Crowd Fraud Detection in Internet Advertising
Abstract
The rise of crowdsourcing brings new types of malpractices in Internet advertising. One can easily hire web workers through malicious crowdsourcing platforms to attack other advertisers. Such human generated crowd frauds are hard to detect by conventional fraud detection methods. In this paper, we carefully examine the characteristics of the group behaviors of crowd fraud and identify three persistent patterns, which are moderateness, synchronicity and dispersivity. Then we propose an effective crowd fraud detection method for search engine advertising based on these patterns, which consists of a constructing stage, a clustering stage and a filtering stage. At the constructing stage, we remove irrelevant data and reorganize the click logs into a surfer-advertiser inverted list; At the clustering stage, we define the sync-similarity between surfers' click histories and transform the coalition detection to a clustering problem, solved by a nonparametric algorithm; and finally we build a dispersity filter to remove false alarm clusters. The nonparametric nature of our method ensures that we can find an unbounded number of coalitions with nearly no human interaction. We also provide a parallel solution to make the method scalable to Web data and conduct extensive experiments. The empirical results demonstrate that our method is accurate and scalable.
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Identifying organic ASO activities is an open problem due to the ability of such workers to evade existing detection solutions.
“Identifying 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, 5759, 65, 66, 84, 87, 88, 92].”
From RacketStore (2021) · cites this paper as [78]
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- Authors
- Tian, T., Zhu, J., Xia, F., Zhuang, X., & Zhang, T.
- Affiliations
- State Key Laboratory of Intelligent Technology and Systems (CN)
- Baidu (China) (CN)
- Venue
- Proceedings of the 24th International Conference on World Wide Web
- Published
- 2015-05-18
- Pages
- 1100-1110
- OpenAlex
- W2235660311
- Cited by
- 67
- References
- 20
- Citations by year
- 2025: 7 · 2024: 5 · 2023: 2 · 2022: 3 · 2021: 13 · 2020: 8 · 2019: 8 · 2018: 12 · 2017: 6 · 2016: 3
- FWCI
- 6.08
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International World Wide Web Conference (WWW) (publishedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- Funders
- National Basic Research Program (973 Program) of China (2013CB329403)
- National Natural Science Foundation of China (61322308, 61332007)
- Tsinghua National Laboratory for Information Science and Technology Big Data Initiative
- Topics
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Physical Sciences › Computer Science › Computer Science Applications › Mobile Crowdsensing and Crowdsourcing
- Physical Sciences › Computer Science › Information Systems › Web Data Mining and Analysis
- Keywords
- crowdsourcing, search engine advertising, click fraud, nonparametric clustering, parallel scalability
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