1. “DETECTING IMPOSTURE IN MOBILE APPLICATION”

2. Problem Statement
To develop a web application for users of playstores that accurately locate deception for mobile application by mining active periods.

3. Scope
For the futher enhancement of users experience, we propose to analyse of review, rating, ranking, and also features and description to get effective imposture or fraud evidence.
To find best app for that category.

4. Objective
To collaborate information and opinion of the mobile application like review, rating, ranking, and features and description.
Processing and analyzing data for fraud or fake detection.
Generate Analysis report for fraud detection on input data for particular application.

5. Abstract
Play store provides large number of application but unfortunately few of those applications are fraud.
Such applications must be marked, so that they will be recognizable for play store users.
So we are proposing a web application to locate them, which will process the information like rating , reviews ,ranking and description of the application with natural language processing to give results in the form of graph .
Here we are using Hadoop so it would be easier to detect application is fraud or not.
Finally processing and comparison of multiple application will be done at a time to get more relevant application.

6. Architectural Diagram
Apps store &
Historical Data
Mining leading session
Ranking Based Analysis
Web application
Review Based Analysis
Aggregation
Rating Based Analysis
Best solution
Description Analysis

7. Literature survey

10. References Web References:
Hengshu Zhu, Hui Xiong,,Yong Ge, and Enhong Chen,”Discovery of Ranking Fraud for Mobile Apps”,IEEE Transactions on knowledge and data engineering,2015,pp
B. Zhou, J. Pei, and Z. Tang, “A spamicity approach to web spam detection,” in Proc. SIAM Int. Conf. Data Mining, 2008, pp. 277–288.
H. Zhu, H. Cao, E. Chen, H. Xiong, and J. Tian, “Exploiting enriched contextual information for mobile app classification,” in Proc. 21st ACMInt. Conf. Inform. Knowl. Manage., 2012, pp. 1617–1621.
H. Zhu, E. Chen, K. Yu, H. Cao, H. Xiong, and J. Tian, “Mining personal context-aware preferences for mobile users,” in Proc. IEEE 12th Int. Conf. Data Mining, 2012, pp. 1212–1217.
H. Zhu, H. Xiong, Y. Ge, and E. Chen, “Ranking fraud detection for mobile apps: A holistic view,” in Proc. 22nd ACM Int. Conf. Inform. Knowl. Manage., 2013, pp. 619–628.

11. References Websites: http://en.wikipedia.org/wiki/ cohen’s_kappa
information_retrieval
index.php?id= a
apples-crackdown-on-app-ranking-manipulation

12. Questions ?

13. Thank You