T. Namrata1                                                      T. Ranga Rao2                                 Briskilal J. 3 (M. E.)

B. Tech. (C.S.E.)                                            B. Tech. (C.S.E.)                                     Assistant Professor

SRM
Institute of Technology                       SRM Institute of Technology       SRM Institute of Technology

         

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.

 

Keywords: ranking fraud
detection; ranking based evidence; rating based evidence; review based
evidence; evidence aggregation.

 

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
papers were reviewed to make the paper comprehensive for further research.

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: B. Spirin et al. did a survey that introduced
many algorithms and principles in literation for Web Spam Detection.

3

 

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.

A
flexible generative model for preference aggregation authored by M. N.
Volkovs and R. Zemel has expressed a model that proposes a malleable model over
comparisons where preferences to items could be conveyed in different forms
that  otherwise make the aggregation
problem hard. Several experiments done on high yardstick datasets state higher
performance compared to existent methods.

Unsupervised
rank Aggregation with domain-specific expertise proposed by A. KKlemetiev, D.
Roth, K. Small and I. Titov have suggested a framework for learning to
aggregate rankings with domain

specific
expertise sans supervision by applying it to the sceneries of combining full
rankings and aggregating top-k lists, indicating major progress over
domain-agnostic standard in these cases.

These are
the sources of literature based on which the proposed system was articulated
and presented.

 

1.       Ranking Detection
and Avoidance Frauds in Mobile Apps Store9

Author/citation

Objective

Key findings
 

Limitations

ABHIILASH T P, L DINESHA
 

Detecting and avoiding ranking
frauds in mobile applications

Experiene based evidences

Review based evidences could be
taken into consideration as well.

 

2.       Fraud Application Detection Using Data
Mining Techniques 11

Author/citation

Objective

Key findings
 

Limitations

Tejaswini Shingare , Madhuri
Sancheti, Swaleha Shaikh, Jyoti Ugale, Prof.J.N.Kale

Detecting ranking frauds in mobile
applications

Boolean weight as 0 or 1 is
assigned to every evidence where no fraud is indicated by 0 and fraud is
indicated by 1.

Fraud usually occurs in the leading
sessions of the apps lifecycle which is not mined for.

 

3.       Discovery of Ranking Fraud for Mobile Apps 14

Author/citation

Objective

Key findings
 

Limitations

E. Ramya,. V. Vetriselvi

Identifying ranking frauds in mobile
apps

a positioning extortion discovery
framework for mobile Apps

Ideal bond between ranking, rating
and survey evidences can be established.

 

4.       Discovery of Ranking of Fraud for Mobile
Apps

Author/citation

Objective

Key findings
 

Limitations

Ranjitha.R, Mathumitha.K, Meena.S,
S.Hariharan

Detecting fraudulent  ranking in mobile applications

an optimized admin verification
method to evaluate the reliability of leading sessions of mobile Applications

Aggregation of the evidences could
lead to a collective understanding of the fraud.

 

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.

 

                                   

                                                                                Overview
of the Proposed System

 

System Proposed:

 

We have proposed a simple algorithm with good efficacy to detect
leading sessions of each App based upon its’ historical records. It is
discovered that fraudulent Apps have their ratings spiked during the leading
sessions by analysing their ranking behaviours. By examining the ranking
behaviours of Apps, we notice that the fraudulent Apps habitually have
different ranking patterns in each leading session likened with normal
Applications.

 

 

Furthermore, grounded on Apps’ past records of rating and review, two
kinds of fraud evidences are gathered. Any anomaly detected will flag the red
flags for fraud detection. The time period of popularity for an App is
reflected by its leading sessions. Thus, ranking fraud scenarios can be founded
by identifying susceptible leading sessions. Also, the major work here involves
extraction of leading sessions from the Apps’ historical records of ranking.

The two main segments of fraudulent ranking detection are as follows:

o  
Detecting mobile apps’ leading sessions.

o  
Detecting evidences that support ranking fraud
detection

To have a brief look at these aspects,

 

1) Detecting mobile apps’ leading sessions.

This in turn is divided into two segments. Firstly, the leading events
are extracted from the Apps past records of ranking. Secondly, leading sessions
are erected by merging the leading events together. An algorithm identifies
leading events and sessions by skimming the historical records of the App from
pseudo code for mining sessions of a certain mobile app.

 

2) Detecting evidences that support ranking fraud detection.

There are three types of evidences that support the detection of
fraudulent ranking.

 

a)Evidence based on Ranking: The leading sessions comprise of the
leading events which can be analysed of their general behaviours for an anomaly
with the app’ past records of the same. It is observed that a certain pattern
of ranking is always fulfilled by ranking behaviour of the app in case of a
leading event.

 

b) Evidence based on Rating: The previous evidence is helpful but not
adequate for conclusion of results. To restrict the problem of “restrict time
depletion”, evidence accumulation is also based on historical records of rating
for mobile apps. Since the rating is done after an app is installed by the
user, the higher the rating the higher its position in the leader board which
would result in further downloads by attracting new users. Naturally, rating
fraud occurs during the leading sessions in the case of an anomaly which can be
used to identify evidence for fraudulent rating of the mobile apps.

 3) Evidence based on Review:
Review contain textual comments on the app and its performance. These reviews
are given by current users of the app who have already installed the said app.
This can be termed as the hardest segment of evidence that can be gathered.  These are compared again with the apps’
historical record of reviews and if there is an unusual spike of good reviews
during the leading sessions, evidence is said to be gathered.

 

The above mentioned three evidences are merged using evidence
aggregation technique that is unsupervised. This helps test the integrity of
mobile Apps’ leading sessions. The statistical hypotheses tests models Apps’
ranking, rating and review behaviors to extract all the evidences. This outline
is scalable which can be drawn-out with other area spawned evidences for
detecting ranking fraud. At last, the proposed system will be tested with
real-world data of Apps composed from Apple’s App store for a time extent of
more than two years.

 

Deduction:

 

 This paper reviews various
existing methods used for web spam detection, which is related to the ranking
fraud for mobile Apps. Also, we have seen references for online review spam
detection and mobile App recommendation. By mining the leading sessions of
mobile Apps, we aim to locate the ranking fraud. The leading sessions works for
detecting the local anomaly of App rankings. The system aims to detect the
ranking frauds based on three types of evidences, such as ranking based
evidences, rating based evidences and review

based evidences. Further, an optimization based aggregation method
combines all the

 

three evidences to detect the fraud.

 

References:

 

1https://en.wikipedia.org/wiki/History_of_mobile_phones

2https://en.wikipedia.org/wiki/Public_switched_telephone_network

3https://www.smashingmagazine.com/2017/02/current-trends-future-prospects-mobile-app-market/

4https://www.foundersspace.com/marketing-pr/marketing-your-app-banner-ads-vs-bots-human-water-armies/

5https://www.protected.media/press/seven-things-you-should-know-about-in-app-fraud/

6https://www.emarketer.com/Article/Indias-Mobile-Ad-Fraud-Problem-One-of-Worst-World/1016550

7 Y. Ge,
H. Xiong, C. Liu, and Z.-H. Zhou. “A taxi driving fraud detection system” In
Proceedings of the 2011 IEEE 11th International Conference on Data Mining, ICDM
’11, pages 181–190, 2011

 8 Hengshu Zhu, Hui Xiong, Senior Member,
IEEE, Yong Ge, and Enhong Chen, Senior Member, IEEE “Discovery of Ranking Fraud
for Mobile Apps” IEEE Transactions On Knowledge And Data Engineering, Vol. 27,
No. 1, January 2015.

9 ABHIILASH
T P, L DINESHA “Ranking Detection and Avoidance Frauds in Mobile Apps Store” 1st
International Conference on Innovations in Computing & Networking
(ICICN16), CSE, RRCE

10 Monali
Zende & Prof.Aruna Gupta “Ranking Fraud and Fake Reviews Detection for
Mobile Apps”International Journal of Advanced Research in Computer Science,2016

11 Tejaswini
Shingare, Madhuri Sancheti, Swaleha Shaikh, Jyoti Ugale, Prof.J.N.Kale “Fraud Application
Detection Using Data Mining Techniques”, IJARIIE, Vol-3 Issue-2 2017

12 Ranjitha.R,
Mathumitha.K, Meena.S, S.Hariharan, “Discovery of Ranking of Fraud for Mobile
Apps”, (IJIREM), Volume-3, Issue-3, May-2016

13 Ms.
Prajakta U. Gayke , Prof. Sanjay B. Thakare, “”Ranking Fraud Detection for
Mobile Apps using Evidence Aggregation Method, IJEDR Volume 4, Issue 3,2016.

14 E.
Ramya Mrs. V. Vetriselvi,” Discovery of Ranking Fraud for Mobile Apps”002C IJSTE
– Volume 2, Issue 12,June 2016.

15 Mrs. CH
APPORVA, 2Mr. K.ASHOK KUMAR,” Discovery of ranking fraud for Mobile Apps”,
IJCERT, Volume 3, Issue 6, June-2016.