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Click Fraud Detection on the Advertiser Side

  • Haitao Xu
  • Daiping Liu
  • Aaron Koehl
  • Haining Wang
  • Angelos Stavrou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8713)

Abstract

Click fraud—malicious clicks at the expense of pay-per-click advertisers—is posing a serious threat to the Internet economy. Although click fraud has attracted much attention from the security community, as the direct victims of click fraud, advertisers still lack effective defense to detect click fraud independently. In this paper, we propose a novel approach for advertisers to detect click frauds and evaluate the return on investment (ROI) of their ad campaigns without the helps from ad networks or publishers. Our key idea is to proactively test if visiting clients are full-fledged modern browsers and passively scrutinize user engagement. In particular, we introduce a new functionality test and develop an extensive characterization of user engagement. Our detection can significantly raise the bar for committing click fraud and is transparent to users. Moreover, our approach requires little effort to be deployed at the advertiser side. To validate the effectiveness of our approach, we implement a prototype and deploy it on a large production website; and then we run 10-day ad campaigns for the website on a major ad network. The experimental results show that our proposed defense is effective in identifying both clickbots and human clickers, while incurring negligible overhead at both the server and client sides.

Keywords

Click Fraud Online Advertising Feature Detection 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Haitao Xu
    • 1
  • Daiping Liu
    • 1
  • Aaron Koehl
    • 1
  • Haining Wang
    • 1
  • Angelos Stavrou
    • 2
  1. 1.College of William and MaryWilliamsburgUSA
  2. 2.George Mason UniversityFairfaxUSA

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