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Forcing Johnny to Login Safely

Long-Term User Study of Forcing and Training Login Mechanisms
  • Amir Herzberg
  • Ronen Margulies
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6879)

Abstract

We present the results of the first long-term user study of site-based login mechanisms which force and train users to login safely. We found that interactive site-identifying images received 70% detection rates, which is significantly better than passive indicators’ results [15,8,12]. We also found that login bookmarks, when used together with ‘non-working’ links, doubled the prevention rates of reaching spoofed login pages in the first place. Combining these mechanism provides effective prevention and detection of phishing attacks, and when several images are displayed in the login page, the best detection rates (82%) and overall resistance rates (93%) are achieved. We also introduce the notion of negative training functions, which train users not to take dangerous actions by experiencing failure when taking them.

Keywords

Detection Rate User Study Resistance Rate Home Page Force Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Amir Herzberg
    • 1
  • Ronen Margulies
    • 1
  1. 1.Dept. of Computer ScienceBar Ilan UniversityIsrael

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