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Interpretable Probabilistic Password Strength Meters via Deep Learning
v1v2v3v4 (latest)

Interpretable Probabilistic Password Strength Meters via Deep Learning

European Symposium on Research in Computer Security (ESORICS), 2020
15 April 2020
Dario Pasquini
G. Ateniese
M. Bernaschi
    AAML
ArXiv (abs)PDFHTML

Papers citing "Interpretable Probabilistic Password Strength Meters via Deep Learning"

5 / 5 papers shown
MoPE: A Mixture of Password Experts for Improving Password Guessing
MoPE: A Mixture of Password Experts for Improving Password Guessing
Mingjian Duan
Ming Xu
Shenghao Zhang
Jiaheng Zhang
Weili Han
MoE
117
0
0
20 Sep 2025
On the Account Security Risks Posed by Password Strength Meters
On the Account Security Risks Posed by Password Strength MetersACM Asia Conference on Computer and Communications Security (AsiaCCS), 2025
Ming Xu
Weili Han
Jitao Yu
Jing Liu
Xinsong Zhang
Yun Lin
Jin Song Dong
407
1
0
13 May 2025
PassGPT: Password Modeling and (Guided) Generation with Large Language
  Models
PassGPT: Password Modeling and (Guided) Generation with Large Language ModelsEuropean Symposium on Research in Computer Security (ESORICS), 2023
Javier Rando
Fernando Perez-Cruz
Briland Hitaj
GAN
184
26
0
02 Jun 2023
Universal Neural-Cracking-Machines: Self-Configurable Password Models
  from Auxiliary Data
Universal Neural-Cracking-Machines: Self-Configurable Password Models from Auxiliary DataIEEE Symposium on Security and Privacy (IEEE S&P), 2023
Dario Pasquini
G. Ateniese
Carmela Troncoso
FedML
473
13
0
18 Jan 2023
A Survey on Threat Situation Awareness Systems: Framework, Techniques,
  and Insights
A Survey on Threat Situation Awareness Systems: Framework, Techniques, and Insights
Hooman Alavizadeh
Julian Jang
S. Y. Enoch
Harith Al-Sahaf
I. Welch
S. Çamtepe
Dong Seong Kim
136
7
0
29 Oct 2021
1
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