Fast, Lean, and Accurate: Modeling Password Guessability Using Neural Networks

338 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Defeating Image Obfuscation with Deep Learning (2016, background): “Melicher et al. [29] used deep neural networks to analyze user passwords and construct a password prediction model.”
Automated Crowdturfing Attacks and Defenses in Online Review Systems (2017, background): “Work in this category mainly includes proposals to improve network intrusion detection [8, 25], malware classification [62, 66, 81], and passwordbased authentication systems [39].”
Evaluation and Design of Robust Neural Network Defenses (2018, background): “Distillation can potentially increase accuracy on the test set as well as the rate at which the smaller model learns to predict the hard labels [45, 82].”
Targeted Online Password Guessing: An Underestimated Threat (2016, background): “, 10(10) guesses or beyond [24, 28]) could have been better placed.”
The AI-Based Cyber Threat Landscape (2020, background): “The characterlevel language modeling with NNs learns patterns from the past and predicts the next character in a sequence by generating new passwords in a specific style [36, 53, 78].”
PassGAN: A Deep Learning Approach for Password Guessing (2017, methodology): “• For FLA, we set up the code from [47] according to the instruction provided at [30].”