Article

Impression fraud in online advertising via pay-per-view networks

Authors:

Kevin Springborn,

Paul BarfordAuthors Info & Claims

SEC'13: Proceedings of the 22nd USENIX conference on Security

Pages 211 - 226

Published: 14 August 2013 Publication History

Abstract

Advertising is one of the primary means for revenue generation for millions of websites and mobile apps. While the majority of online advertising revenues are based on pay-per-click, alternative forms such as impression-based display and video advertising have been growing rapidly over the past several years. In this paper, we investigate the problem of invalid traffic generation that aims to inflate advertising impressions on websites. Our study begins with an analysis of purchased traffic for a set of honeypot websites. Data collected from these sites provides a window into the basic mechanisms used for impression fraud and in particular enables us to identify pay-per-view (PPV) networks. PPV networks are comprised of legitimate websites that use JavaScript provided by PPV network service providers to render unwanted web pages "underneath" requested content on a real user's browser so that additional advertising impressions are registered. We describe the characteristics of the PPV network ecosystem and the typical methods for delivering fraudulent impressions. We also provide a case study of scope of PPV networks in the Internet. Our results show that these networks deliver hundreds of millions of fraudulent impressions per day, resulting in hundreds of millions of lost advertising dollars annually. Characteristics unique to traffic delivered via PPV networks are also discussed. We conclude with recommendations for countermeasures that can reduce the scope and impact of PPV networks.

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Cited By

Pastor APärssinen MCallejo PVallina PCuevas RCuevas ÁKotila MAzcorra A(2019)Nameles: An intelligent system for Real-Time Filtering of Invalid Ad TrafficThe World Wide Web Conference10.1145/3308558.3313601(1454-1464)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313601
Dong FWang HLi LGuo YBissyandé TLiu TXu GKlein JLeavens GGarcia APăsăreanu C(2018)FraudDroid: automated ad fraud detection for Android appsProceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3236024.3236045(257-268)Online publication date: 26-Oct-2018
https://dl.acm.org/doi/10.1145/3236024.3236045
Kim IWang WKwon YZheng YAafer YMeng WZhang XChampin PGandon FMédini LLalmas MIpeirotis P(2018)AdBudgetKillerProceedings of the 2018 World Wide Web Conference10.1145/3178876.3186096(297-307)Online publication date: 10-Apr-2018
https://dl.acm.org/doi/10.1145/3178876.3186096
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Information

Published In

cover image Guide Proceedings

SEC'13: Proceedings of the 22nd USENIX conference on Security

August 2013

702 pages

ISBN:9781931971034

Program Chair:
Sam King
University of Illinois and Adrenaline Mobility

Sponsors

Akamai: Akamai
Google Inc.
IBMR: IBM Research
NSF
Microsoft Reasearch: Microsoft Reasearch

Publisher

USENIX Association

United States

Publication History

Published: 14 August 2013

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Cited By

Pastor APärssinen MCallejo PVallina PCuevas RCuevas ÁKotila MAzcorra A(2019)Nameles: An intelligent system for Real-Time Filtering of Invalid Ad TrafficThe World Wide Web Conference10.1145/3308558.3313601(1454-1464)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313601
Dong FWang HLi LGuo YBissyandé TLiu TXu GKlein JLeavens GGarcia APăsăreanu C(2018)FraudDroid: automated ad fraud detection for Android appsProceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3236024.3236045(257-268)Online publication date: 26-Oct-2018
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Kim IWang WKwon YZheng YAafer YMeng WZhang XChampin PGandon FMédini LLalmas MIpeirotis P(2018)AdBudgetKillerProceedings of the 2018 World Wide Web Conference10.1145/3178876.3186096(297-307)Online publication date: 10-Apr-2018
https://dl.acm.org/doi/10.1145/3178876.3186096
Kline JBarford PCahn ASommers JUhlig SMaennel O(2017)On the structure and characteristics of user agent stringProceedings of the 2017 Internet Measurement Conference10.1145/3131365.3131406(184-190)Online publication date: 1-Nov-2017
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DeBlasio JGuha SVoelker GSnoeren AUhlig SMaennel O(2017)Exploring the dynamics of search advertiser fraudProceedings of the 2017 Internet Measurement Conference10.1145/3131365.3131393(157-170)Online publication date: 1-Nov-2017
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Chen YKintis PAntonakakis MNadji YDagon DLee WFarrell M(2016)Financial Lower Bounds of Online Advertising AbuseProceedings of the 13th International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment - Volume 972110.1007/978-3-319-40667-1_12(231-254)Online publication date: 7-Jul-2016
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Javed MHerley CPeinado MPaxson VCho KFukuda KPai VSpring N(2015)Measurement and Analysis of Traffic Exchange ServicesProceedings of the 2015 Internet Measurement Conference10.1145/2815675.2815708(1-12)Online publication date: 28-Oct-2015
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Pujol EHohlfeld OFeldmann ACho KFukuda KPai VSpring N(2015)Annoyed UsersProceedings of the 2015 Internet Measurement Conference10.1145/2815675.2815705(93-106)Online publication date: 28-Oct-2015
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