1. User Interfaces and Algorithms
for Fighting Phishing
Jason I. Hong
Carnegie Mellon University

2. Everyday Privacy and Security Problem

3. This entire process
known as phishing

4. Phishing is a Plague on the Internet
• Estimated 3.5 million people have fallen for phishing
• Estimated $350m-$2b direct losses a year
• 9255 unique phishing sites reported in June 2006
• Easier (and safer) to phish than rob a bank

5. Project: Supporting Trust Decisions
• Goal: help people make better online trust decisions
– Currently focusing on anti-phishing
• Large multi-disciplinary team project at CMU
– Six faculty, five PhD students, undergrads, staff
– Computer science, human-computer interaction,
public policy, social and decision sciences, CERT

6. Our Multi-Pronged Approach
• Human side
– Interviews 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
Automate where possible, support where necessary

7. Our Multi-Pronged Approach
• Human side
– Interviews 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
What do users know about phishing?

8. Interview Study
• Interviewed 40 Internet users (35 non-experts)
• “Mental models” interviews included email
role play and open ended questions
• Brief overview of results (see paper for details)
J. Downs, M. Holbrook, and L. Cranor. Decision Strategies
and Susceptibility to Phishing. In Proceedings of the
2006 Symposium On Usable Privacy and Security, 12-14
July 2006, Pittsburgh, PA.

9. Little Knowledge of Phishing
• Only about half knew meaning of the term “phishing”
“Something to do with the band Phish, I take it.”

10. Little Attention Paid to URLs
• Only 55% of participants said they had ever
noticed an unexpected or strange-looking URL
• Most did not consider them to be suspicious

11. Some Knowledge of Scams
• 55% of participants reported being cautious
when email asks for sensitive financial info
– But very few reported being suspicious of email
asking for passwords
• Knowledge of financial phish reduced likelihood
of falling for these scams
– But did not transfer to other scams, such as an
amazon.com password phish

12. Naive Evaluation Strategies
• The most frequent strategies don’t help much
in identifying phish
– This email appears to be for me
– It’s normal to hear from companies you do business with
– Reputable companies will send emails
“I will probably give them the information that they asked for.
And I would assume that I had already given them that
information at some point so I will feel comfortable giving it to
them again.”

13. Summary of Findings
• People generally not good at identifying scams
they haven’t specifically seen before
• People don’t use good strategies to protect
themselves
• Currently running large-scale survey across
multiple cities in the US to gather more data
• Amazon also active in looking for fake domain names

14. Our Multi-Pronged Approach
• Human side
– Interviews 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
Can we train people not to fall for phish?

15. Web Site Training Study
• Laboratory study of 28 non-expert computer users
• Asked participants to evaluate 20 web sites
– Control group evaluated 10 web sites, took 15 min break to
read email or play solitaire, evaluated 10 more web sites
– Experimental group same as above, but spent 15 min break
reading web-based training materials
• Experimental group performed significantly better
identifying phish after training
– Less reliance on “professional-looking” designs
– Looking at and understanding URLs
– Web site asks for too much information
People can learn from web-based training materials,
if only we could get them to read them!

16. How Do We Get People Trained?
• Most people don’t proactively look for training
materials on the web
• Companies send “security notice” emails to
employees and/or customers
• We hypothesized these tend to be ignored
– Too much to read
– People don’t consider them relevant
– People think they already know how to protect themselves
• Led us to idea of embedded training

17. Embedded Training
• Can we “train” people during their normal use of
email to avoid phishing attacks?
– Periodically, people get sent a training email
– Training email looks like a phishing attack
– If person falls for it, intervention warns and highlights
what cues to look for in succinct and engaging format
P. Kumaraguru, Y. Rhee, A. Acquisti, L. Cranor, J.
Hong, and E. Nunge. Protecting People from
Phishing: The Design and Evaluation of an
Embedded Training Email System. CHI 2007.

18. Embedded training example
Subject: Revision to Your Amazon.com Information
Please login and enter your information
http://www.amazon.com/exec/obidos/sign-in.html

19. Intervention #1 – Diagram

20. Intervention #1 – Diagram
Explains why they are
seeing this message

21. Intervention #1 – Diagram
Explains what a
phishing scam is

22. Intervention #1 – DiagramExplains how to identify
a phishing scam

23. Intervention #1 – DiagramExplains simple things
you can do to protect self

24. Intervention #2 – Comic Strip

25. Embedded Training Evaluation #1
• Lab study comparing our prototypes to
standard security notices
– EBay, PayPal notices
– Intervention #1 – Diagram that explains phishing
– Intervention #2 – Comic strip that tells a story
• 10 participants in each condition (30 total)
– Screened so we only have novices
• Go through 19 emails, 4 phishing attacks
scattered throughout, 2 training emails too
– Role play as Bobby Smith at Cognix Inc

26. Embedded Training Results
• Existing practice of security notices is ineffective
• Diagram intervention somewhat better
• Comic strip intervention worked best
– Statistically significant
– Combination of less text, graphics, story?

27. Evaluation #2
• New questions:
– Have to fall for phishing email to be effective?
– How well do people retain knowledge?
• Roughly same experiment as before
– Role play as Bobby Smith at Cognix Inc, go thru 16 emails
– Embedded condition means have to fall for our email
– Non-embedded means we just send the comic strip
– Had people come back after 1 week
– Improved design of comic strip intervention
• To appear in APWG eCrime Researchers’ Summit
(Oct 4-5 at CMU)

29. Results of Evaluation #2
• Have to fall for phishing email to be effective?
• How well do people retain knowledge after a week?
0.07
0.18
0.64
0.14
0.04
0.68
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
before immediate delay
Training set
Meancorrectness
Non-embedded condition Embedded condition
Correctness

30. Results of Evaluation #2
• Have to fall for phishing email to be effective?
• How well do people retain knowledge after a week?
0.07
0.18
0.64
0.14
0.04
0.68
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
before immediate delay
Training set
Meancorrectness
Non-embedded condition Embedded condition
Correctness

31. Anti-Phishing Phil
• A game to teach people not to fall for phish
– Embedded training focuses on email
– Our game focuses on web browser, URLs
• Goals
– How to parse URLs
– Where to look for URLs
– Use search engines for help
• Try the game!
– http://cups.cs.cmu.edu/antiphishing_phil

32. Anti-Phishing Phil

38. Evaluation of Anti-Phishing Phil
• Test participants’ ability to identify phishing
web sites before and after training up to 15 min
– 10 web sites before training, 10 after, randomized order
• Three conditions:
– Web-based phishing education
– Printed tutorial of our materials
– Anti-phishing Phil
• 14 participants in each condition
– Screened out security experts
– Younger, college students

39. Results
• No statistically significant difference in
false negatives among the three groups
– Actually a phish, but participant thinks it’s not
– Unsure why, considering a larger online study
• Though game group had fewest false positives

42. Our Multi-Pronged Approach
• Human side
– Interviews 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
Do people see, understand,
and believe web browser warnings?

43. Screenshots
Internet Explorer – Passive Warning

44. Screenshots
Internet Explorer – Active Block

45. Screenshots
Mozilla FireFox – Active Block

46. How Effective are these Warnings?
• We 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
– Users were phished after purchases
– Real email accounts and personal information
– Spoofing eBay and Amazon (2 phish/user)
– We observed them interact with the warnings

47. How Effective are these Warnings?

48. Improving Phishing Indicators
• Passive warning failed for many reasons
– Didn’t interrupt the main task
– Wasn’t clear what the right action was
– Looked too much like other ignorable warnings
• Now looking at science of warnings
– How to create effective security warnings

49. Our Multi-Pronged Approach
• Human side
– Interviews 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
Can we automatically detect phish emails?

50. PILFER Email Anti-Phishing Filter
• Philosophy: automate where possible, support
where necessary
• Goal: Create email filter that detects phishing emails
– Spam filters well-explored, but how good for phishing?
– Can we create a custom filter for phishing?
• I. Fette, N. Sadeh, A. Tomasic. Learning to Detect
Phishing Emails. In WWW 2007.

51. PILFER Email Anti-Phishing Filter
• Heuristics combined in SVM
– IP addresses in link (http://128.23.34.45/blah)
– Age of linked-to domains (younger domains likely phishing)
– Non-matching URLs (ex. most links point to PayPal)
– “Click here to restore your account”
– HTML email
– Number of links
– Number of domain names in links
– Number of dots in URLs
(http://www.paypal.update.example.com/update.cgi)
– JavaScript
– SpamAssassin rating

52. PILFER Evaluation
• Ham corpora from SpamAssassin (2002 and 2003)
– 6950 good emails
• Phishingcorpus
– 860 phishing emails

53. PILFER Evaluation

54. PILFER Evaluation
• PILFER now implemented as SpamAssassin filter
• Alas, Ian has left for Google

55. Our Multi-Pronged Approach
• Human side
– Interviews 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
Can we do better in automatically
detecting phish web sites?

56. Lots of Phish Detection Algorithms
• Dozens of anti-phishing toolbars offered
– Built into security software suites
– Offered by ISPs
– Free downloads
– Built into latest version of popular web browsers
– 132 on download.com

57. Lots of Phish Detection Algorithms
• Dozens of anti-phishing toolbars offered
– Built into security software suites
– Offered by ISPs
– Free downloads
– Built into latest version of popular web browsers
– 132 on download.com
• But how well do they detect phish?
– Short answer: still room for improvement

58. Testing the Toolbars
• November 2006: Automated evaluation of 10 toolbars
– Used phishtank.com and APWG as source of phishing URLs
– Evaluated 100 phish and 510 legitimate sites
Y. Zhang, S. Egelman, L. Cranor, J. Hong. Phinding Phish:
An Evaluation of Anti-Phishing Toolbars. NDSS 2006.

59. Testbed System Architecture

60. Results
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 1 2 12 24
Time (hours)
Phishingsitescorrectlyidentified
SpoofGuard
EarthLink
Netcraft
Google
IE7
Cloudmark
TrustWatch
eBay
Netscape
McAfee
38% false positives
1% false positives
PhishTank

61. 0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 1 2 12 24
Time (hours)
Phishingsitescorrectlyidentified
SpoofGuard
EarthLink
Netcraft
Firefox w/Google
IE7
Cloudmark
TrustWatch
eBay
Netscape
CallingID
Firefox
APWG

62. Results
• Only one toolbar >90% accuracy (but high false positives)
• Several catch 70-85% of phish with few false positives

63. Results
• Only one toolbar >90% accuracy (but high false positives)
• Several catch 70-85% of phish with few false positives
• Can we do better?
– Can we use search engines to help find phish?
Y. Zhang, J. Hong, L. Cranor. CANTINA: A Content-
Based Approach to Detecting Phishing Web Sites. In
WWW 2007.

64. 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

65. Adapting TF-IDF for Anti-Phishing
• Can same basic approach be used for anti-phishing?
– Scammers often directly copy web pages
– With Google search engine, fake should have low page rank
Fake Real

66. How CANTINA Works
• Given a web page, calculate TF-IDF score for
each word in that page
• Take five words with highest TF-IDF weights
• Feed these five words into a search engine (Google)
• If domain name of current web page is in top N
search results, we consider it legitimate
– N=30 worked well
– No improvement by increasing N
• Later, added some heuristics to reduce false positives

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

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

71. Evaluating CANTINA
PhishTank

72. Summary
• Whirlwind tour of our work on anti-phishing
– Human side: how people make decisions, training, UIs
– Computer side: better algorithms for detecting phish
• More info about our work at cups.cs.cmu.edu

73. Acknowledgments
• Alessandro Acquisti
• Lorrie Cranor
• Sven Dietrich
• Julie Downs
• Mandy Holbrook
• Norman Sadeh
• Anthony Tomasic
Supported by NSF, ARO, CyLab, Portugal Telecom
• Serge Egelman
• Ian Fette
• Ponnurangam
Kumaraguru
• Bryant Magnien
• Elizabeth Nunge
• Yong Rhee
• Steve Sheng
• Yue Zhang

74. CMU Usable Privacy and Security
Laboratory
http://cups.cs.cmu.edu/

76. Embedded Training Results
0
10
20
30
40
50
60
70
80
90
1003:Phish
5:Training
7:Real
8:Spam
11:Training
12:Spam
13:Real
14:Phish
16:Phish
17:Phish
Emails which had links in them
Percentageofuserswhoclicked
onalink
Group A Group B Group C

77. Is it legitimate
Our label
Yes No
Yes True positive False positive
No False negative True negative

81. Minimal Knowledge of Lock Icon
“I think that it means secured, it symbolizes
some kind of security, somehow.”
• 85% of participants were aware of lock icon
• Only 40% of those knew that it was supposed
to be in the browser chrome
• Only 35% had noticed https, and many of those
did not know what it meant