Abstract | ||
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Advertising plays a vital role in supporting free websites and smartphone apps. Click-spam, i.e., fraudulent or invalid clicks on online ads where the user has no actual interest in the advertiser's site, results in advertising revenue being misappropriated by click-spammers. While ad networks take active measures to block click-spam today, the effectiveness of these measures is largely unknown. Moreover, advertisers and third parties have no way of independently estimating or defending against click-spam. In this paper, we take the first systematic look at click-spam. We propose the first methodology for advertisers to independently measure click-spam rates on their ads. We also develop an automated methodology for ad networks to proactively detect different simultaneous click-spam attacks. We validate both methodologies using data from major ad networks. We then conduct a large-scale measurement study of click-spam across ten major ad networks and four types of ads. In the process, we identify and perform in-depth analysis on seven ongoing click-spam attacks not blocked by major ad networks at the time of this writing. Our findings highlight the severity of the click-spam problem, especially for mobile ads. |
Year | DOI | Venue |
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2012 | 10.1145/2342356.2342394 | SIGCOMM |
Keywords | Field | DocType |
major ad network,online ad,click-spam rate,advertising revenue,ongoing click-spam attack,different simultaneous click-spam attack,click-spam problem,ad network,fingerprinting click-spam,automated methodology,mobile ad,click fraud | Revenue,Internet privacy,Share of voice,Computer security,Computer science,Computer network,Online advertising,Click fraud | Conference |
Volume | Issue | ISSN |
42 | 4 | 0146-4833 |
Citations | PageRank | References |
52 | 2.06 | 17 |
Authors | ||
3 |
Name | Order | Citations | PageRank |
---|---|---|---|
Vacha Dave | 1 | 266 | 10.12 |
Saikat Guha | 2 | 1546 | 116.91 |
Yin Zhang | 3 | 3492 | 281.04 |