Cited by 94Spam and Phishing DetectionUser Authentication and Security SystemsInformation and Cyber Security

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Does domain highlighting help people identify phishing sites?

Open access
Lin, Eric, Greenberg, Saul, Trotter, Eileah, Ma, David, Aycock, John·2011·Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Abstract

Phishers are fraudsters that mimic legitimate websites to steal user's credenfitial information and exploit that information for identity theft and other criminal activities. Various anti-phishing techniques attempt to mitigate such attacks. Domain highlighting is one such approach recently incorporated by several popular web browsers. The idea is simple: the domain name of an address is highlighted in the address bar, so that users can inspect it to determine a web site's legitimacy. Our research asks a basic question: how well does domain highlighting work? To answer this, we showed 22 participants 16 web pages typical of those targeted for phishing attacks, where participants had to determine the page's legitimacy. In the first round, they judged the page's legitimacy by whatever means they chose. In the second round, they were directed specifically to look at the address bar. We found that participants fell into 3 types in terms of how they determined the legitimacy of a web page; while domain highlighting was somewhat effective for one user type, it was much less effective for others. We conclude that domain highlighting, while providing some benefit, cannot be relied upon as the sole method to prevent phishing attacks.

Overview

Integrity signals

Registry

Crossref · 10.1145/1978942.1979244

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Awareness systems built into browser security systems, including security toolbars, domain highlighting for suspected phishing and HTTPS/SSL/TLS connection indicators, were not effective because users would not understand or pay attention to warnings.

    “Their results showed that none of these methods were effective because users would not understand or pay attention to warnings.”

Citations

Cites

  • What makes Web sites credible?: a report on a large quantitative study

    Fogg, B. J., Marshall, J., Laraki, O., Osipovich, A., Varma, C., Fang, N., Paul, J., Rangnekar, A., Shon, J., Swani, P., et al. (2001) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/365024.365037 · Cited by 685 · cited 4×

    “Content amount, the kind of information, and its layout within a page were also important factors contributing to Type A and AB interpretations of a site’s trustworthiness (see also [4]).”
    BackgroundResultInfluential

Flagged first, then by citations

Research works printed but not linked: 17

  1. APWG ( 2009 ) Phishing activity trends report , 4th quarter . http://www.antiphishing.org. APWG (2009) Phishing activity trends report, 4th quarter. http://www.antiphishing.org.
  2. 10.1145/1124772.1124861 · DOI · Paper page
  3. 10.1145/1357054.1357219 · DOI
  4. 10.1145/503124.503156 · DOI
  5. 10.1145/1290958.1290968 · DOI · Paper page
  6. Jakobsson , M. ( 2007 ) The human factor in phishing . Privacy and Security of Consumer Information '07 . Jakobsson, M. (2007) The human factor in phishing. Privacy and Security of Consumer Information '07.
  7. 10.1109/MSP.2008.52 · DOI
  8. 10.5555/1785594.1785639 · DOI
  9. 10.1145/1754393.1754396 · DOI
  10. 1 Litan , A. ( 2009 ) The war on phishing is far from over. Garnter , April . http://www.gartner.com. 1Litan, A. (2009) The war on phishing is far from over. Garnter, April. http://www.gartner.com.
  11. PhishTank ( 2010 ) Statistics about phishing activity and PhishTank usage. //phishtank.com/stats/2010/08/ , Aug. PhishTank (2010) Statistics about phishing activity and PhishTank usage. //phishtank.com/stats/2010/08/, Aug.
  12. 10.1109/SP.2007.35 · DOI · Paper page
  13. Strauss , A. and Corbin , J . Basics of Qualitative Research . 2 nd Edition. Sage Publications , 1999 . Strauss, A. and Corbin, J. Basics of Qualitative Research. 2nd Edition. Sage Publications, 1999.
  14. Vaughan , C. ( 2008 ) Address Bar Improvements in Internet Explorer 8 Beta 1 , IEBlog, 11 March. Vaughan, C. (2008) Address Bar Improvements in Internet Explorer 8 Beta 1, IEBlog, 11 March.
  15. 10.5555/1089508.1089532 · DOI
  16. 10.5555/1251421.1251435 · DOI
  17. 10.1145/1124772.1124863 · DOI

Cited by

Sorted by citations

Metadata

Authors
Lin, E., Greenberg, S., Trotter, E., Ma, D., & Aycock, J.
Affiliations
University of Calgary (CA)
Venue
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Publisher
ACM
Published
2011-05-07
Pages
2075-2084
OpenAlex
W1995554356
Cited by
94
References
1
Citations by year
2026: 1 · 2025: 4 · 2024: 10 · 2023: 9 · 2022: 5 · 2021: 5 · 2020: 6 · 2019: 5 · 2018: 5 · 2017: 4 · 2016: 8 · 2015: 7 · 2014: 8 · 2013: 5 · 2012: 9
FWCI
6.01
Type
conference paper
Language
en
Open access
Locations
CHI Conference on Human Factors in Computing Systems (publishedVersion)
Funders
Natural Sciences and Engineering Research Council of Canada
Topics
  • Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
  • Physical Sciences › Computer Science › Information Systems › User Authentication and Security Systems
  • Physical Sciences › Computer Science › Information Systems › Information and Cyber Security
Keywords
phishing, user behavior, browser security, user types, identity theft

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review