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| Content Provider | ACM Digital Library |
|---|---|
| Author | Guha, Saikat Zhang, Yin Dave, Vacha |
| Abstract | 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. |
| Starting Page | 175 |
| Ending Page | 186 |
| Page Count | 12 |
| File Format | |
| ISSN | 01464833 |
| DOI | 10.1145/2377677.2377715 |
| Journal | ACM SIGCOMM Computer Communication Review (CCRV) |
| Volume Number | 42 |
| Issue Number | 4 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 1993-07-01 |
| Publisher Place | New York |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Invalid clicks Traffic quality Click fraud Advertising fraud Click-spam |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Networks and Communications Software |
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