Proceedings of the International Conference on Advanced Materials, Manufacturing and Sustainable Development (ICAMMSD 2024)

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Detecting Mobile App Fraud Review and Fake Ranking

Authors
P. N. V. S. Pavan Kumar1, *, N. Kasiviswanath2, A. Suresh Babu3
1Assistant Professor, Department of CSE (AIML), G. Pulla Reddy Engineering College, Kurnool, Andhra Pradesh, India
2Professor, Department of CSE, G. Pulla Reddy Engineering College, Kurnool, Andhra Pradesh, India
3Professor, Department of CSE, JNTUA College of Engineering, Ananthapuramu, India
*Corresponding author. Email: pavan.ecs@gprec.ac.in
Corresponding Author
P. N. V. S. Pavan Kumar
Available Online 17 March 2025.
DOI
10.2991/978-94-6463-662-8_25How to use a DOI?
Keywords
Fraud ranking; Spam Review Detection; Ranking based evidence; Mobile Apps
Abstract

Image Mobile Applications are hugely installed in various mobile phones now a days as mobile application developments are overwhelmingly increasing. As per survey huge number of mobiles apps is developed for performing fraud activities in recent years. Many application developers are indulging in shady means utilization, imposter postings and also in positioning distortion. To get fame and usage of mobile Applications, in various app stores generating leader boards in apps every day in order to specify the graph rankings of trendy applications. Efforts to promote their Apps are put in such App leader boards using various advertisements in order to have high ranking for their apps. Shady App designers are indulging in shady means activities to defame others genuine apps and boost their Apps and generate the fake rankings in their mobile application store. Web positioning spam acknowledgment (WPSA) and portable app recommendation systems are used in recent years. As per the research survey, the issue of fake reviews and defaming activities for mobile Apps is still unresolved. The issue of identifying fraud ranking in mobile applications is still under research. To specify the solutions for these issues various machine learning algorithms are compared and the best results are obtained from Random Forest algorithm approach.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Advanced Materials, Manufacturing and Sustainable Development (ICAMMSD 2024)
Series
Advances in Engineering Research
Publication Date
17 March 2025
ISBN
978-94-6463-662-8
ISSN
2352-5401
DOI
10.2991/978-94-6463-662-8_25How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - P. N. V. S. Pavan Kumar
AU  - N. Kasiviswanath
AU  - A. Suresh Babu
PY  - 2025
DA  - 2025/03/17
TI  - Detecting Mobile App Fraud Review and Fake Ranking
BT  - Proceedings of the International Conference on Advanced Materials, Manufacturing and Sustainable Development (ICAMMSD 2024)
PB  - Atlantis Press
SP  - 313
EP  - 318
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-662-8_25
DO  - 10.2991/978-94-6463-662-8_25
ID  - Kumar2025
ER  -