skip to main content
10.1145/2484313.2484371acmconferencesArticle/Chapter ViewAbstractPublication Pagesasia-ccsConference Proceedingsconference-collections
research-article

TabShots: client-side detection of tabnabbing attacks

Published:08 May 2013Publication History

ABSTRACT

As the web grows larger and larger and as the browser becomes the vehicle-of-choice for delivering many applications of daily use, the security and privacy of web users is under constant attack. Phishing is as prevalent as ever, with anti-phishing communities reporting thousands of new phishing campaigns each month. In 2010, tabnabbing, a variation of phishing, was introduced. In a tabnabbing attack, an innocuous-looking page, opened in a browser tab, disguises itself as the login page of a popular web application, when the user's focus is on a different tab. The attack exploits the trust of users for already opened pages and the user habit of long-lived browser tabs.

To combat this recent attack, we propose TabShots. TabShots is a browser extension that helps browsers and users to remember what each tab looked like, before the user changed tabs. Our system compares the appearance of each tab and highlights the parts that were changed, allowing the user to distinguish between legitimate changes and malicious masquerading. Using an experimental evaluation on the most popular sites of the Internet, we show that TabShots has no impact on 78% of these sites, and very little on another 19%. Thereby, TabShots effectively protects users against tabnabbing attacks without affecting their browsing habits and without breaking legitimate popular sites.

References

  1. E. Adler. Tabnabbing without JavaScript . http://blog.eitanadler.com/2010/05/tabnabbing-without-javascript.html.Google ScholarGoogle Scholar
  2. N. Agarwal, S. Renfro, and A. Bejar. Yahoo!'s Sign-in Seal and current anti-phishing solutions.Google ScholarGoogle Scholar
  3. AOL acts to thwart hackers. http://simson.net/clips/1995/95.SJMN.AOL_Hackers.html.Google ScholarGoogle Scholar
  4. R. Dhamija and J. D. Tygar. The battle against phishing: Dynamic security skins. In Proceedings of the 2005 symposium on Usable privacy and security, SOUPS '05, pages 77--88, New York, NY, USA, 2005. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. R. Dhamija, J. D. Tygar, and M. Hearst. Why phishing works. In Proceedings of the SIGCHI conference on Human Factors in computing systems, CHI '06, pages 581--590, New York, NY, USA, 2006. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. P. Dubroy. How many tabs do people use? (Now with real data!). http://dubroy.com/blog/how-many-tabs-do-people-use-now-with-real-data/.Google ScholarGoogle Scholar
  7. S. Egelman, L. F. Cranor, and J. Hong. You've been warned: an empirical study of the effectiveness of web browser phishing warnings. In Proceedings of the SIGCHI conference on Human factors in computing systems, CHI '08, pages 1065--1074, New York, NY, USA, 2008. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. D. Jang, R. Jhala, S. Lerner, and H. Shacham. An empirical study of privacy-violating information flows in JavaScript Web applications. In Proceedings of CCS 2010, pages 270--83. ACM Press, Oct. 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. J. Leyden. Hackers break onto White House military network. http://www.theregister.co.uk/2012/10/01/white_house_hack/.Google ScholarGoogle Scholar
  10. L. Masinter. The "data" url scheme. 1998.Google ScholarGoogle Scholar
  11. N. Nikiforakis, A. Makridakis, E. Athanasopoulos, and E. P. Markatos. Alice, What Did You Do Last Time? Fighting Phishing Using Past Activity Tests. In Proceedings of the 3rd European Conference on Computer Network Defense (EC2ND), volume 30, pages 107--117, 2009.Google ScholarGoogle ScholarCross RefCross Ref
  12. NoScript - JavaScript/Java/Flash blocker for a safer Firefox experience! http://noscript.net/.Google ScholarGoogle Scholar
  13. PhishTank | Join the fight against phishing. http://www.phishtank.com.Google ScholarGoogle Scholar
  14. A. Raskin. Tabnabbing: A new type of phishing attack. http://www.azarask.in/blog/post/a-new-type-of-phishing-attack/.Google ScholarGoogle Scholar
  15. Safe Browsing API -- Google Developers. https://developers.google.com/safe-browsing/.Google ScholarGoogle Scholar
  16. D. R. Sandler and D. S. Wallach. "input type="password" must die!" In Proceedings of W2SP 2008: Web 2.0 Security & Privacy 2008, Oakland, CA, May 2008.Google ScholarGoogle Scholar
  17. SiteKey Security from Bank of America. https://www.bankofamerica.com/privacy/online-mobile-banking-privacy/sitekey.go.Google ScholarGoogle Scholar
  18. StatCounter. Screen resolution alert for web developers.Google ScholarGoogle Scholar
  19. R. K. Suri, D. S. Tomar, and D. R. Sahu. An approach to perceive tabnabbing attack. In Internation Journal of Scientific & Technology Research, volume 1, 2012.Google ScholarGoogle Scholar
  20. S. Unlu and K. Bicakci. Notabnab: Protection against the "tabnabbing attack". In eCrime Researchers Summit (eCrime), 2010, pages 1--5, oct. 2010.Google ScholarGoogle Scholar
  21. Z. Weinberg, E. Y. Chen, P. R. Jayaraman, and C. Jackson. I still know what you visited last summer: Leaking browsing history via user interaction and side channel attacks. In Proceedings of the 2011 IEEE Symposium on Security and Privacy, SP '11, pages 147--161, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. L. Wenyin, G. Huang, L. Xiaoyue, Z. Min, and X. Deng. Detection of phishing webpages based on visual similarity. In Special interest tracks and posters of the 14th international conference on World Wide Web, WWW '05, pages 1060--1061, New York, NY, USA, 2005. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. M. Wu, R. C. Miller, and S. L. Garfinkel. Do security toolbars actually prevent phishing attacks? In Proceedings of the SIGCHI conference on Human Factors in computing systems, CHI '06, pages 601--610, New York, NY, USA, 2006. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Y. Zhang, J. I. Hong, and L. F. Cranor. Cantina: a content-based approach to detecting phishing web sites. In Proceedings of the 16th international conference on World Wide Web, WWW '07, pages 639--648, New York, NY, USA, 2007. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. TabShots: client-side detection of tabnabbing attacks

            Recommendations

            Comments

            Login options

            Check if you have access through your login credentials or your institution to get full access on this article.

            Sign in
            • Published in

              cover image ACM Conferences
              ASIA CCS '13: Proceedings of the 8th ACM SIGSAC symposium on Information, computer and communications security
              May 2013
              574 pages
              ISBN:9781450317672
              DOI:10.1145/2484313

              Copyright © 2013 ACM

              Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

              Publisher

              Association for Computing Machinery

              New York, NY, United States

              Publication History

              • Published: 8 May 2013

              Permissions

              Request permissions about this article.

              Request Permissions

              Check for updates

              Qualifiers

              • research-article

              Acceptance Rates

              ASIA CCS '13 Paper Acceptance Rate35of216submissions,16%Overall Acceptance Rate418of2,322submissions,18%

            PDF Format

            View or Download as a PDF file.

            PDF

            eReader

            View online with eReader.

            eReader