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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, the authors take the first systematic look at click-spam. They propose the first methodology for advertisers to independently measure click-spam rates on their ads. They also develop an automated methodology for ad networks to proactively detect different simultaneous click-spam attacks.
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