IEEE Style Citation: Priyanka.jali , Nagavani Biradar , “A Novel Approach of Investigating Deceptive Activities of Developer for Ranking apps,” International Journal of Computer Sciences and Engineering, Vol.4, Issue.3, pp.139-141, 2016.
MLA Style Citation: Priyanka.jali , Nagavani Biradar "A Novel Approach of Investigating Deceptive Activities of Developer for Ranking apps." International Journal of Computer Sciences and Engineering 4.3 (2016): 139-141.
APA Style Citation: Priyanka.jali , Nagavani Biradar , (2016). A Novel Approach of Investigating Deceptive Activities of Developer for Ranking apps. International Journal of Computer Sciences and Engineering, 4(3), 139-141.
BibTex Style Citation:
@article{Biradar_2016,
author = {Priyanka.jali , Nagavani Biradar },
title = {A Novel Approach of Investigating Deceptive Activities of Developer for Ranking apps},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {3 2016},
volume = {4},
Issue = {3},
month = {3},
year = {2016},
issn = {2347-2693},
pages = {139-141},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=844},
publisher = {IJCSE, Indore, INDIA},
}
RIS Style Citation:
TY - JOUR
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=844
TI - A Novel Approach of Investigating Deceptive Activities of Developer for Ranking apps
T2 - International Journal of Computer Sciences and Engineering
AU - Priyanka.jali , Nagavani Biradar
PY - 2016
DA - 2016/03/30
PB - IJCSE, Indore, INDIA
SP - 139-141
IS - 3
VL - 4
SN - 2347-2693
ER -
VIEWS
PDF
XML
1435
1368 downloads
1366 downloads
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 modelling Apps’ ranking, rating and review behaviours 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.
Key-Words / Index Term
Mobile ranking, fraudulent mobile apps
References
[1] L. Azzopardi, M. Girolami, and K. V. Risjbergen, “Investigating the relationship between language model perplexity and ir precision-recall measures,” in Proc. 26th Int. Conf. Res. Develop. Inform. Retrieval, 2003, pp. 369–370.
[2] D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent Dirichlet allocation,” J. Mach. Learn. Res., pp. 993–1022, 2003.
[3] Java The complete Reference.
[4] My sql Reference Books.
VIEWS
PDF 
XML 
