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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
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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 thejdetection algorithm as well as some
regularity of ranking fraud activities.
EXISTING SYSTEM:
The number of mobile Apps has grown at a huge rate over the past few years. To
stimulate the development of mobile Apps, many App stores launched daily App
leaderboards, which demonstrate the chart rankings of most popular Apps. A higher rank
on the leaderboard usually leads to a huge number of downloads and million dollars in
revenue. instead of ‘relying on traditional marketing solutions, shady App developers
resort to some fraudulent means to deliberately boost their Apps and eventually
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manipulate the chart rankings on an App store. This is usually implemented by using so-
called “bot farms” or “human water armies” to inflate the App downloads, ratings and
reviews in a very short time.
DISADVANTAGE OF EXISTING SYSTEM:
> When an App was promoted with the help of ranking manipulation it could be
top in leaderboard and more new users could be purchased that product.
> affect other App reputation.
PROPOSED SYSTEM:
In proposed system, we propose to develop a ranking fraud detection system for
mobile Apps. Ranking fraud does not always happen in the whole life cycle of an App, so
we need to detect the time when fraud happens. Indeed, our careful observation reveals
that mobile Apps are not always ranked high in the leaderboard, but only in some leading
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events, which form different leading sessions. In other words, ranking fraud usually
happens in these leading sessions. Specifically, 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.
ADVANTAGE OF PROPOSED SYSTEM:
> Detect fraud ranking in daily App leaderboards.
> Avoid ranking manipulation.
MODULES:
A module is a part/ .,of aprogram. Programs are composed of one or more
independently developed modules that are not combined until the program is linked. A
single module can contain one or several routines.
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Our project modules are given below:
> Identifying leading sessions
> Ranking based evidences
> Rating based evidences
> Review based evidences
> Evidence aggregation
IDENTIFYING LEADING SESSIONS:
Ranking fraud usually happens in leading sessions Therefore, detecting ranking
fraud of mobile Apps is actually to detect ranking fraud within leading sessions of mobile
Apps. Specifically, 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.
miccmsinfotech, N0: 8 . 100 FEET ROAD. PONDlCHERRY.
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Mining Leading Sessions: There are two main steps for mining leading sessions.
First, we need to discover leading events from the App’s historical, ranking records.
Second, we need to merge adjacent leading events for constructing leading sessions.
RANKING BASED EVIDENCES:
A leading session is composed of severalfleading events. Therefore, we should
first analyze the basic characteristics of leading events for extracting fraud evidences. By
analyzing the Apps’ historical ranking records, we observe that Apps’ ranking behaviors
in a leading event always satisfy a specific ranking pattern, which consists of three
different ranking phases, namely, rising phase, maintaining phase and recession phase.
Specifically, in each leading event, an App’s ranking first increases to a peak position in
the leaderboard (i. e., rising phase), then keeps such peak position for a period (i. e.,
maintaining phase), and finally decreases till the end of the event (i. e., recession phase).
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RATING BASED EVIDENCES:
The ranking based evidences are useful for ranking fraud detection. However,
sometimes, it is not sufficient to only use ranking based evidences. Specifically, after an
App has been published, it can be rated by any user who downloaded it. Indeed, user
rating is one of the most important features of App advertisement. An App which has
higher rating may attract more users to download and can also be ranked higher in the
leaderboard. Thus, rating manipulation is also an important perspective of ranking fraud.
Intuitively, if an App has ranking fraud in a leading session s, the ratings during the time
period of s may have anomaly patterns compared with its historical ratings, which can be
used for constructing rating based evidences.
REVIEW BASED EVIDENCES:
Besides ratings, most of the App stores also allow users to write some textual
comments as App reviews. Such reviews can reflect the personal perceptions and usage
experiences of existing users for particular mobile Apps. Indeed, review manipulation is
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one of the most important perspective of App ranking fraud. Specifically, before
downloading or purchasing a new mobile App, users often firstly 5, read its historical
reviews to ease their decision making, and a mobile App contains more positive reviews
may attract more users to download. Therefore, imposters often post fake reviews in the
leading sessions of a specific App in order to inflate the App downloads, and thus propel
the App’s ranking position in the leaderboard. Although some previous works on review
spam detection have been reported in recent years, the problem of detecting the local
anomaly of reviews in the leading sessions and capturing them as evidences for ranking
fraud detection are still under-explored.
EVIDENCE AGGREGATION:
After extracting three types of fraud evidences, the next challenge is how to
combine them for ranking fraud detection. Indeed, there are many ranking and evidence
aggregation methods in the literature, such as permutation based models, score based
models and Dempster-Shafer rules. However, some of these methods focus on learning a
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global ranking for all candidates. This is not proper for detecting ranking fraud for new
Apps. Other methods are based on supervised learning techniques, which depend on the
labeled training data and are hard to be exploited. Instead, we propose an unsupervised
approach based on fraud similarity to combine these evidences.
SYSTEM REQUIREMENTS:
The goal of system requirement specification is to completely specify the technical
requirements for the product in a concise and unambiguous manner.
HARDWARE REQUIREMENTS:
- System : Pentium IV 2.4 GHZ.
- Hard Disk : 40 GB.
- Floppy Drive 2 1.44 Mb.
0 Monitor : 15 VGA Colour.
0 Mouse : Sony.
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‘r 7 Years of Excellence in IEEE Project development for universities across INDIA,
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[2] H. Zhu, E. Chen, . . ao, H. Xiong, and J . Tian. Mining personal context-
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