Search Rank Fraud De-Anonymization in Online Systems
Open accessAbstract
We introduce the fraud de-anonymization problem, that goes beyond fraud detection, to unmask the human masterminds responsible for posting search rank fraud in online systems. We collect and study search rank fraud data from Upwork, and survey the capabilities and behaviors of 58 search rank fraudsters recruited from 6 crowdsourcing sites. We propose Dolos, a fraud de-anonymization system that leverages traits and behaviors extracted from these studies, to attribute detected fraud to crowdsourcing site fraudsters, thus to real identities and bank accounts. We introduce MCDense, a min-cut dense component detection algorithm to uncover groups of user accounts controlled by different fraudsters, and leverage stylometry and deep learning to attribute them to crowdsourcing site profiles. Dolos correctly identified the owners of 95% of fraudster-controlled communities, and uncovered fraudsters who promoted as many as 97.5% of fraud apps we collected from Google Play. When evaluated on 13,087 apps (820,760 reviews), which we monitored over more than 6 months, Dolos identified 1,056 apps with suspicious reviewer groups. We report orthogonal evidence of their fraud, including fraud duplicates and fraud re-posts.
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Specialized fraud workers control multiple user accounts and seek employment from product developers to post fake reviews or activities.
“Specialized fraud workers (also referred to as fraud freelancers, or fraudsters) control multiple user accounts and seek employment by product developers to post fake reviews or activities for their products.”
From Fraud De-Anonymization for Fun and Profit (2018) · cites this paper as [45]
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Flagged first, then by citations
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Cited by
Sorted by citations
Understanding Incentivized Mobile App Installs on Google Play Store
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Fraud De-Anonymization for Fun and Profit
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“…organic ASO activities is an open problem due to the ability of such workers to evade existing detection solutions, e.g., that leverage lockstep behaviors [32, 43, 51, 72, 73, 78, 85, 86, 89, 92, 93] or review bursts [27, 28, 35, 39, 40, 42, 44, 45, 50–53, 53, 57– 59, 65, 66, 84, 87, 88, 92].”
BackgroundSelf-citation
- Authors
- Rahman, M., Hernandez, N., Carbunar, B., & Chau, D. H.
- Affiliations
- Florida Int'l University, Miami, FL, USA
- Florida International University (US)
- Georgia Institute of Technology (US)
- ORCID
- Chau, Duen Horng
- Venue
- Proceedings of the 29th on Hypertext and Social Media
- Publisher
- ACM
- Published
- 2018-07-03
- Pages
- 174-182
- OpenAlex
- W2810845273
- Cited by
- 8
- References
- 27
- Citations by year
- 2022: 1 · 2021: 2 · 2020: 3 · 2018: 2
- FWCI
- 1.37
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM Conference on Hypertext and Social Media (HT) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1527153)
- Topics
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Physical Sciences › Computer Science › Information Systems › Cybercrime and Law Enforcement Studies
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Keywords
- stylometry, Google Play Store, Upwork, fraud detection
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