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Unraveling One-Click Fraud Schemes: A Comprehensive Analysis

Explore the intricate world of one-click frauds through data mining and machine learning techniques. Discover vulnerabilities, economic incentives, and legal aspects while dissecting evidence of illicit activities. Gain insights into fraud profitability and field measurements.

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Unraveling One-Click Fraud Schemes: A Comprehensive Analysis

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  1. Dissecting One Click Frauds Authors: Nicolas Christin, Sally S. Yanagihara, Keisuke Kamataki Proceedings of the ACM CCS 2010 Reporter: Jing Chiu Advisor: Yuh-Jye Lee Email: D9815013@mail.ntust.edu.tw Data Mining & Machine Learning Lab

  2. Outlines • Introduction • One Click Fraud • Data Collection • Channel BBS • Koguma-neko Teikoku • Wan-Cli Zukan • Data Analysis • Infrastructural loopholes • Grouping miscreants • Evidence of other illicit activities • Economic Incentives • Cost-benefit analysis • Fraud profitability • Legal aspects • Field measurements • Conclusions Data Mining & Machine Learning Lab

  3. Introduction • One Click Frauds Data Mining & Machine Learning Lab

  4. Data Collection • 2 Channel BBS • The largest bulletin board in Japan • March 6, 2006 ~ October 26, 2009 • Koguma-neko Teikoku • Privately owned website • August 24, 2006 ~ August 14, 2009 • Wan-Cli Zukan • Privately owned website • September 6,2006 ~ October 26, 2009 Data Mining & Machine Learning Lab

  5. Data Collection (cont.) • Data parsing • Extracted attributes • Store to MySQL database Data Mining & Machine Learning Lab

  6. Data Collection (cont.) Data Mining & Machine Learning Lab

  7. Data Analysis • Infrastructural loopholes • Phone numbers • Bank • DNS registrars • DNS resellers • Grouping miscreants • Use undirected graph to represent the dataset • Fraud distribution • Evidence of other illicit activities • Eight blacklisting services and Google Safe Browsing Data Mining & Machine Learning Lab

  8. Economic Incentives • Cost-benefit analysis • Fraud profitability • Legal aspects • Field measurements Data Mining & Machine Learning Lab

  9. Conclusions • Collect and analyze a corpus of over 2,000 reported One Click Fraud incidents • Describe a number of potential vulnerabilities which be used for scam • Shows an important reason for why scam flourish Data Mining & Machine Learning Lab

  10. Thanks for your attention • Questions? Data Mining & Machine Learning Lab

  11. DNS Registrars • Top 10 popular registrars vs. Top 11 in One Click Frauds Data Mining & Machine Learning Lab

  12. DNS Resellers Data Mining & Machine Learning Lab

  13. Data Mining & Machine Learning Lab

  14. Fraud Distribution Data Mining & Machine Learning Lab

  15. Evidence of other illicit activities Data Mining & Machine Learning Lab

  16. Ten most common amounts of money requested Data Mining & Machine Learning Lab

  17. Press reports of One Click Fraud arrests Data Mining & Machine Learning Lab

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