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 2015/9/8 1 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 2015/9/8 2 Data Mining & Machine Learning Lab

3. One Click Frauds Introduction 2015/9/8Data Mining & Machine Learning Lab 3

4. 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 Collection 2015/9/8Data Mining & Machine Learning Lab 4

5. Data parsing Extracted attributes Store to MySQL database Data Collection (cont.) 2015/9/8Data Mining & Machine Learning Lab 5

6. Data Collection (cont.) 2015/9/8Data Mining & Machine Learning Lab 6

7. Infrastructural loopholes ▫Phone numbers ▫Bank ▫DNS registrarsDNS registrars ▫DNS resellersDNS resellers Grouping miscreants ▫Use undirected graph to represent the datasetUse undirected graph to represent the dataset ▫Fraud distributionFraud distribution Evidence of other illicit activities ▫Eight blacklisting services and Google Safe BrowsingEight blacklisting services and Google Safe Browsing Data Analysis 2015/9/8Data Mining & Machine Learning Lab 7

8. Cost-benefit analysis Fraud profitability Legal aspects Field measurements Economic Incentives 2015/9/8Data Mining & Machine Learning Lab 8

9. 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 Conclusions 2015/9/8Data Mining & Machine Learning Lab 9

10. Questions? Thanks for your attention 2015/9/8Data Mining & Machine Learning Lab 10

11. Top 10 popular registrars vs. Top 11 in One Click Frauds DNS Registrars 2015/9/8Data Mining & Machine Learning Lab 11

12. DNS Resellers 2015/9/8Data Mining & Machine Learning Lab 12

13. 2015/9/8Data Mining & Machine Learning Lab 13

14. Fraud Distribution 2015/9/8Data Mining & Machine Learning Lab 14

15. Evidence of other illicit activities 2015/9/8Data Mining & Machine Learning Lab 15

16. Ten most common amounts of money requested 2015/9/8Data Mining & Machine Learning Lab 16

17. Press reports of One Click Fraud arrests 2015/9/8Data Mining & Machine Learning Lab 17