GroupTie: toward hidden collusion group discovery in app stores
Abstract
The current centralized application (or app) markets provide convenient ways to distribute mobile apps. Their vendors maintain rating systems, which allow customers to leave ratings and reviews. Since positive ratings and reviews can lead to more downloads/installations and hence more monetary benefit, the rating systems have become a target of manipulation by some collusion groups hired by app developers. In this paper, we thoroughly analyze the features of hidden collusion groups and propose a novel method called \emph{GroupTie} to narrow down the suspect list of collusive reviewers for further investigation by app stores. As members of a hidden collusion group have to work together more frequently and their ratings often deviate more from apps' quality, collusive actions will enhance their relation over time. We build a relation graph named \emph{tie graph} and detect collusion groups by applying graph clustering. Simulation results show that the precision of GroupTie approaches to $99.70\%$ and the recall is about $91.50\%$. We also apply our method to detect hidden collusion groups among the reviewers of $89$ apps in Apple's China App Store. A large number of reviewers are discovered belonging to a large collusion group and several small groups.
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Decreasing account reuse decreases profits, because reputable accounts are often preferred in search rank fraud jobs.
“Decreasing account reuse decreases profits, as reputable accounts are often preferred in search rank fraud jobs [22,68,87].”
From Fraud De-Anonymization for Fun and Profit (2018) · cites this paper as [68]
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Detecting, Representing and Querying Collusion in Online Rating SystemsPreprint
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Self-citationThe Art and Craft of Fraudulent App Promotion in Google Play
Rahman, M., Hernandez, N., Recabarren, R., Ahmed, S. I., & Carbunar, B. (2019) · Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3319535.3345658 · Cited by 24 · see citing works
Fraud De-Anonymization for Fun and Profit
Hernandez, N., Rahman, M., Recabarren, R., & Carbunar, B. (2018) · Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3243734.3243770 · Cited by 16 · see citing works
Search Rank Fraud De-Anonymization in Online Systems
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- Authors
- Xie, Z., & Zhu, S.
- Affiliations
- Pennsylvania State University (US)
- ORCID
- Zhu, Sencun
- Venue
- Proceedings of the 2014 ACM conference on Security and privacy in wireless & mobile networks
- Publisher
- ACM
- Published
- 2014-07-23
- Pages
- 153-164
- OpenAlex
- W2027565943
- Cited by
- 19
- References
- 16
- Citations by year
- 2025: 2 · 2021: 1 · 2020: 1 · 2019: 3 · 2018: 4 · 2017: 3 · 2016: 2 · 2015: 3
- FWCI
- 2.27
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec) (publishedVersion)
- Funders
- National Science Foundation (1320605)
- Google (A gift)
- Topics
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Physical Sciences › Mathematics › Statistics and Probability › Benford’s Law and Fraud Detection
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- collusion detection, app reviews, graph clustering, App Store
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