Measuring and fingerprinting click-spam in ad networks

Dave, Vacha; Guha, Saikat; Zhang, Yin

doi:10.1145/2342356.2342394

Cited by 76 publications

(60 citation statements)

References 16 publications

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“…Rule-based techniques [39,22], correlation analysis [29,30,42], and bluff ads [16] have focused on detecting clicks from infected users before the advertiser is charged. Clustering [4] and anomaly detection [5] have focused on detecting malicious publishers.…”

Section: Web Advertising and Click Fraudmentioning

confidence: 99%

“…Moreover, by design, online advertising is a low-friction market designed to engage the broadest possible set of participants, and thus presents few barriers-to-entry for potential bad actors. Unsurprisingly, criminal groups have developed a range of techniques for generating synthetic advertisement clicks for profit at the expense of legitimate advertisers and ad networks [4]. Indeed, such click fraud accounts for as much as 10% of all advertising clicks, potentially defrauding advertisers of hundreds of millions of dollars annually, with some experts predicting the rate increasing by more than 50% per year [38].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Characterizing Large-Scale Click Fraud in ZeroAccess

Pearce

Dave

Grier

et al. 2014

Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security

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Click fraud is a scam that hits a criminal sweet spot by both tapping into the vast wealth of online advertising and exploiting that ecosystem's complex structure to obfuscate the flow of money to its perpetrators. In this work, we illuminate the intricate nature of this activity through the lens of ZeroAccess-one of the largest click fraud botnets in operation. Using a broad range of data sources, including peer-to-peer measurements, command-and-control teleme-try, and contemporaneous click data from one of the top ad networks , we construct a view into the scale and complexity of modern click fraud operations. By leveraging the dynamics associated with Microsoft's attempted takedown of ZeroAccess in December 2013, we employ this coordinated view to identify "ad units" whose traffic (and hence revenue) primarily derived from ZeroAccess. While it proves highly challenging to extrapolate from our direct observations to a truly global view, by anchoring our analysis in the data for these ad units we estimate that the botnet's fraudulent activities plausibly induced advertising losses on the order of $100,000 per day.

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Section: Web Advertising and Click Fraudmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Characterizing Large-Scale Click Fraud in ZeroAccess

Pearce

Dave

Grier

et al. 2014

Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security

Self Cite

View full text Add to dashboard Cite

show abstract

“…The actual behavioral targeting mechanisms are not known as they tend to be proprietary. Likewise, recent work has focused on the problem of combating clickspam [10] and characterizing mobile advertising [28]. In addition, past work has also focused on understanding the role online ads play in decision making [24,27] or how to properly set CPM values [7].…”

Section: Related Workmentioning

confidence: 99%

“…Most Web services 1 are offered for free to end users and these Web services operate by relying on revenues generated by online advertising. A lot of work has been done on different facets of online advertising: whether it be understanding the mechanisms used for advertising [25], privacy concerns [13] or combating click spam [10]. However, little is known about the economics of online advertising, chiefly the economics of collecting and using personal information of users for facilitating targeted advertising.…”

Section: Introductionmentioning

confidence: 99%

Best paper -- Follow the money

Gill

Erramilli

Chaintreau

et al. 2013

Proceedings of the 2013 Conference on Internet Measurement Conference

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The large-scale collection and exploitation of personal information to drive targeted online advertisements has raised privacy concerns. As a step towards understanding these concerns, we study the relationship between how much information is collected and how valuable it is for advertising. We use HTTP traces consisting of millions of users to aid our study and also present the first comparative study between aggregators. We develop a simple model that captures the various parameters of today's advertising revenues, whose values are estimated via the traces. Our results show that per aggregator revenue is skewed (5% accounting for 90% of revenues), while the contribution of users to advertising revenue is much less skewed (20% accounting for 80% of revenue). Google is dominant in terms of revenue and reach (presence on 80% of publishers). We also show that if all 5% of the top users in terms of revenue were to install privacy protection, with no corresponding reaction from the publishers, then the revenue can drop by 30%.

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“…The ad brokers may not be in favor of introducing an extra entity to share their ad targeting duty, which is the main source of their revenue. Similarly, the advertisers may be dissatisfied with the perturbed clicking information as perturbation undermines the accuracy of click information, which normally determines their payments [10]. Without guaranteed revenue, the advertisers and ad brokers naturally tend to maintain the adoption of traditional targeted advertising systems instead of upgrading the systems to provide privacy protection.…”

Section: Introductionmentioning

confidence: 99%