This document discusses research into detecting and preventing phishing attacks. It begins by outlining the negative impacts of phishing from consumer, corporate, and societal perspectives. It then describes how phishing attacks are becoming more sophisticated through spear phishing, social engineering, and the use of malware. The document outlines various techniques developed by the author and his colleagues to educate users and automatically detect phishing attacks, including embedded phishing training software called PhishGuru, educational microgames like Anti-Phishing Phil, analyzing search engine results to detect fake websites, and using machine learning to label new phishing sites based on similarities to known ones. User studies demonstrated that embedded, game-based training can significantly reduce users falling for phishing

1. Jason Hong, PhD
Carnegie Mellon University
Wombat Security Technologies
Teaching Johnny Not
to Fall for Phish

2. Everyday Privacy and Security Problem

3. This entire process
known as phishing

4. 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

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

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

7. How Bad Is Phishing?
Perspective of Corporations
• Direct damage
– Loss of sensitive customer data
– Loss of intellectual property
– Fraud
– Disruption of network services
• Indirect damage
– Damage to reputation, lost sales, etc
– Response costs (call centers, recovery)
• One bank estimated it cost them $1M per phishing attack

8. 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

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

10. Phishing Increasing in Sophistication
Targeting You Specifically
• Type #2 – Uses info specifically about you
– Social phishing
• Might use information from social networking sites,
corporate directories, or publicly available data
• Ex. Fake email from friends or co-workers
• Ex. Fake videos of you and your friends

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

12. 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

13. 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. Protecting People from Phishing
• Human side
– Interviews and surveys to understand decision-making
– PhishGuru embedded training
– Micro-games for security training
– Understanding effectiveness of browser warnings
• Computer side
– PILFER email anti-phishing filter
– CANTINA web anti-phishing algorithm
– Machine learning of blacklists
– Social web + machine learning to combat scams

16. Results of Our Research
• Startup
– Customers of micro-games featured include
governments, financials, universities
– Our 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

17. Outline of Rest of Talk
• Rest of talk will focus on educating end-users
• PhishGuru embedded training
• Anti-Phishing Phil micro-game
• Anti-Phishing Phyllis micro-game

18. User Education is Challenging
• 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

19. But Actually, 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
P. Kumaraguru, S. Sheng, A. Acquisti, L. Cranor, and J. Hong.
Teaching Johnny Not to Fall for Phish. CyLab Technical Report
CMU CyLab07003, 2007.

20. How Do We Get People Trained?
• Solution
– Find “teachable moments”: PhishGuru
– Make training fun: Anti-Phishing Phil,
Anti-Phishing Phyllis
– Use learning science principles

21. 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
• Multiple user studies have demonstrated
that PhishGuru is effective
• Delivering same training via direct email is
not effective!

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

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

25. 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.

26. 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.

27. 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

28. 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

29. 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....”
• Contrast to US DOJ and Guam

30. 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 20 months

31. 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.

32. Anti-Phishing Phil

38. 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

39. 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

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

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

42. 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

49. Summary
• Phishing is already a plague on the Internet
– Seriously affects consumers, businesses, governments
– Criminals getting more sophisticated
• End-users can be trained, but only if done right
– PhishGuru embedded training uses simulated phishing
– Anti-Phishing Phil and Anti-Phishing Phyllis micro-games
• Can try PhishGuru, Phil, and Phyllis at:
www.wombatsecurity.com
• Will show free demo of Phil and Phyllis to anyone
who can explain to me what’s going on in Lost

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

52. Screenshots
Internet Explorer – Passive Warning

53. Screenshots
Internet Explorer – Active Block

54. Screenshots
Mozilla FireFox – Active Block

55. 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 purchases (2 phish/user)
– 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.

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

57. How Effective are these Warnings?

58. Discussion of Phish Warnings
• Nearly everyone will fall for highly contextual phish
• Passive IE warning failed for many reasons
– Didn’t interrupt the main task
– 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 dismisses

59. Screenshots
Internet Explorer – Passive Warning

60. Discussion of Phish Warnings
• Active IE warnings
– Most saw but 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

61. Screenshots
Internet Explorer – Active Block

62. Internet Explorer 8 Re-design

63. A Science of
Warnings
• See the warning?
• Understand?
• Believe it?
• Motivated?
• Can and will act?
• Refining this model for
computer warnings

64. Outline
• Human side
– Interviews and surveys to understand decision-making
– PhishGuru embedded training
– Anti-Phishing Phil game
– Understanding effectiveness of browser warnings
• Computer side
– PILFER email anti-phishing filter
– CANTINA web anti-phishing algorithm
– Machine learning of blacklists
Can we improve phish detection
of web sites?

65. Detecting Phishing Web Sites
• Industry uses blacklists to label phishing sites
– But blacklists slow to new attacks
• Idea: Use search engines
– Scammers often directly copy web pages
– But fake pages should have low PageRank on search engines
– Generate text-based “fingerprint” of web page keywords and
send to a search engine
Y. Zhang, S. Egelman, L. Cranor, and J. Hong Phinding Phish:
Evaluating Anti-Phishing Tools. In NDSS 2007.
Y. Zhang, J. Hong, and L. Cranor. CANTINA: A content-based
approach to detecting phishing web sites. In WWW 2007.
G. Xiang and J. Hong. A Hybrid Phish Detection Approach by Identity
Discovery and Keywords Retrieval. In WWW 2009.

66. Robust Hyperlinks
• Developed by Phelps and Wilensky to solve
“404 not found” problem
• Key idea was to add a lexical signature to URLs
that could be fed to a search engine if URL failed
– Ex. http://abc.com/page.html?sig=“word1+word2+...+word5”
• How to generate signature?
– Found that TF-IDF was fairly effective
• Informal evaluation found five words was sufficient
for most web pages

67. Fake
eBay, user, sign, help, forgot

68. Real
eBay, user, sign, help, forgot

71. Evaluating CANTINA
PhishTank

72. Machine Learning of Blacklists
• Human-verified blacklists maintained by Microsoft,
Google, PhishTank
– Pros: Reliable, extremely low false positives
– Cons: Slow to respond, can be flooded with URLs (fast flux)
• Observation #1: many phishing sites similar
– Constructed through toolkits
• Observation #2: many phishing sites similar
– Fast flux (URL actually points to same site)
• Idea: Rather than just examining URL, compare
content of a site to known phishing sites

73. Machine Learning of Blacklists
• Approach #1: Use hashcodes of web page
– Simple, good against fast flux
– Easy to defeat (though can allow some flexibility)
• Approach #2: Use shingling
– Shingling is an approach used by search engines to find
duplicate pages
– “connect with the eBay community” ->
{connect with the, with the eBay, the eBay community}
– Count the number of common shingles out of total shingles,
set threshold

74. Machine Learning of Blacklists
• Use Shingling
• Protect against false positives
– Phishing sites look a lot like real sites
– Have a small whitelist (ebay, paypal, etc)
– Use CANTINA too

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

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

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

78. Explains how criminals conduct
phishing attacks
Explains how criminals conduct
phishing attacks