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 31000 unique phishing sites reported in June 2007 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 –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. Outline 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. Subject: Revision to Your Amazon.com Information Please login and enter your information http://www.amazon.com/exec/obidos/sign-in.html Embedded Training Example

19. Intervention #1 – Diagram

20. Explains why they are seeing this message

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

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

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

24. Intervention #2 – Comic Strip

27. Embedded Training Evaluation #1 Lab study comparing our prototypes to standard security notices –Group A – eBay, PayPal notices –Group B – Diagram that explains phishing –Group C – 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

28. Embedded Training Results

29. Existing practice of security notices is ineffective Diagram intervention somewhat better –Though people still fell for final phish Comic strip intervention worked best –Statistically significant –Combination of less text, graphics, story?

30. 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 –Also had people come back after 1 week To appear in APWG eCrime Researchers’ Summit (Oct 4-5 at CMU)

32. Results of Evaluation #2 Have to fall for phishing email to be effective? How well do people retain knowledge after a week?

33. Results of Evaluation #2 Have to fall for phishing email to be effective? How well do people retain knowledge after a week? Correctness

34. Results of Evaluation #2 Have to fall for phishing email to be effective? How well do people retain knowledge after a week? Correctness

38. Anti-Phishing Phil A game to teach people not to fall for phish –Embedded training focuses on email –Our game focuses on web browser 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

39. Anti-Phishing Phil

45. 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

46. Results No statistically significant difference in false negatives among the three groups –Actually a phish, but participant thinks it’s not –Unsure why, though game group had fewest false positives Press release last month, 50k new users –Still analyzing results –High knowledge retention 1 week later by participants –Faster at identifying phish (~12 seconds to ~6 seconds) Banks, non-profits, consulting firms, Air Force, ISPs

49. Outline 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?

50. Screenshots Internet Explorer – Passive Warning

51. Screenshots Internet Explorer – Active Block

52. Screenshots Mozilla FireFox – Active Block

53. 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 –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

54. How Effective are these Warnings?

56. 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

57. Screenshots Internet Explorer – Passive Warning

58. 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

59. Screenshots Internet Explorer – Active Block

60. A Science of Warnings See the warning? Understand? Believe it? Motivated? Planning on refining this model for computer warnings

61. Outline 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?

62. 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 W W W 2007.

63. PILFER Email Anti-Phishing Filter Heuristics combined in SVM –IP addresses in link (http://128.23.34.45/blah)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

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

65. PILFER Evaluation

66. PILFER now implemented as SpamAssassin filter Alas, Ian has left for Google

67. Outline 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 How good is phish detection for web sites? Can we do better?

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

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

70. 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.

71. Testbed System Architecture

72. Results 38% false positives 1% false positives PhishTank

73. APWG

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

75. 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 W W W 2007.

76. 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”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

77. 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 FakeReal

78. 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

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

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

83. Evaluating CANTINA PhishTank

84. Weaknesses in CANTINA Bad guys may try to subvert search engines Only works if legitimate page is indexed –Intranets May be confused if same login page in multiple places

85. 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

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

87. C MU U sable P rivacy and S ecurity Laboratory http://cups.cs.cmu.edu/