research-article

Fighting online click-fraud using bluff ads

Author:

Hamed HaddadiAuthors Info & Claims

ACM SIGCOMM Computer Communication Review, Volume 40, Issue 2

Pages 21 - 25

https://doi.org/10.1145/1764873.1764877

Published: 09 April 2010 Publication History

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Abstract

Online advertising is currently the richest source of revenue for many Internet giants. The increased number of online businesses, specialized websites and modern profiling techniques have all contributed to an explosion of the income of ad brokers from online advertising. The single biggest threat to this growth, is however, click-fraud. Trained botnets and individuals are hired by click-fraud specialists in order to maximize the revenue of certain users from the ads they publish on their websites, or to launch an attack between competing businesses.

In this note we wish to raise the awareness of the networking research community on potential research areas within the online advertising field. As an example strategy, we present Bluff ads; a class of ads that join forces in order to increase the effort level for click-fraud spammers. Bluff ads are either targeted ads, with irrelevant display text, or highly relevant display text, with irrelevant targeting information. They act as a litmus test for the legitimacy of the individual clicking on the ads. Together with standard threshold-based methods, fake ads help to decrease click-fraud levels.

References

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A. Juels, S. Stamm, and M. Jakobsson. Combating click fraud via premium clicks. In SS'07: Proceedings of 16th USENIX Security Symposium on USENIX Security Symposium, pages 1--10, Berkeley, CA, USA, 2007. USENIX Association.

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Cited By

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Liang YChen XChen YXiao PZhang J(2024)Mobile ad fraud: Empirical patterns in publisher and advertising campaign dataInternational Journal of Research in Marketing10.1016/j.ijresmar.2023.09.00341:2(265-281)Online publication date: Jun-2024
https://doi.org/10.1016/j.ijresmar.2023.09.003
Zhou SZhang HGao GSun YHuang HWang XWang Y(2024)KTSketch: Finding k-Persistent t-Spread Flows in High-Speed NetworksWeb and Big Data10.1007/978-981-97-7241-4_21(326-342)Online publication date: 31-Aug-2024
https://dl.acm.org/doi/10.1007/978-981-97-7241-4_21
Yu LLi SMeng YWang XZhu H(2024)Poisoning Attack in Machine Learning Based Invalid Ad Traffic DetectionNetwork Simulation and Evaluation10.1007/978-981-97-4519-7_5(60-72)Online publication date: 2-Aug-2024
https://doi.org/10.1007/978-981-97-4519-7_5
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Index Terms

Fighting online click-fraud using bluff ads
1. Applied computing
  1. Law, social and behavioral sciences

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Published In

cover image ACM SIGCOMM Computer Communication Review

ACM SIGCOMM Computer Communication Review Volume 40, Issue 2

April 2010

75 pages

ISSN:0146-4833

DOI:10.1145/1764873

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Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 April 2010

Published in SIGCOMM-CCR Volume 40, Issue 2

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Liang YChen XChen YXiao PZhang J(2024)Mobile ad fraud: Empirical patterns in publisher and advertising campaign dataInternational Journal of Research in Marketing10.1016/j.ijresmar.2023.09.00341:2(265-281)Online publication date: Jun-2024
https://doi.org/10.1016/j.ijresmar.2023.09.003
Zhou SZhang HGao GSun YHuang HWang XWang Y(2024)KTSketch: Finding k-Persistent t-Spread Flows in High-Speed NetworksWeb and Big Data10.1007/978-981-97-7241-4_21(326-342)Online publication date: 31-Aug-2024
https://dl.acm.org/doi/10.1007/978-981-97-7241-4_21
Yu LLi SMeng YWang XZhu H(2024)Poisoning Attack in Machine Learning Based Invalid Ad Traffic DetectionNetwork Simulation and Evaluation10.1007/978-981-97-4519-7_5(60-72)Online publication date: 2-Aug-2024
https://doi.org/10.1007/978-981-97-4519-7_5
Ousat BTofighi MKharraz A(2023)An End-to-End Analysis of Covid-Themed Scams in the WildProceedings of the 2023 ACM Asia Conference on Computer and Communications Security10.1145/3579856.3582831(509-523)Online publication date: 10-Jul-2023
https://dl.acm.org/doi/10.1145/3579856.3582831
Joy BR D(2023)A Tensor Based Approach for Click Fraud Detection on Online Advertising Using BiLSTM and Attention based CNN2023 International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS)10.1109/ICSSAS57918.2023.10331862(669-674)Online publication date: 18-Oct-2023
https://doi.org/10.1109/ICSSAS57918.2023.10331862
Sisodia DSisodia D(2023)Stacked Generalization Architecture for Predicting Publisher Behaviour from Highly Imbalanced User-Click Data Set for Click Fraud DetectionNew Generation Computing10.1007/s00354-023-00218-141:3(581-606)Online publication date: 29-May-2023
https://dl.acm.org/doi/10.1007/s00354-023-00218-1
Jastrzębska AOwsiński JOpara KGajewski MHryniewicz OKozakiewicz MZadrożny SZwierzchowski TJastrzębska AOwsiński JOpara KGajewski MHryniewicz OKozakiewicz MZadrożny SZwierzchowski T(2023)The Problem and Its Key CharacteristicsAnalysing Web Traffic10.1007/978-3-031-32503-8_1(1-14)Online publication date: 27-Jun-2023
https://doi.org/10.1007/978-3-031-32503-8_1
Sisodia DSisodia D(2022)A hybrid data‐level sampling approach in learning from skewed user‐click data for click fraud detection in online advertisingExpert Systems10.1111/exsy.1314740:2Online publication date: 21-Sep-2022
https://doi.org/10.1111/exsy.13147
Liu YLiu YGheisari M(2022)CFDMA: A Novel Click Fraud Detection Method in Mobile Advertising2022 4th International Conference on Data Intelligence and Security (ICDIS)10.1109/ICDIS55630.2022.00066(394-401)Online publication date: Aug-2022
https://doi.org/10.1109/ICDIS55630.2022.00066
Sisodia DSisodia D(2022)Data Sampling Methods for Analyzing Publishers Conduct from Highly Imbalanced Dataset in Web AdvertisingInformation Systems and Management Science10.1007/978-3-031-13150-9_34(428-441)Online publication date: 29-Nov-2022
https://doi.org/10.1007/978-3-031-13150-9_34
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