Guessing Models

Guessing Models is a computational framework in password security research that characterizes the probabilistic strategies attackers use to infer user-chosen passwords by modeling the relative likelihood of password guessability based on empirical or heuristic distributions.

155

Publications

12.7K

Citations

354

Authors

158

Institutions

Publications per year

2017–2026

37

Authors

354

  1. LF
    Lorrie Faith Cranor

    Carnegie Mellon University, United States

    6 publications · 1.1K citations

    6

    h-index

  2. SK
    Saranga Komanduri

    Carnegie Mellon University, United States

    6 publications · 1.1K citations

    6

    h-index

  3. LB
    Lujo Bauer

    Carnegie Mellon University, United States

    5 publications · 1.1K citations

    5

    h-index

  4. BU
    Blase Ur

    Carnegie Mellon University, United States

    5 publications · 695 citations

    5

    h-index

  5. RS
    Richard Shay

    Carnegie Mellon University, United States

    5 publications · 911 citations

    5

    h-index

1–5 of 354

Venues

  1. IEEE Transactions on Information Theory

    4 publications · 279 citations

    4

    h-index

  2. Journal of Experimental Psychology

    4 publications · 100 citations

    4

    h-index

  3. Educational and Psychological Measurement

    4 publications · 81 citations

    4

    h-index

  4. Applied Psychological Measurement

    4 publications · 177 citations

    4

    h-index

  5. USENIX Security Symposium

    Conference · 3 publications · 373 citations

    3

    h-index

1–5