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Forcing Johnny to Login Safely

Long-Term User Study of Forcing and Training Login Mechanisms

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Part of the book series:Lecture Notes in Computer Science ((LNSC,volume 6879))

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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 issignificantly 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 provideseffective 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 ofnegative training functions, which train users not to take dangerous actions by experiencing failure when taking them.

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Author information

Authors and Affiliations

  1. Dept. of Computer Science, Bar Ilan University, Israel

    Amir Herzberg & Ronen Margulies

Authors
  1. Amir Herzberg

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  2. Ronen Margulies

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Editor information

Editors and Affiliations

  1. MSIS Department and CIMIC, Rutgers University, Washington Park 1, 07102, Newark, NJ, USA

    Vijay Atluri

  2. K.U. Leuven ESAT-COSIC, Kasteelpark Arenberg 10, 3001, Leuven-Heverlee, Belgium

    Claudia Diaz

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© 2011 Springer-Verlag Berlin Heidelberg

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Herzberg, A., Margulies, R. (2011). Forcing Johnny to Login Safely. In: Atluri, V., Diaz, C. (eds) Computer Security – ESORICS 2011. ESORICS 2011. Lecture Notes in Computer Science, vol 6879. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23822-2_25

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