techniques including advertisements. Recently many trends are being used in order to boost the apps sales.

 For example, Google found the developer who undergone the ranking fraud in the app store.

In the literature, while there are some related work, such as Latent Dirichlet allocation , A taxi driving fraud detection systemic in city taxis, Rank aggregation via nuclear norm minimization, An unsupervised learning algorithm for rank aggregation, Unsupervised rank aggregation with distance-based models the problem of detecting ranking fraud for mobile Apps is still under-explored. Thus, we proposed a paper for ranking mobile fraud detection.

To solve this problem, first local deviations are proposed later they are combined and proposed for the global deviation. Finally all this proofs are categorized and it is maintained in the database. And also the patterns which are followed by the fraudulent apps to rank their apps is unique that is it varies for each and every leading sessions of the apps stores. Ranking is calculated based on the user's review and rating. Thus, further two types of proofs are recorded which helps to detect the fraud in the leading board. Finally an unsupervised proof is produced and aggregation method to integrate these three types of proofs for evaluating the credibility of leading board from mobile Apps. Thus the proposed system is more scalable, efficient and it performance is high comparing the existing.

The rest of this paper is partitioned as follows. In section 2 describe the leader board and rank aggregation. Section 3 contains proposed method. Simulated results are discussed in section 4. Finally, section 5 explains the conclusions and future works.

2. RELATED WORKS:

In this section, some of the related works about leader board session and rank aggregation are discussed.

2.1 Leader board session

It is observed that finding the historical records of the App are not always ranked higher in the leader board but only in few events .Thus, it is also found that there exist some adjacent leading events which are closer to one another to form a leading sessions.  Further, to find the ranking fraud from several leading sessions, an effective approach is developed called as Evidence Aggregation based Ranking Fraud Detection (EA-RFD).specifically, this method is denoted by score based aggregation (i.e., Principle 1) as EA-RFD-1, and dealed with rank aggregation (i.e., Principle 2) as EARFD- 2, respectively.

Definition 1 (Leading Event): Given a ranking threshold K_ 2''; K, a leading event e of App a contains a time range Te '' ''testart; te end_ and corresponding rankings of a, which satisfies start _ K_ < start_1, and end _ K_ < end ''1. Moreover, 8 tk 2 start; te end p1, we have raked_ K.

Definition 2 (Leading sessions): A leading session s of App a contains a time range Ts '' ''ts start; ts end and n adjacent leading events fe1; eng, which satisfies ts start '' te1 start, ts end '' ten end and there is no other leading session s that makes Ts . While, 8i 3 ''1; np, we have teip1 start to tei endp < f, where f is a predefined threshold of time for merging leading events.

Figure 1 Distribution of ios and android apps in leading sessions

Fig 2 is a leader board of the apple apps store where it daily updates the ranking of each and every app in their store. And it also give the details of the app which losses its rank and also the app which moves up in the rank list. The fraud is happening mainly in these types of the leader boards and the proposed system helps to detect this fraud. Finally an optimized report is generated which helps to detect the fraudulent apps.

Figure 2 Example of leader board

2.2 Rank aggregation

If we are to cast the rank aggregation, we need to define our objective function. In this context, we would like to and a "super"-list which would be as "close" as possible to all individual ordered lists simultaneously. This is a natural requirement and the objective function, at least in its most abstract form, is very simple and intuitive.

This is a proposed ordered list of length k = jLij, wi is the importance weight associated with list Li, d is a distance function.

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