I discuss some ways of changing people's behaviors with respect to cybersecurity, based on research we did at Carnegie Mellon University.

1. Jason Hong, PhD
Carnegie Mellon University
Wombat Security Technologies
Achieving Behavioral
Change

2. Usable Privacy and Security
• We have done extensive research on usable
privacy and security at Carnegie Mellon
– Passwords, access control, privacy policies, etc
– http://cups.cs.cmu.edu/trust.php
• Today’s talk on behavioral change and phishing
– Why do people fall for these attacks?
– What demographics most vulnerable?
– What are weaknesses in user interfaces?
– Can we actually train people not to fall for phishing?

3. Some Results of Our Research
• Startup
– Customers of micro-games featured include
governments, financials, universities
– Our anti-phishing email filter is labeling several
million emails per day
• Study on browser warnings -> MSIE8
• Elements of our work adopted by
Anti-Phishing Working Group (APWG)
• Popular press article in
Scientific American

4. Two Case Studies + Opportunity
• How effective are web browser user interfaces
in protecting us from phishing scams?
• Can we actually train people to protect
themselves?
– What kinds of training effective? Ineffective?
– Which demographics most vulnerable?
• What do voting, saving energy, and re-using
towels have in common?

5. Everyday Privacy and Security Problem

6. General Patton is retiring next week,
click here to say whether you can
attend his retirement party
Phishing Increasing in Sophistication
Targeting Your Organization
• Spear-phishing targets specific groups or individuals
• Type #1 – Uses info about your organization

7. Phishing Increasing in Sophistication
Targeting Your Organization
• Around 40% of people in our experiments would fall
for emails like this (control condition)

8. Phishing Increasing in Sophistication
Targeting You Specifically
• Type #2 – Uses info specifically about you
– Social phishing
• Uses detailed information from social networking sites,
corporate directories, and publicly available data
• Ex. Fake emails from friends or co-workers
• Ex. Fake colonel (instructor) at West Point
• Ex. Fake videos of you and your friends
– Past studies indicate social phishing ~4.5x more effective

9. Phishing Increasing in Sophistication
Targeting You Specifically
Here’s a video I took of your
poster presentation.

10. Phishing Increasing in Sophistication
Targeting You Specifically
• Type #2 – Uses info specifically about you
– Whaling – focusing on big targets
Thousands of high-ranking executives
across the country have been receiving
e-mail messages this week that appear
to be official subpoenas from the United
States District Court in San Diego. Each
message includes the executive’s name,
company and phone number, and
commands the recipient to appear before
a grand jury in a civil case.
-- New York Times Apr16 2008

11. How Bad Is Phishing?
Perspective of Corporations
• Direct damage
– Loss of sensitive customer data

12. How Bad Is Phishing?
Perspective of Corporations
• Direct damage
– Loss of sensitive customer data
– Loss of intellectual property

13. How Bad Is Phishing?
Perspective of Corporations
• Direct damage
– Loss of sensitive customer data
– Loss of intellectual property
– Fraud
• Recent carbon trading incidents in EU partly due to phish
• Indirect damage
– Damage to reputation, lost sales, etc
– Response costs (call centers, recovery)
• One researcher half-joked that banks feared customer
call center costs more than phishers

14. Phishing Increasing in Sophistication
Combination with Malware
• Malware and phishing are becoming combined
– Poisoned attachments (Ex. custom PDF exploits)
– Links to web sites with malware (web browser exploits)
– Can install keyloggers or remote access software

15. Can Web Browser Interfaces Help?
• Newer web browsers come with blacklists
and special interfaces for identifying phish
– Our evaluation of several blacklists show they catch ~80%
of phish after 24 hours, not very good in first few hours
– Also only catch “shotgun phish” rather than spear-phish
• Are these browser interfaces effective?

16. Screenshots
Internet Explorer 7 – Passive Warning

17. Screenshots
Internet Explorer 7 – Active Block

18. Screenshots
Mozilla Firefox – Active Block

19. How Effective are these Warnings?
• Tested four conditions
– FireFox Active Block
– IE Active Block
– IE Passive Warning
– Control (no warnings or blocks)
• “Shopping Study”
– Setup some fake phishing pages and added to blacklists
– We phished users after real purchases (2 phish/user)
– Used real email accounts and personal information
S. Egelman, L. Cranor, and J. Hong. You've Been Warned: An
Empirical Study of the Effectiveness of Web Browser Phishing
Warnings. CHI 2008.

20. How Effective are these Warnings?
Almost everyone clicked, even those
with strong technical backgrounds

21. How Effective are these Warnings?
• No one in Firefox condition fell for our phish
• People in Firefox condition not more technically savvy

22. Discussion of Phish Warnings
• Nearly everyone will fall for highly targeted
and contextualized phish
• Passive IE warning failed for many reasons
– Didn’t interrupt the main task
– Can be slow to appear (up to 5 seconds)
– Not clear what the right action was
– Looked too much like other ignorable warnings
(habituation)
– Bug in implementation, any keystroke dismissed

23. Screenshots
Internet Explorer – Passive Warning

24. Discussion of Phish Warnings
• Active IE warnings
– Most saw the warning, but many did not believe it
• “Since it gave me the option of still proceeding
to the website, I figured it couldn’t be that bad”
– Some element of habituation (looks like other
warnings)
– Saw two pathological cases

25. Screenshots
Internet Explorer – Active Block

26. IE8 Re-designed Based on our Work

27. A Science of
Warnings
• C-HIP model for real-
world warnings
– See the warning?
– Understand it?
– Believe it?
– Motivated?
– Can and will act?

28. Designing for Path of Least Resistance
• Where possible, make the default
behavior the safe behavior
– Ex. The two pathological cases
– Assume people won’t see, read,
believe, or be motivated by warnings
• Active warnings over passive warnings
– Interrupt people if warning is important
– Need to balance this with habituation
• Make important warnings look very different

29. Two Case Studies + Opportunity
• How effective are web browser user interfaces
in protecting us from phishing scams?
• Can we actually train people to protect
themselves?
– What kinds of training effective? Ineffective?
• What do voting, saving energy, and re-using
towels have in common?

30. Can We Educate End-Users?
• Users are not motivated to learn about security
• Security is a secondary task
• Difficult to teach people to make right online trust
decision without increasing false positives
“User education is a complete waste of time. It is
about as much use as nailing jelly to a wall…. They
are not interested…they just want to do their job.”
Martin Overton, IBM security specialist
http://news.cnet.com/21007350_361252132.html

31. Yes, End-Users Are Trainable
• Our research demonstrates that users can learn
techniques to protect themselves from phishing…
if you can get them to pay attention to training
• Problem is that today’s training often boring,
time consuming, and ineffective
– All day lecture, but no chance to practice skills
– Or read text online and take very basic quizzes
– Or passively watching videos
– Or posters and mugs and calendars
– Raise awareness, but little on what to actually do
P. Kumaraguru, S. Sheng, A. Acquisti, L. Cranor, and J. Hong.
Teaching Johnny Not to Fall for Phish. CyLab Technical Report
CMU CyLab07003, 2007.

32. How Do We Get People Trained?
• Create “teachable moments”: PhishGuru
• Make training fun: Anti-Phishing Phil
• Use learning science principles throughout
– Ex. Concrete-Abstract, Multimedia, Immediate Feedback
PhishGuru Anti-Phishing Phil

33. PhishGuru Embedded Training
• Send emails that look like a phishing attack
• If recipient falls for it, show intervention that teaches
what cues to look for in succinct and engaging format
– Useful for people who don’t know that they don’t know
• Multiple user studies have demonstrated
that PhishGuru is effective
• Delivering same training via direct email is
not effective!

34. Subject: Revision to Your Amazon.com InformationSubject: Revision to Your Amazon.com Information

35. Subject: Revision to Your Amazon.com InformationSubject: Revision to Your Amazon.com Information
Please login and enter your informationPlease login and enter your information

37. Evaluation of PhishGuru
• Is embedded training effective?
– Study 1: Lab study, 30 participants
– Study 2: Lab study, 42 participants
– Study 3: Field trial at company, ~300 participants
– Study 4: Field trial at CMU, ~500 participants
• Studies showed significant decrease in falling for
phish and ability to retain what they learned
P. Kumaraguru et al. Protecting People from Phishing: The Design and
Evaluation of an Embedded Training Email System. CHI 2007.
P. Kumaraguru et al. Getting Users to Pay Attention to Anti-Phishing
Education: Evaluation of Retention and Transfer. eCrime 2007.

38. Study #4 at CMU
• Investigate effectiveness and retention of
training after 1 week, 2 weeks, and 4 weeks
• Compare effectiveness of 2 training
messages vs 1 training message
• Examine demographics and phishing
P. Kumaraguru, J. Cranshaw, A. Acquisti, L. Cranor, J. Hong,
M. A. Blair, and T. Pham. School of Phish: A Real-World Evaluation
of Anti-Phishing Training. 2009. SOUPS 2009.

39. Study design
• Sent email to all CMU students, faculty
and staff to recruit participants (opt-in)
• 515 participants in three conditions
– Control / One training message / Two messages
• Emails sent over 28 day period
– 7 simulated spear-phishing messages
– 3 legitimate (cyber security scavenger hunt)
• Campus help desks and IT departments
notified before messages sent

40. Effect of PhishGuru Training
Condition N % who clicked
on Day 0
% who
clicked on
Day 28
Control 172 52.3 44.2
Trained 343 48.4 24.5

41. Pop Quiz
• Which group most vulnerable to phishing attacks?
– 18-25
– 26-35
– 36-45
– 45+

42. Surprisingly, Students Most Vulnerable
• Students significantly more likely to fall for phish
than staff before training
• No significant differences based on student year,
department, or gender
• 18-25 age group most vulnerable
Age group Day 0 Day 28
18-25 62% 36%
26-35 48% 16%
36-45 33% 18%
45 and older 43% 10%

43. Discussion of PhishGuru
• PhishGuru can teach people to identify phish better
– People retain the knowledge
• People trained on first day less likely to be phished
• Two training messages work better
– People weren’t less likely to click on legitimate emails
– People aren’t resentful, many happy to have learned
• 68 out of 85 surveyed said they recommend CMU
continue doing this sort of training in future
• “I really liked the idea of sending CMU students fake
phishing emails and then saying to them, essentially,
HEY! You could've just gotten scammed! You should
be more careful -- here's how....”

44. APWG Landing Page
• CMU and Wombat helped Anti-Phishing Working
Group develop landing page for taken down sites
– Already in use by several takedown companies
– Seen by ~200,000 people in past 27 months

45. Two Case Studies + Opportunity
• How effective are web browser user interfaces
in protecting us from phishing scams?
• Can we actually train people to protect
themselves?
– What kinds of training effective? Ineffective?
• What do voting, saving energy, and re-using
towels have in common?

46. Voting and Saving Energy
• Many economists say that voting is completely
irrational behavior
– Odds of one vote making a difference is close to zero
– But, strong predictor of whether someone votes or not is
how many other people they know that vote
• Many people say they conserve energy because
– Environmental protection, benefit to society, saving money
– But, strongest predictor is if you believe everyone else is too
– And, strongest intervention is telling people all their
neighbors are saving energy too
– Similar results for recycling, reusing towels
• Is there an opportunity here for improving security?

47. Prize-Linked Lotteries
• Most Americans don’t save enough money
• But average American household spends $500 on
lottery tickets
– Estimates are that 80% of lottery revenue comes
from households of $50k and under
• Prize-Linked Lottery
– Every $25 you save, you get a lottery ticket from bank
– Grand prize of $100k per year, smaller prizes throughout
– Dramatically increased rates of savings
• Better than a CD with 10% interest!
• Is there an opportunity here for improving security?

48. Open Challenge for Computer Security
• Incorporate more human behavioral science into
how we operate
– In terms of how security policies set
– In terms of how products are designed
– Hopefully, I’ve demonstrated (potential) utility
– Lots of untapped potential with even simple approaches
• Challenge here is “magic black box” mentality
– At RSA, lots of technical and marketing people, all think alike
– Not enough about user interfaces, incentives, how small
groups work, how people make decisions, etc

49. Summary
• Browser warnings
– Focus on path of least resistance
– See, understand, believe, motivated?
• Anti-phishing training
– Create teachable moments
– Use learning science
• Behavioral sciences offer many untapped
opportunities
• Can try PhishGuru, Phil, and Phyllis at:
www.wombatsecurity.com

50. Acknowledgments
• Ponnurangam Kumaraguru
• Steve Sheng
• Lorrie Cranor
• Norman Sadeh
Thanks Everyone!

53. Anti-Phishing Phil
• A micro-game to teach people not to fall for phish
– PhishGuru about email, this game about web browser
– Also based on learning science principles
• Goals
– How to parse URLs
– Where to look for URLs
– Use search engines for help
• Try the game!
– Search for “phishing game”
S. Sheng et al. Anti-Phishing Phil: The Design and Evaluation of a
Game That Teaches People Not to Fall for Phish. In SOUPS 2007,
Pittsburgh, PA, 2007.

54. Anti-Phishing Phil

60. Evaluation of Anti-Phishing Phil
• Is Phil effective? Yes!
– Study 1: 56 people in lab study
– Study 2: 4517 people in field trial
• Brief results of Study 1
– Phil about as effective in helping people detect phishing
web sites as paying people to read training material
– But Phil has significantly fewer false positives overall
• Suggests that existing training material making people
paranoid about phish rather than differentiating

61. Evaluation of Anti-Phishing Phil
• Study 2: 4517 participants in field trial
– Randomly selected from 80000 people
• Conditions
– Control: Label 12 sites then play game
– Game: Label 6 sites, play game, then label 6 more,
then after 7 days, label 6 more (18 total)
• Participants
– 2021 people in game condition, 674 did retention portion

62. Anti-Phishing Phil: Study 2
• Novices showed most improvement in false negatives
(calling phish legitimate)

63. Anti-Phishing Phil: Study 2
• Improvement all around for false positives

64. Anti-Phishing Phyllis
• New micro-game just released by Wombat Security
• Focuses on teaching people about what cues
to look for in emails
– Some emails are legitimate, some fake
– Have to identify cues as dangerous or harmless

65. Tells people why they are
seeing this message, uses
engaging character
Tells people why they are
seeing this message, uses
engaging character

66. Tells a story about what
happened and what the
risks are
Tells a story about what
happened and what the
risks are

67. Gives concrete examples of
how to protect oneself
Gives concrete examples of
how to protect oneself

68. Explains how criminals conduct
phishing attacks
Explains how criminals conduct
phishing attacks

71. How Bad Is Phishing?
Consumer Perspective
• Estimated ~0.5% of Internet users per year
fall for phishing attacks
• Conservative $1B+ direct losses a year to consumers
– Bank accounts, credit card fraud
– Doesn’t include time wasted on recovery of funds,
restoring computers, emotional uncertainty
• Growth rate of phishing
– 30k+ reported unique emails / month
– 45k+ reported unique sites / month
• Social networking sites now major targets

72. This entire process
known as phishing