Abstract under-explored. It is worth noting that

Abstract  : Ranking pretender in
the mobile applications  market refers to
irrrelavant or deceptive activities which have a purpose of moving up the apps
in the frequent user list. Indeed, it becomes more frequent for App develops to
use shady means, such as raise their apps’ sales or posting  app ratings, to commit ranking fake. While the
importance of prevent ranking  fake has
been large recognized, there is limited under and research in this area. In this
paper, we provide a dealing with view of ranking fraud and propose a ranking
fraud identification system for mobile apps. Specifically, we investigate two
types of authentication, ranking based authentication and rating based authentication,
by modeling apps’ ranking and rating behaviors through statistical hypothesis
tests. In addition, we propose an optimized aggregation method to combine  all the evidences for fraud detection.

: The
number of mobile apps has grown at a breathtaking rate over the past few years.
For example, as of the end of April 2013, there are more than 1.6 million apps
in apple’s app store and google play. To stimulate the development of mobile
apps, many app stores launched daily app and google play.As a recent trend,
instead of relying on traditional marketing solutions, shady app developers
resort to some fraudulent means to deliberately boost their apps and eventually
manipulate the chart rankings on an app store 
Therefore, app developers tend to explore various ways such as a
blogs,forum, campaigns to promote their apps in order to have their apps ranked
as high as possible in such app Top ranked  In the literature, while the 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. It is worth noting that all the evidences are
extracted by modeling Apps’ ranking, rating and review behaviors through
statistical hypotheses tests. The proposed frame- work is scalable and can be
extended with other domain- generated evidences for ranking fraud detection.

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