Call for Papers:Vol.8 Issue.5

Submission
Last date		 31-Oct-2022
Acceptance Status In One Day
Paper Publish In Two Days
Submit ManuScript

News & Updates

Submit Article

Dear Authors, Article publish in our journal for Volume-8,Issue-5. For article submission on below link: Submit Manuscript


Join As Board

Dear Reviewer, You can join our Reviewer team without given any charges in our journal. Submit Details on below link: Join As Board


Paper Publication Charges

IJARIIE APP
Download Android App

For Authors

Archives

IJARIIE Board

Downloads

Android App

Download IJARIIE APP

Authors

Title: :  Ranking Fraud Detection System for Mobile Apps
PaperId: :  3872
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 3 Issue 2 2017
DUI:    16.0415/IJARIIE-3872
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Akshay V. MetkarSVIT, Chincholi, Nashik
Dipak N. MurtadakSVIT, Chincholi, Nashik
Gaurav K. ShahaneSVIT, Chincholi, Nashik
Santosh D. MutrakSVIT, Chincholi, Nashik

Abstract

Computer Engineering
Mobile Apps, Ranking fraud detection, Evidence Aggregation, Historical ranking Records, rating and review.
It became more and more frequent for App developers to use shameful means, such as inflating their Apps’ sales or posting phony App ratings, to commit ranking fraud. In the mobile App market, ranking fraud refers to fraudulent or deceptive activities which have a purpose of bumping up the Apps in the popularity list. While the importance of preventing ranking fraud has been widely recognized, there is limited understanding and research in this area. In this paper, we provide a comprehensive way 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. 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. We also 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.

Citations

Copy and paste a formatted citation or use one of the links to import into a bibliography manager and reference.

IJARIIE Akshay V. Metkar, Dipak N. Murtadak, Gaurav K. Shahane, and Santosh D. Mutrak. "Ranking Fraud Detection System for Mobile Apps" International Journal Of Advance Research And Innovative Ideas In Education Volume 3 Issue 2 2017 Page 852-856
MLA Akshay V. Metkar, Dipak N. Murtadak, Gaurav K. Shahane, and Santosh D. Mutrak. "Ranking Fraud Detection System for Mobile Apps." International Journal Of Advance Research And Innovative Ideas In Education 3.2(2017) : 852-856.
APA Akshay V. Metkar, Dipak N. Murtadak, Gaurav K. Shahane, & Santosh D. Mutrak. (2017). Ranking Fraud Detection System for Mobile Apps. International Journal Of Advance Research And Innovative Ideas In Education, 3(2), 852-856.
Chicago Akshay V. Metkar, Dipak N. Murtadak, Gaurav K. Shahane, and Santosh D. Mutrak. "Ranking Fraud Detection System for Mobile Apps." International Journal Of Advance Research And Innovative Ideas In Education 3, no. 2 (2017) : 852-856.
Oxford Akshay V. Metkar, Dipak N. Murtadak, Gaurav K. Shahane, and Santosh D. Mutrak. 'Ranking Fraud Detection System for Mobile Apps', International Journal Of Advance Research And Innovative Ideas In Education, vol. 3, no. 2, 2017, p. 852-856. Available from IJARIIE, http://ijariie.com/AdminUploadPdf/Ranking_Fraud_Detection_System_for_Mobile_Apps_ijariie3872.pdf (Accessed : 28 May 2019).
Harvard Akshay V. Metkar, Dipak N. Murtadak, Gaurav K. Shahane, and Santosh D. Mutrak. (2017) 'Ranking Fraud Detection System for Mobile Apps', International Journal Of Advance Research And Innovative Ideas In Education, 3(2), pp. 852-856IJARIIE [Online]. Available at: http://ijariie.com/AdminUploadPdf/Ranking_Fraud_Detection_System_for_Mobile_Apps_ijariie3872.pdf (Accessed : 28 May 2019)
IEEE Akshay V. Metkar, Dipak N. Murtadak, Gaurav K. Shahane, and Santosh D. Mutrak, "Ranking Fraud Detection System for Mobile Apps," International Journal Of Advance Research And Innovative Ideas In Education, vol. 3, no. 2, pp. 852-856, Mar-App 2017. [Online]. Available: http://ijariie.com/AdminUploadPdf/Ranking_Fraud_Detection_System_for_Mobile_Apps_ijariie3872.pdf [Accessed : 28 May 2019].
Turabian Akshay V. Metkar, Dipak N. Murtadak, Gaurav K. Shahane, and Santosh D. Mutrak. "Ranking Fraud Detection System for Mobile Apps." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 3 number 2 (28 May 2019).
Vancouver Akshay V. Metkar, Dipak N. Murtadak, Gaurav K. Shahane, and Santosh D. Mutrak. Ranking Fraud Detection System for Mobile Apps. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2017 [Cited : 28 May 2019]; 3(2) : 852-856. Available from: http://ijariie.com/AdminUploadPdf/Ranking_Fraud_Detection_System_for_Mobile_Apps_ijariie3872.pdf
BibTex EndNote RefMan RefWorks

Number Of Downloads


Last download on 5/28/2019 1:53:33 AM

Save in Google Drive

Similar-Paper

TitleArea of ResearchAuther NameAction
Applying Data Mining Hybrid Classification Techniques and Decision Tree approach for Government Employee’s Performance PredictionComputer EngineeringNirvesh Kumar Download
PHISHING ATTACKS:RAISING AWARENESS AND DEFENDINGCOMPUTER ENGINEERING HARSH DILIP PARDESHI Download
Personal Data Storage and Security for Employee TrackingComputer EngineeringNikita Wagh Download
REINFORCEMENT OF INTRUSION DETECTION SYSTEM USING NEURAL NETWORK AND SVMCOMPUTER SCIENCE & ENGINEERINGMANSI JAIN Download
Reviews Evaluation for Online Courses: A Deep Learning Technique / ApproachComputer Engineering Dr. Shyamrao V. Gumaste Download
Automatic Text Summarization Based on Lexical-Semantic and Similarity Based On Sentence Embedding ModelComputer Engineering Kalpana Metre Download
An analytical Survey on Predicting Crop Yield through Deep Learning techniquesComputer EngineeringOnkar S. Shinde Download
A Review of Energy Efficient Security Protocol for Wireless Sensor Networksdata miningmaster piyush Download
Energy Efficient Security Protocol for Wireless Sensor Networksdata miningmaster piyush Download
Review of Performance Evaluation of Q- LEACH on Cluster Head Selection Techniques in Wireless Sensor Networksdata miningRupesh Kumar Download
Dashboard for Monitoring the Real Time Data in Educational OrganizationComputer Engineering Dr. Prashant Yawalkar Download
Enhanced Phase Mechanism for Data Access Control based on Trust and Reputation in Cloud Computingdata miningNISHANT KUMAR Download
A neural network based efficient technique for Handwritten digit recognition processdata miningabhinav ranjan Download
Image CaptioningComputer EngineeringP Rashmi Download
Heart Diseases Prediction Using Machine LearningComputer EngineeringAvish Patil Download
12