Abstract fraud are integrated by optimization based aggregation method.

Abstract

Apps downloaded by users are mostly based on the psyche of downloading
well-rounded and efficiently working apps. These performance parameters are
assessed by the general users by rating these apps on a scale of 5. The top
rated apps are the first to appear while searching and sorting for the desired
apps. However, these ratings are being tweaked and fraudulently misrepresented
to appear on the popularity lists to boost downloads. There is a collective nod
among the users to keep these dubious deeds of misrepresentation at check. This
fraudulent representation of mobile app ratings will be discerned in this paper
by detecting the leading sessions of the App at which the fraudulent ratings
are depicted. Secondly, rating, ranking and review based evidences are mined by
modelling Apps’ behaviours of the same using statistical hypothesis tests.
Furthermore, all the evidences for the detection of the fraud are integrated by
optimization based aggregation method. The efficacy and the scalability of the
detection algorithm and the proposed system are validates by implementing the
same on real-life data of the Apps collected from iOS App Store.

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Introduction

With the advent of the wide spread practice of cellular mobiles with
internet connectivity that replaced the public switch telephone network (PSTN),
the face of the functioning of humans across the globe has taken giant leaps
towards advancements in the fields of communication and connectivity. Mobile
applications have become the lifelines of these very smart phones with internet
access through mobile broadband. In 2008, the App Store released by Apple gave
a drastic turn to how smartphones are used altogether with the intent of
well-packages, downloadable apps on phones. Since then, the mobile application
market has exponentially multiplied faster than a beanstalk. With projected
gross annual revenue to surpass $189 billion by the year 2020, the population
of web developers has seen a huge rise in numbers. With so much collective
enthusiasm in this field, the number of mobile applications in the play store
has shot up with fierce competitions among the app developers for higher number
of downloads. Like in any field, the bug of fraudulent projections of performances
has bitten this domain as well with fake representation of top rankings of Apps
by some App developers which dupes users into downloading their Apps. The fake
top leader board positions are achieved by paying up for a bot farm or
human/internet water armies that are hired to rate, rank and provide the said
App with a better review. Quite significantly, with 6.2 billion app downloads
in India in 2016, about 16.2% of the downloads showed some kind of fraud with
India ranking 10th highest ranking country for app install fraud
rate by Tune’s Accounting. Thus, this must be controlled to provide the users
with an authentic list of Apps for them to choose from and give a fair chance
to the Apps that genuinely appear on top of the App leader boards.    

To curtail this fraud, the proposed system detects ranking frauds that
occur majorly during the leading sessions of the Apps and not throughout the
lifecycle of the Apps. Leading sessions of the App lifecycle have the highest
probability of a red flag being noticed in the ratings. Thus these leading
sessions must be detected in the first module. Once, the leading sessions are
tracked, the rating based evidences, ranking based evidences and the review
based evidences are extracted from the modelling Apps’ behaviours of rating,
ranking and reviews by making use of statistics hypothesis tests. These
evidences will be aggregated using aggregation methods based on optimization.
If the said evidences differ vastly from the historical performances of Apps in
terms of ratings, rankings and reviews, then there is an anomaly that must be
addressed for course correction in the App rankings.

 

 

Literature
Survey:

 

Several
research papers were referred in order to make this paper a well-rounded paper
for further reference in this field of assessment.

There
are majorly three categories into which the research work can be grouped into.

 Firstly, web ranking spam detection detects
any incidence of web spamming. Web spamming is the procedure of raising particular
web pages by tweaking page ranking algorithms of search engines. A, Ntoulas
presented a range of heuristic methods to detect factors affecting spam on web
based on content to find heuristic methods. Using spamicity, Zhou et al.
proposed online link spam and spam detection methods.

Secondly,
online review spam detection:  spam
detection  of the online reviews. B.
Spirin et al. did a survey that introduced many algorithms and principles in
literation for Web Spam Detection.

Thirdly,
Mobile App Recommendation: it lays emphasis on the algorithms and factors
affecting them in recommending mobile application to users in ways of using
target marketing.

 

Challenges Faced:

 

Identifying
fraud ranking for Apps is a subject still under study. We propose a system to
fill the void a little in detecting this fraud. There are a certain challenges
that we face on doing so that are listed below.

First
challenge, the ranking fraud does not occur all the time in the lifecycle of an
App. Hence, we need to detect the time when it happens leading to identifying
local anomaly instead of global anomaly.

 Second challenge is to possess scalability detect
ranking fraud certainly without the use of any basis information because manual
labelling of ranking fraud for each and every App is very difficult.

Finally,
it is hard to catch and verify the evidences associated with ranking fraud due
to the volatile nature of rankings in the charts, which influences us to
discover contained fraud patterns of mobile Apps as evidences.