Detecting Fraud in the Mobile App using 3-R Evidence Aggregation

	International Journal of P2P Network Trends and Technology (IJPTT)
	© 2017 by IJPTT Journal
	Volume - 7 Issue - 2
	Year of Publication : 2017
	Authors : K.Devi, M.Menaka

Citation

K.Devi, M.Menaka "Detecting Fraud in the Mobile App using 3-R Evidence Aggregation". International Journal of P2P Network Trends and Technology (IJPTT), V7(2):7-10 Mar - Apr 2017, ISSN:2249-2615, www.ijpttjournal.org, Published by Seventh Sense Research Group.

Abstract

Mobile applications (apps) are software developed for use on mobile devices and made available through app stores. App stores are highly competitive markets where developers need to cater to a large number of users spanning multiple countries. This work hypothesizes that there exist country differences in mobile app user behavior and conducts one of the largest surveys to date of app users across the world, in order to identify the precise nature of those differences. And the country wise fraud detection in the mobile app is detected using ranking, rating, review of an app and the fraud in the mobile app is done by the fraudulent with the help of bot farms and the main characteristics of fraud app duplication of data, gathering information without user knowledge and app ranking algorithm is used to detect Fraud in the mobile apps.

References

[1] D. Pagano and W. Maalej, ?User feedback in the appstore: Anempirical study, in Proc. 21st IEEE Int. Requirements Eng. Conf.,2013, pp. 125–134. [2] M. Bohmer, B. Hecht, J. Schoning, A. Kruger, and G. Bauer,?Falling asleep with Angry Birds, Facebook, Kindle: A large scalestudy on mobile application usage, in Proc. 13th Int. Conf. HumanComput. Interaction Mobile Devices Services, 2011, pp. 47–56. [3] B. Yan and G. Chen, ?Personalized mobile application discovery, in Proc. 9th Int. Conf. Mobile Syst., Appl., Serv., 2011,pp. 113–126. [4] S. L. Lim and P. J. Bentley, ?Investigating app store ranking algorithms using a simulation of mobile app ecosystems, in Proc.IEEE Congr. Evol. Comput., 2013,pp2672–2679. [5] S. L. Lim, D. Damian, F. Ishikawa, and A. Finkelstein, ?Using Web2.0 for stakeholder analysis: StakeSource and its application in tenindustrial projects, in Managing Requirements Knowledge. NewYork, NY, USA: Springer, 2013. [6] S. L. Lim and P. J. Bentley, ?How to become a successful appdeveloper? Lessons from the simulation of an app ecosystem,in Proc. Genetic Evol. Comput. Conf., 2012, pp. 129–136. [7] S. Jansen, A. Finkelstein, and S. Brinkkemper, ?A sense of community: A research agenda for software ecosystems, in Proc. 31st Int.Conf. Softw. Eng.-Companion Volume, ICSECompanion, 2009,pp. 187–190. [8] C. Iacob and R. Harrison, ?Retrieving and analyzing mobile apps feature requests from online reviews, in Proc. 10th Int. Workshop Mining Softw. Repositories, 2013, pp. 41–44. [9] H. Khalid, E. Shihab, M. Nagappan, and A. Hassan, ?What do mobile app users complain about? a study on free iOS apps, IEEE Softw., no. 1, p. 1, PrePrints, 2014. [10] N. Maiden, S. Jones, K. Karlsen, R. Neill, K. Zachos, and A. Milne,?Requirements engineering as creative problem solving: A research agenda for idea finding, in Proc. 18th IEEE Int. Requirements Eng. Conf., 2010, pp. 57–66. [11] A. S. Sayyad, T. Menzies, and H. Ammar, ?On the value of user preferences in search-based software engineering: A case study in software product lines, in Proc. Int. Conf. Softw. Eng., 2013, pp. 492–501.

Keywords
Requirements/specifications, market-driven software engineering, mobile application development, survey research, app user behavior, fraud detection , ranking, rating review and app ranking algorithm.