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 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 the detection algorithm as well as some
regularity of ranking fraud activities.
AIM
The
aim of this paper is provide a holistic view of ranking fraud and propose a
ranking fraud detection system for mobile Apps.
SCOPE
The
scope of this paper is investigate three types of evidences, i.e., ranking
based evidences, rating based evidences and review based evidences, by modeling
Apps’ ranking, rating and review behaviors through statistical hypotheses
tests.
EXISTING SYSTEM
In
the literature, while there are some related works, 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 underexplored. To
fill this crucial void, in this paper, we propose to develop a ranking fraud
detection system for mobile Apps. Along this line, we identify several
important challenges. First, ranking fraud does not always happen in the whole
life cycle of an App, so we need to detect the time when fraud happens. Such
challenge can be regarded as detecting the local anomaly instead of global
anomaly of mobile Apps. Second, due to the huge number of mobile Apps, it is
difficult to manually label ranking fraud for each App, so it is important to
have a scalable way to automatically detect ranking fraud without using any
benchmark information. Finally, due to the dynamic nature of chart rankings, it
is not easy to identify and confirm the evidences linked to ranking fraud,
which motivates us to discover some implicit fraud patterns of mobile Apps as
evidences.
DISADVANTAGES
· The
problem of detecting ranking fraud for mobile Apps.
· Huge
number of mobile apps, it is difficult to manually label ranking fraud for each
app.
PROPOSED SYSTEM
In
this project, 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. Thus, we characterize some fraud evidences from Apps’ historical
ranking records, and develop three functions to extract such ranking based
fraud evidences. Nonetheless, the ranking based evidences can be affected by
App developers’ reputation and some legitimate marketing campaigns, such as
“limited-time discount”. As a result, it is not sufficient to only use ranking
based evidences. Therefore, we further propose two types of fraud evidences
based on Apps’ rating and review history, which reflect some anomaly patterns
from Apps’ historical rating and review records. In addition, we develop an
unsupervised evidence-aggregation method to integrate these three types of
evidences for evaluating the credibility of leading sessions from mobile Apps.
It shows the framework of our ranking fraud detection system for mobile Apps.
It is worth noting that all the evidences are extracted by modeling Apps’
ranking, rating and review behaviors through statistical hypotheses tests. The
proposed framework is scalable and can be extended with other domaingenerated
evidences for ranking fraud detection. Finally, we evaluate the proposed system
with real-world App data collected from the Apple’s App store for a long time period,
i.e., more than two years. Experimental results show the effectiveness of the
proposed system, the scalability of the detection algorithm as well as some
regularity of ranking fraud activities.
ADVANTAGES
- An unique perspective of this approach is that all the evidences can be modeled by statistical hypothesis tests, thus it is easy to be extended with other evidences from domain knowledge to detect ranking fraud.
- Identified ranking based evidences, rating based evidences and review based evidences for detecting ranking fraud.
SYSTEM
ARCHITECTURE
SYSTEM CONFIGURATION
HARDWARE REQUIREMENTS:-
· Processor - Pentium –III
·
Speed - 1.1 Ghz
·
RAM - 256 MB(min)
·
Hard
Disk - 20 GB
·
Floppy
Drive - 1.44 MB
·
Key
Board - Standard Windows Keyboard
·
Mouse - Two or Three Button Mouse
·
Monitor -
SVGA
SOFTWARE REQUIREMENTS:-
·
Operating
System : AndroidOS
·
Language : Java
·
Database
: SqLite Database
·
Tool :Eclipse
REFERENCES
Hengshu
Zhu, Hui Xiong , Yong Ge , Enhong Chen “Discovery of Ranking Fraud for Mobile
Apps” IEEE Transactions on Knowledge and Data Engineering, Volume27, Issue 1 April
2014.
No comments:
Post a Comment