Discovery of Ranking Fraud Detection System for Mobile Apps

R. Satraboyina¹ , GK. Chakravarthi²

Section:Review Paper, Product Type: Isroset-Journal
Vol.4 , Issue.4 , pp.7-10, Aug-2016

Online published on Sep 08, 2016

Copyright © R. Satraboyina, GK. Chakravarthi . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

View this paper at   Google Scholar | DPI Digital Library

XML View     PDF Download

How to Cite this Paper

Abstract :
In Mobile App market, ranking fraud refers to fraudulent or deceptive activities for the purpose of bounce up the Apps in the popularity list. It became more common for App developers to raise there App’s by tricky means, sales or posting phony App ratings, to commit ranking fraud. The significance of preventing ranking fraud has been widely identified. There is limited understanding and research in this area. In this paper, we provide a complete view of ranking fraud and suggest a ranking fraud detection system for mobile Apps. Firstly, we 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 rather of global anomaly of App ratings. Moreover, we scrutinize three types of evidences, i.e., ranking based evidences, rating based evidences and review based evidences, by modeling Apps’ ranking, rating and review nature through statistical hypotheses tests. Additionally, we propose an optimization based aggregation method to consolidate all the evidences for fraud detection and finally we evaluate the proposed system with real-world App data collected from the iOS App Store for a lengthy time period. In the experiments, we validate the effectiveness of the suggested system, and show the scalability of the detection algorithm as well as some regularity of ranking fraud activities.

Key-Words / Index Term :
Mobile Apps; ranking fraud detection; evidence aggregation; historical ranking records; rating and review; Recommendation app; KNN

References :
[1]. S. Gupta, "Addressing the Issues in Mobile Application Development", International Journal of Computer Sciences and Engineering, Vol.2, Issue.7, pp.1-5, 2014.
[2]. P. Deshmukh, P. Agarkar, “Mobile Application For Malware Detection”, International Research Journal of Engineering and Technology (IRJET), Vol.2, Issue.2, pp.883-886, 2015.
[3]. B. A. Wichmann, A. A. Canning, D. L. Clutterbuck, L. A. Winsborrow, N. J. Ward, D. W. R. Marsh, "Industrial perspective on static analysis", Journal of Software Engineering, Vol.10, No.2, pp. 69-75, 1995.
[4]. MA. Dar, J. Parvez, “Evaluating Smartphone Application Security: A Case Study on Android”, Global Journal of Computer Science and Technology Network, Web & Security Vol.13, Issue.12, pp. 1-8, 2013.
[5]. N. Radha and S. Ramya , "Performance Analysis of Machine Learning Algorithms for Predicting Chronic Kidney Disease", International Journal of Computer Sciences and Engineering, Vol.3, Issue.8, pp.72-76, 2015.
[6]. A. Agarwal, N. K. Sharma, P. Gupta, P. Saxena, R.K. Pal, S. Mehrotra, P. Nair, M. Wadhwa, "Mobile Application Development with Augmented Reality", International Journal of Computer Sciences and Engineering, Vol.2, Issue.5, pp.20-25, 2014.
[7]. SS. Bhadoria, H. Gupta , "A Wearable Personal Healthcare and Emergency Information Based On Mobile Application", International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.4, pp.24-30, 2013.
[8]. S.Venkatesan and K.Renuka Devi, "Managing Connection Based Access Control Systems on the other hand Mobile Devices", International Journal of Computer Sciences and Engineering, Vol.3, Issue.9, pp.298-301, 2015.
[9]. H. Zhu ; E. Chen ; H. Xiong ; H. Cao ; J. Tian, “Mobile App Classification with Enriched Contextual Information”, IEEE Transactions on Mobile Computing,Vol.13, Issue.7, 2014.