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Phishing Experiment Analysis: Effects of Late Credit Card Payment Behavior

Explore the correlation between late credit card payments and susceptibility to phishing scams. Analyzing data from a research experiment on online fraud victims and non-victims recruited via Mechanical Turk. Investigating financial risks, physical risks, and information risks associated with fraudulent activities. Discover significant correlations and potential preventive measures against phishing attacks. Machine learning efforts underway for further analysis.

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Phishing Experiment Analysis: Effects of Late Credit Card Payment Behavior

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  1. Male, late with your credit card payment, and like to speed? You will be phished. Markus Jakobsson

  2. Why are we asked if we smoke?

  3. Experiment: setup Recruit 500 subjects from Mechanical Turk using survey “Have you lost money to online fraud?” (1 cent) victims non-victims

  4. Experiment: setup Recruit 500 subjects from Mechanical Turk using survey “Have you lost money to online fraud?” (1 cent) Invite all “yes” (~150) and same number of “no”. victims non-victims

  5. Experiment: setup victims non-victims

  6. Experiment: setup Recruit 500 subjects from Mechanical Turk using survey “Have you lost money to online fraud?” (1 cent) Invite all “yes” (~150) and same number of “no”. Final numbers: 101 “yes”, 100 “no” victims non-victims

  7. Experiment: survey (demographics)

  8. Experiment: survey (physical risks)

  9. Experiment: survey (financial risks)

  10. Experiment: survey (information risks)

  11. Some results Same pwd many site Download free softw. Using public wifi Click link email Resp. unsolic. off. .23 .23 Invest real est. Invest stocks Invest bonds Not paying cc bal. Being late cc pay Save <5% retire Make inv. rec fr. .23 .27 .29 .27 .21 .28 .24 .23 .19 .27 .23 .33 .26 .32 .24 .30 .25 .26 .24 .27 .28 .25 .27 .30 Correlations significant on 0.05 level

  12. Some results - hard facts • Weaker, but still there - need more variables! • Example (with 0.01 significance) • “Other fraud” ~ “Not paying cc balance” (0.19) • ~ “Save less 5% retirement” (0.17) • Notable correlations between types of fraud • Some negative correlations! • Machine Learning efforts under way Thanks to Nathan Good for helpful discussions

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