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. 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

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? Correctness

30. Results of Evaluation #2 Have to fall for phishing email to be effective? How well do people retain knowledge after a week? 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)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 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 38% false positives 1% false positives PhishTank

61. 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”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 FakeReal

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

76. Embedded Training Results

77. Is it legitimate Our label YesNo YesTrue positiveFalse positive NoFalse negativeTrue 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