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user interfaces and algorithms

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user interfaces and algorithms

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    1. User Interfaces and Algorithms for Fighting Phishing

    Jason I. Hong Carnegie Mellon University

    2. Everyday Privacy and Security Problem

    This entire process known as phishing

    4. Fast Facts on Phishing

    Estimated 3.5 million people have fallen for phishing Estimated to cost $1-2 billion a year (and growing) 9255 unique phishing sites reported in June 2006 Easier (and safer) to phish than rob a bank 2-3.5 million million

    5. Supporting Trust Decisions

    Goal: help people make better trust decisions Focus on anti-phishing Large multi-disciplinary team project at CMU Supported by NSF, ARO, CMU CyLab 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 Embedded training Anti-phishing game Computer side Email anti-phishing filter Automated testbed for anti-phishing toolbars Our anti-phishing toolbar Automate where possible, support where necessary

    7. What do users know about phishing?

    8. Interview Study

    Interviewed 40 Internet users, included 35 non-experts Mental models interviews included email role play and open ended questions Interviews recorded and coded 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. 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

    11. 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

    12. 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 password phish

    13. Naive Evaluation Strategies

    The most frequent strategies dont help much in identifying phish This email appears to be for me Its 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.

    14. Other Findings

    Web security pop-ups are confusing Yeah, like the certificate has expired. I dont actually know what that means. Dont know what encryption means Summary People generally not good at identifying scams they havent specifically seen before People dont use good strategies to protect themselves

    15. Can we train people not to fall for phishing?

    16. Web Site Training Study

    Laboratory study of 28 non-expert computer users Two conditions, both asked to evaluate 20 web sites Control group evaluated 10 web sites, took 15 minute break to read email or play solitaire, evaluated 10 more web sites Experimental group same as above, but spent 15 minute 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!

    17. How Do We Get People Trained?

    Most people dont proactively look for training materials on the web Many companies send security notice emails to their employees and/or customers But these tend to be ignored Too much to read People dont consider them relevant People think they already know how to protect themselves

    18. 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. CyLab Technical Report. CMU-CyLab-06-017, 2006. [to be presented at CHI 2007]

    19. Diagram Intervention

    20. Diagram Intervention

    Explains why they are seeing this message

    21. Diagram Intervention

    Explains how to identify a phishing scam

    22. Diagram Intervention

    Explains what a phishing scam is

    23. Diagram Intervention

    Explains simple things you can do to protect self

    24. Comic Strip Intervention

    25. Embedded Training Evaluation

    Lab study comparing our prototypes to standard security notices EBay, PayPal notices Diagram that explains phishing Comic strip that tells a story 10 participants in each condition (30 total) Roughly, go through 19 emails, 4 phishing attacks scattered throughout, 2 training emails too Emails are in context of working in an office

    26. Embedded Training Results

    Existing practice of security notices is ineffective Diagram intervention somewhat better Comic strip intervention worked best Statistically significant Pilot study showed interventions most effective when based on real brands

    27. Next Steps

    Iterate on intervention design Have already created newer designs, ready for testing Understand why comic strip worked better Story? Comic format? Preparing for larger scale deployment Include more people Evaluate retention over time Deploy outside lab conditions if possible Real world deployment and evaluation Need corporate partners to let us spoof their brand

    28. Anti-Phishing Phil

    A game to teach people not to fall for phish Embedded training focuses on email Game focuses on web browser, URLs Goals How to parse URLs Where to look for URLs Use search engines instead Available on our web site soon

    29. Anti-Phishing Phil

    30. Outline

    Human side Interviews to understand decision-making Embedded training Anti-phishing game Computer side Email anti-phishing filter Automated testbed for anti-phishing toolbars Our anti-phishing toolbar

    31. How accurate are todays anti-phishing toolbars?

    32. Some Users Rely on Toolbars

    Dozens of anti-phishing toolbars offered Built into security software suites Offered by ISPs Free downloads Built into latest version of popular web browsers

    34. Some Users Rely on Toolbars

    Dozens of anti-phishing toolbars offered Built into security software suites Offered by ISPs Free downloads Built into latest version of popular web browsers Previous studies demonstrated usability problems that need further work But how well do they detect phish?

    35. Testing the Toolbars

    April 2006: Manual evaluation of 5 toolbars Required lots of undergraduate labor over 2-week period Summer 2006: Created a semi-automated test bed September 2006: Automated evaluation of 5 toolbars Used APWG feed as source of phishing URLs November 2006: Automated evaluation of 10 toolbars Used as source of phishing URLs Evaluated 100 phish and 510 legit sites in just 2 days L. Cranor, S. Egelman, J. Hong and Y. Zhang. Phinding Phish: An Evaluation of Anti-Phishing Toolbars. CyLab Technical Report. CMU-CyLab-06-018, 2006. [to be presented at NDSS]

    36. Testbed for Anti-Phishing Toolbars

    Manual evaluation was tedious, slow, error-prone Created a testbed that could semi-automatically evaluate these toolbars Just give it a set of URLs to check (labeled as phish or not) Checks all the toolbars, aggregates statistics How to automate this for different toolbars? Different APIs (if any), different browsers Image-based approach, take screenshots of web browser and compare relevant portions to known states

    37. Testbed System Architecture

    38. Finding Fresh Phish for Test

    Need a source with lots of fresh phishing URLs Cant use toolbar black lists if we are testing their tools Sites get taken down within a few days, need phish less than one day old To observe how fast black lists get updated, the fresher the better Experimented with several sources APWG - high volume, but many duplicates and legitimate URLs included - lower volume but easier to extract phish Other phish archives - often low volume or not fresh enough Choice of feed impacts results

    39. November 2006 evaluation

    Tested 10 toolbars Microsoft Internet Explorer v7.0.5700.6 Netscape Navigator v8.1.2 EarthLink v3.3.44.0 eBay v McAfee SiteAdvisor v1.7.0.53 NetCraft v1.7.0 TrustWatch v3. SpoofGuard Cloudmark v1.0. Google Toolbar v2.1 (Firefox) Most use blacklists and simple heuristics SpoofGuard only one to rely solely on heuristics

    40. November 2006 Evaluation

    Test URLs 100 manually confirmed fresh phish from (reported within 6 hours) Did not use the fully confirmed ones 60 legitimate sites linked to by phishing messages 510 legitimate sites tested by 3Sharp in Sept 2006 report

    41. Results

    38% false positives 1% false positives

    42. Results

    Only toolbar >90% accuracy has high false positive rate Several catch 70-85% of phish with few false positives After 15 minutes of training, users seem to do as well Few improvements in catch rates seen over 24 hours Suggests most toolbars not taking advantage of available phish feeds to quickly update black lists Combination of heuristics and frequently updated black list (and white list?) seems to be most promising approach Plan to periodically repeat study every quarter Should only consider this a rough ordering Different sources of phishing URLs lead to different results

    43. Our Anti-Phishing Toolbar

    44. 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. How to generate signature? Found that TF-IDF was fairly effective Informal evaluation found five words was sufficient for most web pages

    45. 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

    46. Adapting TF-IDF for Anti-Phishing

    Rough algorithm Given a web page, calculate TF-IDF for each word on page Take five terms with highest TF-IDF weights Feed these terms into a search engine (Google) If domain name of current web page is in top N search results, consider it legitimate (N=30 worked well)

    48. Evaluation #1

    100 phishing URLs fro 100 legitimate URLs from 3Sharps study

    49. Discussion of Evaluation #1

    Very good results (97%), but false positives (10%) Added several heuristics to reduce false positives Many of these heuristics used by other toolbars Age of domain Known images Suspicious URLs (has @ or -) Suspicious links (see above) IP Address in URL Dots in URL (>= 5 dots) Page contains text entry field TF-IDF Used simple forward linear model to weight these

    50. Evaluation #2

    Compared to SpoofGuard and NetCraft SpoofGuard uses all heuristics NetCraft 1.7.0 uses heuristics (?) and extensive blacklist 100 phishing URLs from 100 legitimate URLs Sites often attacked (citibank, paypal) Top pages from Alexa (most popular sites) Random web pages from

    51. Results of Evaluation #2

    52. Discussion

    Pretty good results for TF-IDF approach 97% with 6% false positive, 89% with 1% false positive False positives due to JavaScript phishing sites Limitations Does not work well for non-English web sites (TF-IDF) System performance (querying Google each time) Attacks by criminals Using images instead of words Invisible text Circumventing TF-IDF and PageRank (hard in practice?)

    53. Summary

    Large multi-disciplinary team project at CMU looking at trust decisions, currently anti-phishing Human side Interviews to understand decision-making Embedded training Anti-phishing game Computer side Automated testbed for anti-phishing toolbars Our anti-phishing toolbar

    55. Embedded Training Results

    Email #16 was from CardMember Services with the subject "Your Online Statement Is Now Available" Email #17 was from with the subject "Reactivate your PayPal Account" Email #16 was from CardMember Services with the subject "Your Online Statement Is Now Available" Email #17 was from with the subject "Reactivate your PayPal Account"

    56. Email Anti-Phishing Filter

    Philosophy: automate where possible, support where necessary Goal: Create an email filter that detects phishing emails Well explored area for spam Can we do better for phishing?

    57. Email Anti-Phishing Filter

    Heuristics combined in SVM IP addresses in links ( 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 ( JavaScript SpamAssassin rating

    58. Email Anti-Phishing Filter Evaluation

    Ham corpora from SpamAssassin (2002 and 2003) 6950 good emails Phishingcorpus 860 phishing emails

    59. Email Anti-Phishing Filter Evaluation

    60. Is it legitimate